Banking Technology & Allied Topics

11/24/21, 3:21 PM BaaS: Why you should know about Banking as a Service To succeed in this arena, BaaS solutions need to be easy to use and integrate seamlessly, and above all else, they need to be totally safe – protecting both the primary bank and the users' data and adhering to all compliance criteria. Now is the time for BaaS to shine, and only the best providers will carry through the digital revolution that is already taking wing in the banking world. https://www.finextra.com/blogposting/21290/baas-why-you-should-know-about-banking-as-a-service 2/2

Global Banking & Securities AI-powered decision making for the bank of the future Banks are already strengthening customer relationships and lowering costs by using artificial intelligence to guide customer engagement. Success requires that capability stacks include the right decisioning elements. by Akshat Agarwal, Charu Singhal, and Renny Thomas © Getty Images March 2021

The ongoing transition to digital channels creates engaging with them continuously and intelligently an opportunity for banks to serve more customers, to strengthen each relationship across diverse expand market share, and increase revenue at lower products and services. cost. Crucially, banks that pursue this opportunity also can access the bigger, richer data sets required — Lower operating costs. Banks can lower costs to fuel advanced-analytics (AA) and machine- by automating as fully as possible document learning (ML) decision engines. Deployed at scale, processing, review, and decision making, these decision-making capabilities powered particularly in acquisition and servicing. by artificial intelligence (AI) can give the bank a decisive competitive edge by generating significant — Lower credit risk. To lower credit risks, banks incremental value for customers, partners, and can adopt more sophisticated screening of the bank. Banks that aim to compete in global prospective customers and early detection of and regional markets increasingly influenced behaviors that signal higher risk of default and by digital ecosystems will need a well-rounded fraud. AI-and-analytics capability stack comprising four main layers: reimagined engagement, AI-powered As banks think about how to design and build a highly decision making, core technology and data flexible and fully automated decisioning layer of infrastructure, and leading-edge operating model. the AI-bank capability stack, they can benefit from organizing their efforts around four interdependent The layers of the AI-bank capability stack are elements: (1) leveraging AA/ML models for interdependent and must work in unison to deliver automated, personalized decisions across the value, as discussed in the first article in our series customer life cycle; (2) building and deploying AA/ on the AI bank of the future.¹ In our second article, ML models at scale; (3) augmenting AA/ML models we examined how AI-first banks are reimagining with what we call “edge” capabilities³ to reduce customer engagement to provide superior costs, streamline customer journeys, and enhance experiences across diverse bank platforms and the overall experience; and (4) building an enterprise- partner ecosystems.² In the current article, we focus wide digital-marketing engine to translate insights on the AA/ML decisioning capabilities required to generated in the decision-making layer into a set of understand and respond to customers’ fast-evolving coordinated messages delivered through the bank’s needs with precision, speed, and efficiency. Banks engagement layer. that leverage machine-learning models to determine in (near) real time the best way to engage with each Automated, personalized decisions customer have potential to increase value in four across the customer life cycle ways: If financial institutions begin by prioritizing the use — Stronger customer acquisition. Banks gain an cases where AA/ML models can add the most value, edge by creating superior customer experiences they can automate more than 20 decisions in diverse with end-to-end automation and using advanced customer journeys. Within the lending life cycle, for analytics to craft highly personalized messages example, leading banks are relying increasingly at each step of the customer-acquisition journey. on AI and analytics capabilities to add value in five main areas: customer acquisition, credit decisioning, — Higher customer lifetime value. Banks can monitoring and collections, deepening relationships, increase the lifetime value of customers by and smart servicing (Exhibit 1, next page). 1Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas, “AI-bank of the future: Can banks meet the AI challenge?” September 2020, McKinsey.com. 2Violet Chung, Malcolm Gomes, Sailee Rane, Shwaitang Singh, and Renny Thomas, “Reimagining customer engagement for the AI bank of the future,” October 2020, McKinsey.com. 3Edge capabilities refer to next-generation AI-powered technologies that can provide financial institutions an edge over the competition. Natural language processing (NLP), voice-script analysis, virtual agents, computer vision, facial recognition, blockchain, robotics, and behavioral analytics are some of the technologies that we classify as “edge capabilities.” These capabilities can be instrumental in improving customer experience and loyalty across multiple dimensions (engagement channels, intelligent advisory, faster processing), personalizing offers with highly accurate underwriting, and improving operational efficiency across the value chain (from customer servicing to monitoring, record management, and more.). 2 AI-powered decision making for the bank of the future

Exhibit 1 Banks should prioritize using advanced analytics (AA) and machine learning (ML) in decisions across the customer life cycle. Customer life cycle Customer Credit decisioning Monitoring and Deepening Smart servicing acquisition Credit qualification collections relationships Hyperpersonalized Limit assessment Intelligent offers (eg, Servicing personas Pricing optimization Early-warning next product to buy) offers Fraud prevention signals Dynamic customer Churn reduction routing (channel, agent) Customer retargeting Probability of default/self-cure Channel propensity Real-time recom- Propensity-to-buy mendation engine scoring VAR-based customer Fatigue rule engine AI-enabled agent segmentation review and training Channel mapping Agent–customer mapping Monthly customer- Credit-approval Average days past Deposit/AUM Net promoter score, acquisition run rate turnaround time, % due, nonperforming attrition rate, cost of servicing products 6 per of applications assets approved customer Key metrics 1VAR is value at risk. AUM is assets under management. Customer acquisition profile of each customer, including financial position The use of advanced analytics is crucial to the design and provisional credit scoring. Based on real-time of journeys for new customers, who may follow a analysis of a customer’s digital footprint, banks can variety of paths to open a new card account, apply for display a landing page tailored to their profile and a mortgage, or research new investment opportunities. preferences. Some may head directly to the bank’s website, mobile app, branch kiosk, or ATM. Others may arrive indirectly These tools can also help banks tailor follow-up through a partner’s website or by clicking on an ad. messages and offers for each customer. Replacing Many banks already use analytical tools to understand much of the mass messaging that used to flow to each new customer’s path to the bank, so they get an thousands or tens of thousands of customers in a accurate view of the customer’s context and direction subsegment, advanced analytics can help prioritize of movement, which enables them to deliver highly customers for continued engagement. The bank can personalized offers directly on the landing page. select customers according to their responsiveness Following local regulations governing the use and to prior messaging—also known as their “propensity protection of customer data, banks can understand to buy”—and can identify the best channel for individuals’ needs more precisely by analyzing how each type of message, according to the time of customers enter the website (search, keywords, day. And for the “last mile” of the customer journey, advertisements), their browsing history (cookies, AI-first institutions are using advanced analytics site history), and social-media data to form an initial to generate intelligent, highly relevant messages AI-powered decision making for the bank of the future 3

and provide smart servicing via assisted channels to usage data, and more. This decisioning process create a superior experience, which has been shown to is automated from end to end, so it can be contribute to higher rates of conversion.⁴ completed nearly instantaneously, enabling the bank to predict the likelihood of default for Credit decisioning individuals in a vast and potentially profitable Setting themselves apart from traditional banks, segment of unbanked and underbanked whose customers may wait anywhere from a day to a consumers and SMEs. As banks build and refine week for credit approval, AI-first banks have designed their qualification model, they can proceed streamlined lending journeys, using extensive gradually, testing and improving the model—for automation and near-real-time analysis of customer example, by using auto-approvals for customers data to generate prompt credit decisions for retailers, up to a certain threshold with significantly lower small and medium-size enterprises (SMEs), and default risk and using manual verification to corporate clients. They do this by sifting through a review those estimated to have a higher default variety of structured and unstructured data collected risk and then gradually shifting more cases to from conventional sources (such as bank transaction automated decisioning. history, credit reports, and tax returns) and new ones (including location data, telecom usage data, utility — Limit assessment. Leading banks are also using bills, and more). Access to these nontraditional data AA/ML models to automate the process for sources depends on open banking and other data determining the maximum amount a customer sharing guidelines as well the availability of officially may borrow. These loan-approval systems, by approved APIs and data aggregators in the local leveraging optical character recognition (OCR) market. Further, while accessing and leveraging to extract data from conventional data sources personal data of customers, banks must secure data such as bank statements, tax returns, and and protect customer privacy in accordance with utilities invoices, can quickly assess a customer’s local regulations (e.g., the General Data Protection disposable income and capacity to make regular Regulation in the EU and the California Consumer loan payments. The proliferation of digital Privacy Act in the US). interactions also provides vast and diverse data sets to fuel complex machine-learning models. By using powerful AA/ML models to analyze these By building data sets that draw upon both broad and diverse data sets in near real time, banks conventional and new sources of data, banks can qualify new customers for credit services, can generate a highly accurate prediction of determine loan limits and pricing, and reduce the risk a customer’s capacity to pay. Just a few data of fraud. sources that may be available for analysis (with the customer’s permission) are emails, SMS, and — Credit qualification. Lenders seeking to determine e-commerce expenditures. if a customer qualifies for a particular type of loan have for many years used rule-based or — Pricing. Banks generally have offered highly logistic-regression models to analyze credit standardized rates on loans, with sales bureau reports. This approach, which relies on representatives and relationship managers a narrow set of criteria, fails to serve a large having some discretion to adjust rates within segment of consumers and SMEs lacking a formal certain thresholds. However, fierce competition credit history, so these potential customers turn on loan pricing, particularly for borrowers with a to nonbank sources of credit. In recent years, strong risk score, places banks using traditional however, leading banks and fintech lenders approaches at a considerable disadvantage have developed complex models for analyzing against AI-and-analytics leaders. Fortified with structured and unstructured data, examining highly accurate machine-learning models for risk hundreds of data points collected from social scoring and loan pricing, AI-first banks have been media, browsing history, telecommunications able to offer competitive rates while keeping their 4Erik Lindecrantz, Madeleine Tjon Pian Gi, and Stefano Zerbi, “Personalizing the customer experience: Driving differentiation in retail,” April 2020, McKinsey.com. 4 AI-powered decision making for the bank of the future

risk costs low. Some are also using their decisioning (e.g., fraud by sales agents), customer fraud, and capabilities to quantify the customer’s propensity payment fraud (including money laundering and to buy according to the customer’s use of different sanctions violations). Banks should continuously types of financial products. Some even leverage update their fraud detection and prevention natural-language processing (NLP) to analyze models, as we discuss later with regard to edge unstructured transcripts of interactions with sales capabilities. Ping An, for example, uses an image- and service representatives and, in some cases, analytics model to recognize 54 involuntary micro- collections personnel. By basing the offered rate expressions that occur before the brain has a on both creditworthiness and propensity to buy, the chance to control facial movements.⁵ bank can optimize the balance of total asset volume, risk, and interest income within a lending portfolio. AI-driven credit decisioning can build the business while lowering costs. Sharper identification of risky — Fraud management. As competition for credit customers enables banks to increase approval relationships becomes concentrated in digital rates without increasing credit risk. What is more, by channels, the automated processing of loan automating as much of the lending journey as possible, applications and use of AA/ML models to expedite banks can reduce the costs of support functions and credit approval and disbursement of funds not strengthen each customer’s experience with faster only positions the bank to acquire new customers loan approval and disbursement of funds, fewer and increase market share, but also opens new requests for documentation, and credit offers precisely opportunities for fraud. The costliest instances of tailored to meet customer needs. Exhibit 2 illustrates fraud typically fall into one of five categories: identity how AI-enabled decisioning capabilities underpin a theft, employee fraud, third-party or partner fraud customer’s onboarding journey. 5 “Chinese banks start scanning borrowers’ facial movements,” Financial Times, October 28, 2018, ft.com. Exhibit 2 The combination of AI and analytics enhances the onboarding journey for each new customer. Name: Joy Family: Married, no children Age: 32 years Profile attribute: Avid traveler Occupation: Working professional Propensity-to-buy model to identify AA-enabled real- Joy whom to retarget time credit underwriting, limit completes Joy’s Channel propensity Joy assessment, and online know-your- Hyperpersonalized cross-sell and landing page to identify right receives a call to customer form, upsell offers shows her per- outreach channel assist in journey; pricing provides details for Loan disbursed to Joy; curated sonalized offers: caller also employment verifi- catalog of offers personal loan for informs cation, and sets up ahead of Joy’s travel sent by travel, 5% off on Joy about custom an online pay- email travel insurance travel services Joy completes a ment mandate Joy receives a WhatsApp streamlined reminder for 3-click journey Analytics-backed personal loan Analytics-enabled to see the AI capabilities to hyperpersonalized for travel at zero customer–caller offer terms and conduct relevant mapping with fraud checks (eg, offers based on processing conditions charges customer specific cues facial recognition microsegment provided to caller with know-your- customer docs) AI-powered decision making for the bank of the future 5

Monitoring and collections variety of external data partnerships for location Once a bank has employed AA/ML models to data and transaction history can help the bank automate loan underwriting and pricing, it can also understand both the customer’s position and the deploy AI and advanced analytics to reduce the most effective approach, or contact strategy, for burden of nonperforming loans. Increasingly, banks averting default (Exhibit 3). are engaging with clients proactively to help them keep up with payments and work more closely Contact strategy. To determine an appropriate with clients who encounter difficulties. By drawing contact strategy for customers at risk of default, upon internal and external data sources to build a banks can segment accounts according to value 360-degree view of a customer’s financial position, at risk (VAR), which is the loan balance times banks can recognize early-warning signals that a the probability of default. This allows banks to borrower’s risk profile may have changed and that focus high-touch interactions on borrowers that the risk of default should be reassessed. account for the highest VAR; banks can then use low-cost channels like telephoning and texting for Beyond conventional data sources like repayment borrowers posing less risk. Banks have used this data and credit bureau reports, banks can digitize approach to reduce both the cost of collections and leverage other interaction data from campaigns, and the volume of loans to be resolved through field visits, and collection agents’ comments to restructuring, sale, or write-off.⁶ draw insights for collections strategy. Further, a 6Ignacio Crespo and Arvind Govindarajan, “The analytics-enabled collections model,” April 2018, McKinsey.com. Exhibit 3 Advanced analytics and machine learning can classify customers into microsegments for targeted interventions. Customer type True low-risk Absentminded Dialer-based True high-touch Unable to cure Targeted Use least Ignore or use Match agents to Focus on customers Offer debt- intervention experienced agents interactive voice customers; send live able to pay and at restructuring provided with set message (segment prompts to agents high risk of not settlements early for scripts will probably to modify scripts paying those truly self-cure) underwater Impact Onscreen prompts 10% of time saved, Matching and Added focus Significant increase guide agent–client allowing for conversation based reassignment of prompts can addresses higher in restructuring and on probability of agents to more breaking promises difficult customers increase sense of probability of default settlements and specific connection and rates in this segment increases chance of campaigns likelihood of paying collecting at least part of debt Source: Ignacio Crespo and Arvind Govindarajan, “The analytics-enabled collections model,” McKinsey on Payments, August 2018, McKinsey.com 6 AI-powered decision making for the bank of the future

Treatment strategy. If contact strategies through a prediction algorithm to estimate the ideal various channels are inadequate to help the product-per-customer (PPC) ratio for each customer resume timely payment, banks must user, based on individual needs. If analysis of pursue stronger measures, according to the a customer’s needs produces an anticipated customer’s ability and willingness to pay. Customers product usage ratio of eight but the customer with high willingness but limited ability to pay in the uses only two products, the relationship short term may require restructuring of the loan manager receives a prompt to reach out to the through partial-payment plans or loan extensions. customer and cross-sell or up-sell relevant In cases where the customer exhibits both low ecosystem products.⁷ willingness and limited ability to pay, banks should focus on early settlement and asset recovery. Servicing and engagement Advanced analytics, enabled by unstructured AI-powered decisioning can enable banks to internal data sources such as call transcripts create a smart, highly personalized servicing from collections contact centers and external experience based on customer microsegments, data sources such as spending behavior on other thereby enabling different channels to deliver digital channels, can improve the accuracy of superior service and a compelling experience determinations of ability and willingness to pay. with interactions that are fast, simple, and intuitive.⁸ Banks can support their relationship Deepening relationships managers with timely customer insights and Strong customer engagement is the foundation tailor-made offers for each customer. They can for maximizing customer value, and leaders also significantly improve agents’ productivity are using advanced analytics to identify less with streamlined preapproved products crafted engaged customers at risk of attrition and to craft to meet each customer’s distinct needs. Models messages for timely nudges. As with any customer that analyze voice and speech characteristics communication in a smart omnichannel service can match agents with customers based on environment, each personalized offer is delivered behavioral and psychological mapping. Similarly, through the right channel according to the time of transcript analysis can enable prediction of day. Rich internal data for existing customers can customer distress and suggest resolution to enable financial institutions to create a finely tuned the agent. outreach strategy for each individual customer, guided by risk considerations. Deployment of AA/ML models at scale Deeper relationships are predicated on a bank’s precise understanding of a customer’s unique Leveraging AI to automate decision making in needs and expectations. A bank can craft offers to near real time is a complex and costly endeavor. meet emerging needs and deliver them at the right If banks are to earn the required return on their time and through the right channel. By doing so, the technology investments, they must begin with a bank demonstrates that it understands customers’ strategy and road map to capture maximal scale current position and aspirations and can help them benefits in the design, building, and deployment get from the former to the latter. For example, by of AA/ML models. analyzing browsing history and spending patterns, a bank might recognize a consumer’s need for credit As banks embark on this journey, leaders must to finance an upcoming purchase of a household encourage all stakeholders to break out of siloed appliance. Analysis of internal data on product mindsets and think broadly about how models usage can also reveal areas where the bank can can be designed for uses in diverse contexts make its offering more relevant to a customer’s across the enterprise. AI-first organizations have current needs. Ping An, for example, has developed succeeded by organizing the effort around four 7“Ping An Bank: Change everything,” Asiamoney, September 26, 2019, asiamoney.com. 7 8Violet Chung, Malcolm Gomes, Sailee Rane, Shwaitang Singh, Renny Thomas, “Reimagining customer engagement for the AI bank of the future,” McKinsey.com, October 2020. AI-powered decision making for the bank of the future

main elements: First, they prioritize the analytics environment where cross-functional teams can use cases with the biggest impact on customer experiment with different approaches to achieving experience and the most value for the bank. the value-generating goals of a particular use case, Second, they ensure that the data architecture, moving from minimal viable product to scalable data pipelines, application programming interfaces solution in a matter of weeks. Building AA/ML models (APIs), and other essential components are available at scale and deploying them across the enterprise for building and deploying models at scale through depend on matching the right talent and skills with standardized, repeatable processes.⁹ Third, they each of the roles required for a successful analytics establish a semiautonomous lab for experimentation lab and factory (Exhibit 4).¹0 and prototype development and set up a factory for industrial-scale production of the solution. Fourth, The lab combines talent from business, analytics, they assemble the right mix of talent for agile, cross- technology, operations, and more. There are two functional teams and empower them to maximize main technical roles. One is the data scientist, value in close alignment with enterprise strategy. who is responsible for identifying the analytics techniques required to meet the business goal and Several leading banks have established semi- for programming advanced analytics algorithms. autonomous labs offering a test-and-learn The other is the data engineer, who scopes the data 9Tara Balakrishnan, Michael Chui, Bryce Hall, and Nicolaus Henke, “The state of AI in 2020,” November 2020, McKinsey.com. ¹0Nayur Khan, Brian McCarthy, and Adi Pradhan, “Executive’s guide to developing AI at scale,” October 2020, McKinsey.com. Exhibit 4 Diverse roles are necessary for building and deploying AA/ML models at scale. Lab environment Factory environment Product owner Data scientist Product owner ML engineer Leads the squad; Frames the First point of Optimizes ML typically a business problem contact for models for business owner and develops external stake- performance and who provides voice advanced analytics holders; defines scalability; deploys of the customer algorithms the solution criteria the models into production Data engineer Translator DevOps engineer Scopes the data Interfaces Develops CI/CD Infrastructure available; builds between business pipelines to architect data architecture and technical automate parts of Designs and data pipelines stakeholders the software- infrastructure deployment pipeline components for the analytics use case Full-stack Designer Delivery manager developer Focuses on Responsible for all Develops software interaction aspects of delivery components for between end of the analytics the back and front users and the solution to meet ends of AI solutions analytics solution squad goal output 1Continuous integration and continuous deployment. 8 AI-powered decision making for the bank of the future

available, identifies major sources of data to be Some edge technologies already afford banks consolidated for analytics, develops data pipelines to the opportunity to strengthen existing models simplify and automate data movement, and sets up with expanded data sets. For example, many data architecture for storage and layering. In addition, interactions with customers—via telephone, the role of translator is crucial to ensure consistent mobile app, website, or increasingly, in a communication and smooth collaboration between branch—begin with a conversational interface business leaders and analytics specialists. to establish the purpose of the interaction and collect the information required to resolve the On factory teams, one of the primary technical query or transfer it to an agent. A routing engine roles is the DevOps engineer, who is responsible can use voice and image analysis to understand for developing continuous integration (CI) and a customer’s current sentiment and match the continuous deployment (CD) pipelines for deploying customer with a suitable agent. The models software. In addition, the full-stack developer is underpinning virtual assistants and chatbots responsible for developing software components employ NLP and voice-script analysis to increase for other layers of the stack. The machine-learning their predictive accuracy as they churn through engineer prepares models for deployment at scale, vast unstructured data generated during and the infrastructure architect ensures that the customer-service and sales interactions. analytics solution is compatible with the architecture of the core tech and data layer of the capability stack. While each customer-service journey presents an opportunity to deepen the relationship with the The lab-and-factory setup requires flexible and help of next-product-to-buy recommendations, scalable technologies to handle the changing banks should constantly seek to improve their requirements of analytics engines. It is also important recommendation engines and messaging to give analytics teams access to the centralized campaigns. Feedback loops, for example, can data lake, and these teams must be able to draw help marketing teams and frontline officers upon raw data from diverse sources to generate data gauge the effectiveness of an offer by analyzing sets to be used in building models. The technology customers’ ongoing browsing and transaction supporting the solution must be modular to allow the activity within the bank’s digital ecosystem and transfer of developed solutions to factory production beyond (Exhibit 5, next page). using DevOps tools. Finally, it is crucial to embed performance management and risk controls within As edge capabilities become more powerful, models to avoid adverse impacts on operations. leaders are developing new, increasingly complex analytics solutions to create a superior Once the lab has developed a model, the factory experience and introduce distinctive innovations. takes over, running 24/7 to put the model into Use of computer vision and voice-to-script production and deploying it at scale in diverse use conversion can speed the completion of forms— cases across the enterprise. for instance, enabling a customer to respond orally to questions and upload documents Augmented AA/ML models with edge from which relevant data can be extracted capabilities automatically using optical character recognition (OCR). Facial and sentiment analysis during an The rapid improvement of AI-powered technologies in-person consultation or videoconference can spurs competition on speed, cost, experience, and support frontline representatives with messages intelligent propositions. To maintain its market and offers finely tuned to the customer’s needs leadership, an AI-first institution must develop and aspirations. models capable of meeting the processing requirements of edge capabilities, including natural- Several banks use voice recognition to verify language processing (NLP), computer vision, facial customer identity for certain low-value, high- recognition, and more. volume transactions. Some are using facial AI-powered decision making for the bank of the future 9

Exhibit 5 Edge capabilities enhance customer-service journeys. Digital self-cure Voice-recognition- AI-enabled Voice-analytics- AI-enabled Feedback loop via service-to-sales engagement channels for enabled IVR, customer profiling, and NLP-enabled channels engine customers (eg, frontline bots customer–agent customer- WhatsApp, mobile handling 30–50% matching sentiment analysis app, website) of queries Customer may If query is not Customer Contact-center Customers are Postcall feedback prompted for any and automated seek immediate resolved through connected to the agents supported specific offers follow-up occur via resolution of automated appropriate agent by live feedback preapproved for digital channels queries through channels, (chat or call) based and prompts to them digital self-service customer may on type of query sustain superior channels contact bank via and customer customer chat or request profiling experience call with live agent 1Interactive voice response. Natural-language-processing-enabled. recognition to authenticate customers’ identity as soon of customer behaviors, banks must go the last mile to as they enter a branch, approach an ATM, or open the ensure that these analytical insights have an impact banking app on a mobile device. As noted earlier, facial on customer behavior, such as purchasing a product, analysis is also useful in identifying making a loan payment, or exploring new service potential fraud. offers. In other words, an organization must establish a mechanism to “translate” analytical outcomes Leading banks are using blockchain to create smart into compelling messages to communicate to the contracts, secure trade documents and automate the customer at the right time, through the preferred release of funds upon delivery of goods, and establish channel—be it email, SMS, mobile app, website, shared utilities to reduce the burden of know-your- branch staff, or a relationship manager—according customer (KYC) and anti-money-laundering (AML) to the time of day. compliance for banks and customers. Edge capabilities deployed as part of an enterprise strategy to enhance This last mile from decisioning to messaging is the the AI bank’s value proposition have the potential not domain of the digital marketing engine. Seamlessly only to improve credit underwriting and fraud integrated with applications across the full AI-and- prevention but also to reduce the costs of document analytics capability stack with the help of APIs, handling and regulatory compliance. from data infrastructure to engagement channels, this engine supports the nearly instantaneous Enterprise-wide digital marketing engine processing of raw data to produce tailored messages communicated via engagement channels. Exhibit 6, While the automated decisions generated by AA/ML on the next page, illustrates the position of the digital models provide highly accurate, real-time predictions 10 AI-powered decision making for the bank of the future

Exhibit 6 The digital marketing engine requires full stack capabilities. Engagement layer Email SMS Mobile app Website Branch Contact ... center Decisioning layer Martech stack Design and activation Channel analytics/ Customer Data mgmt. Content Ad tech Campaign feedback loop relationship platform mgmt. server mgmt. management (DMP) platform platform Testing platform (CRM) AA/ML models: Decisioning and personalization engine Edge NLP Voice Virtual Computer Facial Blockchain Robotics Behavioral capabilities analysis agents vision recognition analytics Data lake layer Curated data lake Raw data lake Other use cases Customer 360 Personalization store Additional derived elements for personalization Comprehensive set of data sources: internal structured data (eg, applications, product holding, payment behavior), unstructured data (eg, call logs), CRM, external data (eg, telco, clickstream data), campaign performance data 1Advanced-analytics and machine-learning. Natural-language processing. marketing engine (or martech stack) within the and modified; (2) the ad tech server, which decisioning layer of the AI-bank capability stack. automates advertisements based on data analysis; and (3) the campaign management The digital marketing engine comprises platforms platform, which supports the creation and and applications fulfilling four main functions: management of marketing campaigns, which data management, design and activation, are conducted automatically according to the measurement and testing, and channel analytics. microsegmentation generated by the data The data management platform, which forms part management platform. of the core tech and data infrastructure layer of the AI-bank capability stack, supplies the data Just as the AI-and-analytics capability used to create and manage target customer stack entails fundamental changes in the segments. The design and activation function organization’s talent, culture, and ways of has three elements: (1) the content management working, the success of digital marketing platform, where messages, offers, advertisements, capabilities depends on an agile operating and other interventions are created, managed, model. This model consists of autonomous AI-powered decision making for the bank of the future 11

cross-functional teams (or pods) drawing upon (CBA) leverages its mobile app to test messages the talent of different parts of the enterprise, such and learn within hours what works and what must as business units, marketing, analytics, channels, be changed. This cadence enables rapid scaling of operations, and technology. Each pod should also campaigns to similar customer segments.¹¹ include representatives from partner organizations crucial to the digital marketing effort—for example, In measurement of campaigns’ impact, scientific user-interface and user-experience designers, rigor is crucial. To allow for precise measurement who lay out the campaign’s look and feel and its of the incremental value of the campaign, each flow, and copywriters, who finalize the language target segment should include a control group of of any intervention. The members of each pod customers excluded from the campaign. The tools collaborate on developing, managing, and improving and capabilities for evaluating the effectiveness of engagement campaigns, and each member is customer-engagement campaigns help employees accountable for campaigns’ impact according to across the organization understand how they can clearly defined key performance indicators (KPIs). enhance their impact on individual customers and add value to an AI-oriented culture. To achieve the desired outcome, an AI-first bank launching daily personalized communications to The rapid improvement of AI-powered technologies millions of customers must build tools for continual spurs competition on speed, cost, experience, and testing and learning. The measurement and testing intelligent propositions. To remain competitive, platform flags potential aspects of content or banks must engage customers with highly distribution to improve, thereby enabling teams to personalized and timely content to build loyalty. evaluate in real time the effectiveness of campaigns. Personalized offers with tailored communication delivered at the right time through the customer’s Another source of continual feedback is channel preferred channel can help banks maximize the analytics, which includes tools and dashboards lifetime value of each customer relationship and for real-time tracking of engagement across each reinforce the organization’s market leadership. target segment. Every day, each pod leverages the To achieve these benefits, banks must build channel analytics and measurement and testing AI-powered decisioning capabilities fueled by a rich platforms to closely track various indicators, mixture of internal and external data and augmented including delivery rates, email open rates, click- by edge technologies. The core technology and through rates by channel for customers seeking data infrastructure required to collect and curate more information (the first call to action), conversion increasingly diverse and voluminous data sets is the rates, and more. These diagnostics help members topic of the next article in our series on the AI-bank of the pod experiment with potential enhancements capability stack. to messages, advertisements, and campaign design. As an example, Commonwealth Bank of Australia Akshat Agarwal is an associate partner in McKinsey’s Bangalore office. Charu Singhal a consultant and Renny Thomas is a senior partner, both in the Mumbai office. Copyright © 2021 McKinsey & Company. All rights reserved. 11Paul McIntyre, “CommBank’s analytics chief on how its AI-powered ‘Customer Engagement Engine’ is changing everything,” Mi3, September 21, 2020, mi-3.com.au. 12 AI-powered decision making for the bank of the future

Global Banking & Securities AI-bank of the future: Can banks meet the AI challenge? Artificial intelligence technologies are increasingly integral to world we live in, and banks need to deploy these technologies at scale to remain relevant. Success requires a holistic transformation spanning multiple layers of the organization. by Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas © Getty Images September 2020

In 2016, AlphaGo, a machine, defeated 18-time 3. What obstacles prevent banks from deploying world champion Lee Sedol at the game of AI capabilities at scale? Go, a complex board game requiring intuition, imagination, and strategic thinking—abilities 4. How can banks transform to become AI-first? long considered distinctly human. Since then, artificial intelligence (AI) technologies have 1. Why must banks become AI-first? advanced even further,¹ and their transformative impact is increasingly evident across Over several decades, banks have continually industries. AI-powered machines are tailoring adapted the latest technology innovations to recommendations of digital content to individual redefine how customers interact with them. Banks tastes and preferences, designing clothing introduced ATMs in the 1960s and electronic, lines for fashion retailers, and even beginning to card-based payments in the ’70s. The 2000s saw surpass experienced doctors in detecting signs of broad adoption of 24/7 online banking, followed cancer. For global banking, McKinsey estimates by the spread of mobile-based “banking on the go” that AI technologies could potentially deliver up to in the 2010s. $1 trillion of additional value each year.² Few would disagree that we’re now in the Many banks, however, have struggled to move AI-powered digital age, facilitated by falling costs from experimentation around select use cases to for data storage and processing, increasing scaling AI technologies across the organization. access and connectivity for all, and rapid Reasons include the lack of a clear strategy for AI, advances in AI technologies. These technologies an inflexible and investment-starved technology can lead to higher automation and, when deployed core, fragmented data assets, and outmoded after controlling for risks, can often improve upon operating models that hamper collaboration human decision making in terms of both speed between business and technology teams. What and accuracy. The potential for value creation is more, several trends in digital engagement is one of the largest across industries, as AI can have accelerated during the COVID-19 pandemic, potentially unlock $1 trillion of incremental value and big-tech companies are looking to enter for banks, annually (Exhibit 1). financial services as the next adjacency. To compete successfully and thrive, incumbent Across more than 25 use cases,³ AI technologies banks must become “AI-first” institutions, can help boost revenues through increased adopting AI technologies as the foundation for personalization of services to customers (and new value propositions and distinctive customer employees); lower costs through efficiencies experiences. generated by higher automation, reduced errors rates, and better resource utilization; and uncover In this article, we propose answers to four new and previously unrealized opportunities questions that can help leaders articulate a clear based on an improved ability to process and vision and develop a road map for becoming an generate insights from vast troves of data. AI-first bank: More broadly, disruptive AI technologies can 1. Why must banks become AI-first? dramatically improve banks’ ability to achieve four key outcomes: higher profits, at-scale 2. What might the AI-bank of the future look like? personalization, distinctive omnichannel 1 AI can be defined as the ability of a machine to perform cognitive functions associated with human minds (e.g., perceiving, reasoning, learning, and problem solving). It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and autonomous vehicles. See “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com. 2 “The executive’s AI playbook,” McKinsey.com. 3 For an interactive view, visit: www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-executives-ai- playbook?page=industries/banking/ 2

Exhibit 1 Potential annual value of AI and analytics for global banking could reach as high as $1 trillion. Total potential annual value, $ billion Traditional AI 1,022.4 (15.4% of sales) Advanced AI and analytics 660.9 361.5 % of value driven by advanced AI, by function 100 50 Risk: 372.9 Finance and IT: 8.0 Other operations: $2.4 B 288.6 84.3 0.0 8.0 0.0 2.4 Marketing and sales: 624.8 363.8 261.1 HR: 14.2 8.6 5.7 0 Source: \"The executive's AI playbook,\" McKinsey.com. (See \"Banking,\" under \"Value & Assess.\") experiences, and rapid innovation cycles. Banks As consumers increase their use of digital that fail to make AI central to their core strategy banking services, they grow to expect more, and operations—what we refer to as becoming particularly when compared to the standards “AI-first”—will risk being overtaken by competition they are accustomed to from leading consumer- and deserted by their customers. This risk is internet companies. Meanwhile, these digital further accentuated by four current trends: experience leaders continuously raise the bar on personalization, to the point where they — Rising customer expectations as adoption sometimes anticipate customer needs before of digital banking increases. In the first few the customer is aware of them, and offer highly- months of the COVID-19 pandemic, use of tailored services at the right time, through the online and mobile banking channels across right channel. countries has increased by an estimated 20 to 50 percent and is expected to continue at — Leading financial institutions’ use of advanced this higher level once the pandemic subsides. AI technologies is steadily increasing. Nearly Across diverse global markets, between 15 and 60 percent of financial-services sector 45 percent of consumers expect to cut back respondents in McKinsey’s Global AI Survey on branch visits following the end of the crisis.⁴ report⁵ that their companies have embedded 4 John Euart, Nuno Ferreira, Jonathan Gordon, Ajay Gupta, Atakan Hilal, Olivia White, “A global view of financial life during COVID-19—an update,” July 2020, McKinsey.com. 5 Arif Cam, Michael Chui, Bryce Hall, “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com. AI-bank of the future: Can banks meet the AI challenge? 3

at least one AI capability. The most commonly but also to book a cab, order food, schedule used AI technologies are: robotic process a massage, play games, send money to a automation (36 percent) for structured contact, and access a personal line of credit. operational tasks; virtual assistants or Similarly, across countries, nonbanking conversational interfaces (32 percent ) for businesses and “super apps” are embedding customer service divisions; and machine financial services and products in their learning techniques (25 percent) to detect journeys, delivering compelling experiences fraud and support underwriting and risk for customers, and disrupting traditional management. While for many financial services methods for discovering banking products and firms, the use of AI is episodic and focused on services. As a result, banks will need to rethink specific use cases, an increasing number of how they participate in digital ecosystems, banking leaders are taking a comprehensive and use AI to harness the full power of data approach to deploying advanced AI, and available from these new sources. embedding it across the full lifecycle, from the front- to the back-office (Exhibit 2). — Technology giants are entering financial services as the next adjacency to their — Digital ecosystems are disintermediating core business models. Globally, leading traditional financial services. By enabling technology giants have built extraordinary access to a diverse set of services through market advantages: a large and engaged a common access point, digital ecosystems customer network; troves of data, enabling a have transformed the way consumers discover, robust and increasingly precise understanding evaluate, and purchase goods and services. of individual customers; natural strengths For example, WeChat users in China can use in developing and scaling innovative the same app not only to exchange messages, technologies (including AI); and access to Exhibit 2 Banks are expanding their use of AI technologies to improve customer eexxppeerriieenncceessaannddbabcakc-kof-foicffie cperopcreoscseess.ses. Front office Back office Smile-to-pay facial scanning Micro-expression analysis Biometrics (voice, video, Machine learning to detect print) to authenticate and fraud patterns, to initiate transaction with virtual loan officers authorize cybersecurity attacks Conversational bots for Humanoid robots in branches Machine vision and natural- Real-time transaction basic servicing requests to serve customers language processing to scan analysis for risk monitoring and process documents 4 AI-bank of the future: Can banks meet the AI challenge?

low-cost capital. In the past, tech giants have digital era, the AI-first bank will offer propositions aggressively entered into adjacent businesses and experiences that are intelligent (that in search of new revenue streams and to is, recommending actions, anticipating and keep customers engaged with a fresh stream automating key decisions or tasks), personalized of offerings. Big-tech players have already (that is, relevant and timely, and based on a gained a foothold in financial services in select detailed understanding of customers’ past domains (especially in payments and, in some behavior and context), and truly omnichannel cases, lending and insurance), and they may (seamlessly spanning the physical and online soon look to press their advantages to deepen contexts across multiple devices, and delivering their presence and build greater scale. a consistent experience) and that blend banking capabilities with relevant products and services 2. What might the AI-bank of the beyond banking. Exhibit 3 illustrates how such a future look like? bank could engage a retail customer throughout the day. Exhibit 4 shows an example of the banking To meet customers’ rising expectations and experience of a small-business owner or the beat competitive threats in the AI-powered treasurer of a medium-size enterprise. Exhibit 3 How AI transforms banking for a retail customer. Name: Anya Age: 28 years Occupation: Working professional Anya receives App offers money- integrated portfolio management and view and a set of Anya uses smile- savings solutions, actions with the Seamless to-pay to Analytics- prioritizes card Aggregated potential to integration with nonbanking apps initiate payment backed payments overview of daily augment returns personalized offers activities Bank app Facial recognition Anya gets 2% off Personalized Anya receives Savings and recognizes Anya's for frictionless end-of-day investment recom- spending patterns payment on health money-management solutions mendations and suggests insurance overview of her coffee at nearby premiums based activities, with cafes on her gym augmented reality, activity and and reminders to sleep habits pay bills Intelligent Personalized Omnichannel Banking and beyond banking AI-bank of the future: Can banks meet the AI challenge? 5

Exhibit 4 How AI transforms banking for a small- or medium-size-enterprise customer. Name: Dany Age: 36 years Occupation: Treasurer of a small manufacturing unit Dany answers short questionnaire; app scans his facial An AI-powered virtual adviser movements Dany is assisted resolves queries Firm is credited in sourcing and Dany seeks professional advice with funds after selecting the Beyond- on a lending offer banking support Customized application Seamless right vendors services lending solutions approval inventory and receiv- and partners ables management Bank is integrated Micro-expression App suggests SME platform to Dany gets prefilled Serviced by an AI- with client analysis to review loan items to reorder, source suppliers business tax documents to powered virtual applications gives visual reports and buyers review and management adviser on receivables approve; files with systems management a single click Dany gets loan Dany receives offer based on customized company projected solutions for cash flows invoice discounting, factoring, etc. Intelligent Personalized Omnichannel Banking and beyond banking Internally, the AI-first institution will be optimized The AI-first bank of the future will also enjoy for operational efficiency through extreme the speed and agility that today characterize automation of manual tasks (a “zero-ops” mindset) digital-native companies. It will innovate and the replacement or augmentation of human rapidly, launching new features in days or decisions by advanced diagnostic engines in weeks instead of months. It will collaborate diverse areas of bank operations. These gains extensively with partners to deliver new in operational performance will flow from broad value propositions integrated seamlessly application of traditional and leading-edge AI across journeys, technology platforms, and technologies, such as machine learning and data sets. facial recognition, to analyze large and complex reserves of customer data in (near) real time. 6 AI-bank of the future: Can banks meet the AI challenge?

3. What obstacles prevent banks from cases. Without a centralized data backbone, it is deploying AI capabilities at scale? practically impossible to analyze the relevant data and generate an intelligent recommendation or Incumbent banks face two sets of objectives, offer at the right moment. If data constitute the which on first glance appear to be at odds. On bank’s fundamental raw material, the data must be the one hand, banks need to achieve the speed, governed and made available securely in a manner agility, and flexibility innate to a fintech. On the that enables analysis of data from internal and other, they must continue managing the scale, external sources at scale for millions of customers, security standards, and regulatory requirements in (near) real time, at the “point of decision” across of a traditional financial-services enterprise. the organization. Lastly, for various analytics and advanced-AI models to scale, organizations need Despite billions of dollars spent on change- a robust set of tools and standardized processes the-bank technology initiatives each year, few to build, test, deploy, and monitor models, in a banks have succeeded in diffusing and scaling repeatable and “industrial” way. AI technologies throughout the organization. Among the obstacles hampering banks’ efforts, Banks’ traditional operating models further the most common is the lack of a clear strategy impede their efforts to meet the need for for AI.⁶ Two additional challenges for many continuous innovation. Most traditional banks banks are, first, a weak core technology and data are organized around distinct business lines, backbone and, second, an outmoded operating with centralized technology and analytics model and talent strategy. teams structured as cost centers. Business owners define goals unilaterally, and alignment Built for stability, banks’ core technology with the enterprise’s technology and analytics systems have performed well, particularly in strategy (where it exists) is often weak or supporting traditional payments and lending inadequate. Siloed working teams and “waterfall” operations. However, banks must resolve implementation processes invariably lead several weaknesses inherent to legacy systems to delays, cost overruns, and suboptimal before they can deploy AI technologies at scale performance. Additionally, organizations lack (Exhibit 5). First and foremost, these systems a test-and-learn mindset and robust feedback often lack the capacity and flexibility required loops that promote rapid experimentation and to support the variable computing requirements, iterative improvement. Often unsatisfied with the data-processing needs, and real-time analysis performance of past projects and experiments, that closed-loop AI applications require.⁷ Core business executives tend to rely on third-party systems are also difficult to change, and their technology providers for critical functionalities, maintenance requires significant resources. starving capabilities and talent that should ideally What is more, many banks’ data reserves are be developed in-house to ensure competitive fragmented across multiple silos (separate differentiation. business and technology teams), and analytics efforts are focused narrowly on stand-alone use 6 Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com. 7 “Closed loop” refers to the fact that the models’ intelligence is applied to incoming data in near real time, which in turn refines the content presented to the user in near real time. AI-bank of the future: Can banks meet the AI challenge? 7

Exhibit 5 Investments in core tech are critical to meet increasing demands for ssccaalalabbiliiltiyty, ,flflexeixbiibliitliyt,ya,nadndspsepede.ed. Cloud Data API Challenges How cloud computing can help Core/legacy systems can’t scale sufficiently Enables higher scalability, resilience of services and (eg, 150+ transactions/second) platforms through virtualization of infrastructure Significant time, effort, and team sizes Reduces IT overhead, enables automation of several required to maintain infrastructure infrastructure-management tasks, and allows development teams to “self-serve” Long time required to provision environments for development and testing (eg, 40+ days in Enables faster time to market; dramatically reduces time by some cases) providing managed services (e., setting up new environments in minutes vs days) Challenges How best-in-class data management can help High error rates; poor refresh rates; lack of Ensures high degree of accuracy and single source of truth golden source of truth in a cost-effective manner Hard to access in a timely fashion for various Enables timely and role-appropriate access for various use use cases cases (eg, regulatory, business intelligence at scale, advanced analytics and machine learning, exploratory) Data trapped in silos across multiple units and hard to integrate with external sources Enables a 360-degree view across the organization to enable generation of deeper insights by decision-making algorithms and models Challenges How APIs can help Longer time to market, limited reusability of Promote reusability and accelerate development by enabling code and software across internal teams access to granular services (internal and external) Hard to partner or collaborate with external Reduce complexity and enable faster collaboration with partners; long time to integrate external partners Suboptimal user experience—hard to stitch Enhance customer experience by enabling timely access to data and services across multiple functional data and services across different teams; faster time to market siloes for an integrated proposition due to limited coordination, cross-team testing 1Application programming interface. 8 AI-bank of the future: Can banks meet the AI challenge?

4. How can banks transform to First, banks will need to move beyond highly become AI-first? standardized products to create integrated propositions that target “jobs to be done.”⁸ This To overcome the challenges that limit requires embedding personalization decisions organization-wide deployment of AI (what to offer, when to offer, which channel technologies, banks must take a holistic to offer) in the core customer journeys and approach. To become AI-first, banks must invest designing value propositions that go beyond the in transforming capabilities across all four layers core banking product and include intelligence of the integrated capability stack (Exhibit 6): the that automates decisions and activities on engagement layer, the AI-powered decisioning behalf of the customer. Further, banks should layer, the core technology and data layer, and the strive to integrate relevant non-banking operating model. products and services that, together with the core banking product, comprehensively address As we will explain, when these interdependent the customer end need. An illustration of the layers work in unison, they enable a bank to “jobs-to-be-done” approach can be seen in the provide customers with distinctive omnichannel way fintech Tally helps customers grapple with experiences, support at-scale personalization, the challenge of managing multiple credit cards. and drive the rapid innovation cycles critical The fintech’s customers can solve several pain to remaining competitive in today’s world. points—including decisions about which card to Each layer has a unique role to play—under- pay first (tailored to the forecast of their monthly investment in a single layer creates a weak link income and expenses), when to pay, and how that can cripple the entire enterprise. much to pay (minimum balance versus retiring principal)—a complex set of tasks that are often The following paragraphs explore some of the not done well by customers themselves. changes banks will need to undertake in each layer of this capability stack. The second necessary shift is to embed customer journeys seamlessly in partner Layer 1: Reimagining the customer ecosystems and platforms, so that banks engagement layer engage customers at the point of end use and Increasingly, customers expect their bank to be in the process take advantage of partners’ present in their end-use journeys, know their data and channel platform to increase higher context and needs no matter where they interact engagement and usage. ICICI Bank in India with the bank, and to enable a frictionless embedded basic banking services on WhatsApp experience. Numerous banking activities (a popular messaging platform in India) and (e.g., payments, certain types of lending) are scaled up to one million users within three becoming invisible, as journeys often begin and months of launch.⁹ In a world where consumers end on interfaces beyond the bank’s proprietary and businesses rely increasingly on digital platforms. For the bank to be ubiquitous in ecosystems, banks should decide on the customers’ lives, solving latent and emerging posture they would like to adopt across multiple needs while delivering intuitive omnichannel ecosystems—that is, to build, orchestrate, or experiences, banks will need to reimagine how partner—and adapt the capabilities of their they engage with customers and undertake engagement layer accordingly. several key shifts. 8 Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, 9 September 2016, hbr.org. 9 “ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com. AI-bank of the future: Can banks meet the AI challenge?

Exhibit 6 To become an AI-first institution, a bank must streamline its capability stack for value creation. AI bank of the future Profitability Personalization Omnichannel Speed and at scale experience innovation Reimagined Intelligent products, Within-bank channels and Beyond-bank channels engagement tools, experiences for customers and journeys (eg, web, apps, and journeys (eg, Smart service and operations employees mobile, smart devices, ecosystems, partners, 4 1 branches, Internet of Things) distributors) 23 5 Digital marketing AI-powered 6 Customer Credit Monitoring Retention Servicing decision acquisition decision and and cross- and making Advanced making analytics collections selling, engagement upselling 7 Natural- Voice- Virtual Facial Block- Behav- AI capabilities language script agents, Computer recog- chain process- analysis bots vision nition Robotics ioral analytics ing A. Tech-forward strategy (in-house build of differential capabilities vs buying offerings; in-house talent plan) Core 8 B. Data C. Modern D. Intelligent E. Hollow- F. Cyber- technology manage- API archi- infrastructure ing the security and data Core technology ment for tecture (AI operations core (core and and data AI world command, moderniza- control hybrid cloud tion) tiers setup, etc) 9 A. Autonomous business + tech teams Operating Platform operating B. Agile way C. Remote D. Modern talent E. Culture and model model of working collaboration strategy (hiring, capabilities reskilling) 10 Value capture 10 AI-bank of the future: Can banks meet the AI challenge?

Third, banks will need to redesign overall and stronger risk management (e.g., earlier customer experiences and specific journeys for detection of likelihood of default and omnichannel interaction. This involves allowing fraudulent activities). customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart To establish a robust AI-powered decision devices) seamlessly within a single journey layer, banks will need to shift from attempting and retaining and continuously updating the to develop specific use cases and point latest context of interaction. Leading consumer solutions to an enterprise-wide road map for internet companies with offline-to-online deploying advanced-analytics (AA)/machine- business models have reshaped customer learning (ML) models across entire business expectations on this dimension. Some banks domains. As an illustration, in the domain of are pushing ahead in the design of omnichannel unsecured consumer lending alone, more journeys, but most will need to catch up. than 20 decisions across the life cycle can be automated.¹¹ To enable at-scale development Reimagining the engagement layer of the of decision models, banks need to make the AI bank will require a clear strategy on how development process repeatable and thus to engage customers through channels capable of delivering solutions effectively and owned by non-bank partners. Banks will on-time. In addition to strong collaboration need to adopt a design-thinking lens as they between business teams and analytics build experiences within and beyond the talent, this requires robust tools for model bank’s platform, engineering engagement development, efficient processes (e.g., for interfaces for flexibility to enable tailoring and re-using code across projects), and diffusion personalization for customers, reengineering of knowledge (e.g., repositories) across teams. back-end processes, and ensuring that data- Beyond the at-scale development of decision capture funnels (e.g., clickstream) are granularly models across domains, the road map should embedded in the bank’s engagement layer. All also include plans to embed AI in business- of this aims to provide a granular understanding as-usual process. Often underestimated, of journeys and enable continuous this effort requires rewiring the business improvement.10 processes in which these AA/AI models will be embedded; making AI decisioning “explainable” Layer 2: Building the AI-powered decision- to end-users; and a change-management plan making layer that addresses employee mindset shifts and Delivering personalized messages and skills gaps. To foster continuous improvement decisions to millions of users and thousands beyond the first deployment, banks also of employees, in (near) real time across the full need to establish infrastructure (e.g., data spectrum of engagement channels, will require measurement) and processes (e.g., periodic the bank to develop an at-scale AI-powered reviews of performance, risk management of AI decision-making layer. Across domains within models) for feedback loops to flourish. the bank, AI techniques can either fully replace or augment human judgment to produce Additionally, banks will need to augment significantly better outcomes (e.g., higher homegrown AI models, with fast-evolving accuracy and speed), enhanced experience capabilities (e.g., natural-language processing, for customers (e.g., more personalized computer-vision techniques, AI agents interaction and offerings), actionable insights and bots, augmented or virtual reality) in for employees (e.g., which customer to contact their core business processes. Many of first with next-best-action recommendations), these leading-edge capabilities have the 10Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com. 11 11 Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “Ten lessons for building a winning retail and small-business digital lending franchise,” November 2019, McKinsey.com. AI-bank of the future: Can banks meet the AI challenge?

potential to bring a paradigm shift in customer technology backbone, starved of the investments experience and/or operational efficiency. While needed for modernization, can dramatically many banks may lack both the talent and the reduce the effectiveness of the decision-making requisite investment appetite to develop these and engagement layers. technologies themselves, they need at minimum to be able to procure and integrate these The core-technology-and-data layer has six key emerging capabilities from specialist providers elements (Exhibit 7): at rapid speed through an architecture enabled by an application programming interface (API), — Tech-forward strategy. Banks should have promote continuous experimentation with these a unified technology strategy that is tightly technologies in sandbox environments to test and aligned to business strategy and outlines refine applications and evaluate potential risks, strategic choices on which elements, skill and subsequently decide which technologies to sets, and talent the bank will keep in-house deploy at scale. and those it will source through partnerships or vendor relationships. In addition, the To deliver these decisions and capabilities and to tech strategy needs to articulate how each engage customers across the full life cycle, from component of the target architecture will both acquisition to upsell and cross-sell to retention support the bank’s vision to be an AI-first and win-back, banks will need to establish institution and interact with each layer of the enterprise-wide digital marketing machinery. This capability stack. machinery is critical for translating decisions and insights generated in the decision-making layer — Data management for the AI-enabled world. into a set of coordinated interventions delivered The bank’s data management must ensure through the bank’s engagement layer. This data liquidity—that is, the ability to access, machinery has several critical elements, which ingest, and manipulate the data that serve as include: the foundation for all insights and decisions generated in the decision-making layer. — Data-ingestion pipelines that capture a range Data liquidity increases with the removal of of data from multiple sources both within the functional silos and allows multiple divisions bank (e.g., clickstream data from apps) and to operate off the same data, with increased beyond (e.g., third-party partnerships with coordination. The data value chain begins with telco providers) seamless sourcing of data from all relevant internal systems and external platforms. This — Data platforms that aggregate, develop, and includes ingesting data into a lake, cleaning maintain a 360-degree view of customers and and labeling the data required for diverse use enable AA/ML models to run and execute in cases (e.g., regulatory reporting, business near real time intelligence at scale, AA/ML diagnostics), segregating incoming data (from both existing — Campaign platforms that track past actions and prospective customers) to be made and coordinate forward-looking interventions available for immediate analysis from data to across the range of channels in the be cleaned and labeled for future analysis. engagement layer Furthermore, as banks design and build their centralized data-management infrastructure, Layer 3: Strengthening the core technology and they should develop additional controls and data infrastructure monitoring tools to ensure data security, Deploying AI capabilities across the organization privacy, and regulatory compliance—for requires a scalable, resilient, and adaptable set example, timely and role-appropriate access of core-technology components. A weak core- across the organization for various use cases. 12 AI-bank of the future: Can banks meet the AI challenge?

Exhibit 7 The core-technology-and-data layer accommodates increasing use of the cloud and reduction of legacy technology. Capabilities Our perspective Tech-forward strategy Build differentiating capabilities in-house by augmenting the internal skill base; carefully weigh options to buy, build, or compose modular architecture through best-of-breed solutions Data management for AI world Upgrade data management and underlying architecture to support machine-learning use cases at scale by leveraging cloud, streaming data, and real-time analytics Modern API architecture Leverage modern cloud-native tooling to enable a scalable API platform supporting complex orchestrations while creating experience-enhancing integrations across the ecosystem Intelligent infrastructure Implement infrastructure as code across on-premises and cloud environments; increase platform resiliency by adopting AIOps to support deep diagnostics, auto- recoverability, and auto-scale Hollowing the core Distribute transaction processing across the enterprise stack; selectively identify components that can be externalized to drive broader reuse, standardization, and efficiency Implement robust cybersecurity in the hybrid infrastructure; secure data and Cybersecurity and control tiers applications through zero-trust design principles and centralized command-and- control centers 1Application programming interface. — Modern API architecture. APIs are the — Intelligent infrastructure. As companies connective tissue enabling controlled access in diverse industries increase the share of to services, products, and data, both within workload handled on public and private the bank and beyond. Within the bank, APIs cloud infrastructure, there is ample evidence reduce the need for silos, increase reusability that cloud-based platforms allow for the of technology assets, and promote flexibility higher scalability and resilience crucial to an in the technology architecture. Beyond the AI-first strategy.13 Additionally, cloud-based bank, APIs accelerate the ability to partner infrastructure reduces costs for IT maintenance externally, unlock new business opportunities, and enables self-serve models for development and enhance customer experiences. While teams, which enable rapid innovation cycles by APIs can unlock significant value, it is critical to providing managed services (e.g., setting up new start by defining where they are to be used and environments in minutes instead of days). establish centralized governance to support their development and curation.¹² ¹² Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “Ten lessons for building a winning retail and small-business digital lending franchise,” November 2019, McKinsey.com. ¹³ Arul Elumalai and Roger Roberts, “Unlocking business acceleration in a hybrid cloud world,” August 2019, McKinsey.com. AI-bank of the future: Can banks meet the AI challenge? 13

Layer 4: Transitioning to the platform operating The journey to becoming an AI-first bank entails model transforming capabilities across all four layers The AI-first bank of the future will need a new of the capability stack. Ignoring challenges or operating model for the organization, so it can underinvesting in any layer will ripple through all, achieve the requisite agility and speed and resulting in a sub-optimal stack that is incapable unleash value across the other layers. While of delivering enterprise goals. most banks are transitioning their technology platforms and assets to become more modular A practical way to get started is to evaluate and flexible, working teams within the bank how the bank’s strategic goals (e.g., growth, continue to operate in functional silos under profitability, customer engagement, innovation) suboptimal collaboration models and often lack can be materially enabled by the range of AI alignment of goals and priorities. technologies—and dovetailing AI goals with the strategic goals of the bank. Once this alignment The platform operating model envisions cross- is in place, bank leaders should conduct a functional business-and-technology teams comprehensive diagnostic of the bank’s starting organized as a series of platforms within the bank. position across the four layers, to identify areas Each platform team controls their own assets that need key shifts, additional investments (e.g., technology solutions, data, infrastructure), and new talent. They can then translate these budgets, key performance indicators, and insights into a transformation roadmap that spans talent. In return, the team delivers a family of business, technology, and analytics teams. products or services either to end customers of the bank or to other platforms within the bank. Equally important is the design of an execution In the target state, the bank could end up with approach that is tailored to the organization. To three archetypes of platform teams. Business ensure sustainability of change, we recommend platforms are customer- or partner-facing teams a two-track approach that balances short-term dedicated to achieving business outcomes in projects that deliver business value every quarter areas such as consumer lending, corporate with an iterative build of long-term institutional lending, and transaction banking. Enterprise capabilities. Furthermore, depending on their platforms deliver specialized capabilities and/ market position, size, and aspirations, banks need or shared services to establish standardization not build all capabilities themselves. They might throughout the organization in areas such as elect to keep differentiating core capabilities collections, payment utilities, human resources, in-house and acquire non-differentiating and finance. And enabling platforms enable the capabilities from technology vendors and enterprise and business platforms to deliver partners, including AI specialists. cross-cutting technical functionalities such as cybersecurity and cloud architecture. By integrating business and technology in For many banks, ensuring adoption of AI jointly owned platforms run by cross-functional technologies across the enterprise is no longer teams, banks can break up organizational silos, a choice, but a strategic imperative. Envisioning increasing agility and speed and improving the and building the bank’s capabilities holistically alignment of goals and priorities across the across the four layers will be critical to success. enterprise. Suparna Biswas is a partner, Shwaitang Singh is an associate partner, and Renny Thomas is a senior partner, all in McKinsey’s Mumbai office. Brant Carson is a partner in the Sydney office, and Violet Chung is a partner in the Hong Kong office. The authors would like to thank Milan Mitra, Anushi Shah, Arihant Kothari, and Yihong Wu for their contributions to this article. Copyright © 2020 McKinsey & Company. All rights reserved. 14 AI-bank of the future: Can banks meet the AI challenge?

Global Banking Practice The 2021 McKinsey Global Payments Report October 2021 © Getty Images

Global Banking Practice The 2021 McKinsey Global Payments Report October 2021

Contents 2 Foreword 4 Global payments 2021: Transformation amid turbulent undercurrents 14 CBDC and stablecoins: Early coexistence on an uncertain road 22 How transaction banks are reinventing treasury services 30 Merchant acquiring and the $100 billion opportunity in small business

Foreword Last October, when we published McKinsey’s 2020 In this report, we follow our analysis of the Global Payments Report, it was already clear that key insights behind the 2020 (and estimated the pandemic’s economic impact would lead to the 2021) numbers with a set of chapters offering first decline in global payments revenues in 11 years. perspectives on critical areas where payments leaders’ actions will help determine market One year later, the picture is unexpectedly positive— trajectory. on the payments front—despite challenges. Payments revenue did indeed decline—to $1.9 First, the highly publicized field of digital currency trillion globally—but by less than we anticipated last is entering a critical new phase. Prominent private fall. Indicators point to a nominal but geographically firms are planning the introduction of “stablecoins,” uneven rebound in 2021, bringing revenue back while a growing number of central banks are into the range of 2019’s record high. From there, proceeding with plans for central bank digital McKinsey projects a return to historical mid- currencies (CBDCs) and simultaneously considering single-digit growth rates, generating 2025 global enactment of new regulations with the dual payments revenue of roughly $2.5 trillion. objectives of consumer protection and preserving the efficacy of traditional monetary policy. The The relatively muted 2020 topline numbers mask trend may yet evolve in any of several directions—or some important countervailing effects, however, ultimately prove to be more hype than substance. which are poised to reset the scale of opportunity In “CBDC and stablecoins: Early coexistence on for payments players for years to come. The an uncertain road,” we explore current initiatives, pandemic accelerated ongoing declines in cash highlighting potential challenges and opportunities usage and adoption of electronic and e-commerce for various financial players and steps each can transaction methods. Revenue gains in these take to prepare for and influence the ongoing areas were offset by tightening of net interest conversation. margins earned on deposit balances. All of these trends are expected to outlast the pandemic. The Next in the report, we look at the evolution of contraction of net interest income—combined with global transaction banking. Changes have been technology breakthroughs and the impact of open under way for some time, but the events of the past banking and fintech innovation—has spurred the 18 months have brought the needs of corporate creation of revenue models that within five years treasurers and CFOs into sharp relief. Historically, will offer adjacent opportunities as large as the core bank-provided treasury platforms have focused on payments revenue pool. 2 The 2021 McKinsey Global Payments Report

transaction execution. The advent of software-as- separating payment and software continues to a-service and API connectivity has enabled a varied blur. “Merchant acquiring and the $100 billion landscape of third-party providers to offer robust opportunity in small business” describes the multifunctional workstations. In “How transaction importance of expanding merchant acquiring and banks are reinventing treasury services,” we services to encompass a fuller array of commerce- examine the emergence of white-label treasury- related services, differentiation of merchant needs as-a-service solutions, the digitization of corporate between large corporate enterprises and small payments, and the options that banks have in this and medium-size enterprises as well as by various evolving ecosystem to defend and extend client sectors, and the ongoing impact of omnichannel opportunities. commerce on merchant services. We close with a look at how the new payments- As always, we welcome the opportunity to discuss adjacent revenue models will help define these essential payments topics with you in the future of merchant services, as the line greater detail. Alessio Botta Philip Bruno Jeff Galvin Senior Partner, Milan Partner, New York Senior Partner, Tokyo McKinsey & Company McKinsey & Company McKinsey & Company The authors wish to thank the following colleagues for their contributions to this report: Ashwin Alexander, Diksha Arora, Sukriti Bansal, Reet Chaudhuri, Vaibhav Dayal, Ian De Bode, Olivier Denecker, Nunzio Digiacomo, Puneet Dikshit, Matt Higginson, Vik Iyer, Yash Jain, Tobias Lundberg, Yaniv Lushinsky, Baanee Luthra, Matteo Mantoan, Ratul Nagpal, Marc Niederkorn, Glen Sarvady, Nikki Shah, Julie Stefanich, Bharath Sattanathan, and Aparna Tekriwal. With special thanks to Vijay D’Silva for his guidance. The 2021 McKinsey Global Payments Report 3

Global Banking & Securities Global payments 2021: Transformation amid turbulent undercurrents The global payments sector is poised for a quick return to healthy growth, but the benefits will not flow evenly to all participants. by Philip Bruno, Olivier Denecker, and Marc Niederkorn October 2021 © Goja1 /Getty Images 4

Undoubtedly, 2020 was a tumultuous year on economies spent significant portions of the year in many levels. Payments was no exception—the lockdown. sector experienced its first revenue contraction in 11 years, a consequence of the economic slowdown Looking forward, we see a handful of primary drivers that accompanied the global health crisis of COVID- influencing the payments revenue trajectory. On the 19. Still, government and regulatory measures such one hand, continued cash displacement and a return as fiscal and monetary stimulus held the decline to global economic growth will accelerate existing below the 7 percent we projected in last year’s upward trends in the share and number of electronic report.¹ At the same time, the continued digitization transactions. On the other, interest margins will of commercial and consumer transactions likely remain muted. Sustained softness in this key contributed even greater upward momentum than topline contributor will create greater incentive for expected. payments players to pursue new fee-driven revenue sources and to expand beyond their traditional focus Global payment revenues totaled $1.9 trillion in to adjacent areas such as commerce facilitation and 2020, a 5 percent decline from 2019 (Exhibit 1), as identity services. compared to the 7 percent growth rate observed between 2014 and 2019. This result seems fairly Given the above assumptions we expect global intuitive on the surface; a granular analysis, payments revenues to quickly return to their long- however, reveals a series of often offsetting trends. term 6 to 7 percent growth trajectory, recouping Overall, the payments industry proved remarkably 2020’s declines in 2021 and reaching roughly resilient to drastic economic changes even as many $2.5 trillion by 2025. More importantly, however, 1Philip Bruno, Olivier Denecker, and Marc Niederkorn, “Accelerating winds of change in global payments,” October 2020, McKinsey.com. Exhibit 1 Global payments revenues declined by 5 percent in 2020. Global payments revenue, $ trillion –5% p.a. +7% p.a. 2.6 CAGR, % 0.2 2015 20 2020 25F +7% p.a. 2.0 0.4 0.2 56 2.0 1.9 0.4 0.6 24 0.2 0.2 0.5 0.3 46 1.5 0.3 0.5 68 0.1 0.9 Latam 1.2 0.1 0.3 0.5 EMEA 0.3 0.4 1.3 NA 0.3 1.0 APAC 1.0 0.5 0.7 2011 15 19 20 21E 2025F 39 40 Share of banking 38 38 39 37 revenues, % Source: McKinsey Global Payments Map 5 Global payments 2021: Transformation amid turbulent undercurrents

as “payments” become further absorbed into 2020–21: A period of transition commercial and consumer commerce journeys, established payments providers will gain access The overall 5 percent decline in payment to adjacent opportunities as large as the core revenues is composed of divergent regional trends: payments revenue pool. Of course, an opportunity Asia–Pacific, which has consistently outpaced of this magnitude draws attention—tech firms and other regions in payments revenue growth over the ecosystem competitors are already focusing on past decade, registered a 6 percent pullback in these attractive (and often less regulated) elements 2020, while Latin America’s 8 percent decline was of the payments value chain, rather than traditional the steepest of all regions. Europe, Middle East, interchange, acquiring, and transaction fees linked and Africa (EMEA) and North America experienced to payment flows. revenue declines of 3 percent and 5 percent, respectively, mostly driven by continued reduction Following a brief review of 2020 results and of net interest margins (NIMs) in EMEA and preliminary snapshot of 2021’s projected outcome, contracting credit card balances in North America. we will explore these opportunities in greater detail. Exhibit 2 Asia-Pacific dominates the global payments revenue pool. Payments revenue, 2020, % (100% = $ billion) 100%= APAC North EMEA Latin Cross-border 900 America 335 America transactions 8 485 16 155 5 6 16 11 Account- 30 10 10 related 19 12 liquidity 11 Commercial 3 24 3 1 9 12 Domestic transactions 2 3 Credit cards 4 18 17 12 Cross-border 2 transactions Account- 15 related Consumer liquidity Domestic 7 33 22 transactions 34 Credit cards 15 11 1Cross-border payment services (B2B, B2C). Net interest income on current accounts and overdrafts. Fee revenue on domestic payments transactions and account maintenance (excluding credit cards). Remittance services and C2B cross-border payment services. Source: McKinsey Global Payments Map Note: Figures may not sum to 100%, because of rounding. Global payments 2021: Transformation amid turbulent undercurrents 6

The global contribution of net interest income (NII) to the 2019 result while setting the stage for a to payments revenue has declined steadily from 51 broad-based recovery. From that point, we forecast percent in 2010 to 46 percent in 2020. Over the five-year revenue growth rates roughly on par with past year, a 31-basis-point contraction in global those generated in the five years preceding the interest margins (compared to a decline of 25 bps pandemic—excluding the realization of additional predicted last fall) reduced payments revenue by revenue sources discussed below. $66 billion—two-thirds the total global net decline. Proportionally, the impact was felt even more Enduring shifts in behavior sharply in EMEA, which traditionally relies more heavily on NII, and endured an absolute decline of The pandemic reinforced major shifts in payments $42 billion over the past decade (Exhibit 2). Some behavior: declining cash usage, migration from banks have begun offsetting the interest revenue in-store to online commerce, adoption of instant loss through higher account maintenance fees, payments. These shifts create new opportunities for while negative interest rates on accounts have payments players; however, it is unclear which are materialized in some European markets—mostly on permanent and which are likely to revert—at least corporate accounts but increasingly on large retail partially—to prior trajectories as economies reopen. deposits as well. Nonetheless, the long-term dynamics seem clear. Cross-border payments, a natural casualty of Cash payments declined by 16 percent globally in reduced travel and global supply chain challenges, 2020, performing in line with the projections we accounted for the remainder of the revenue decline. made last fall for most large countries (Brazil 26 By contrast, the explosion in e-commerce and percent decline, United States 24 percent decline, reduction in cash usage helped minimize the decline United Kingdom 8 percent decline). Although the in domestic transaction fee income. pandemic-driven temporary shuttering of many commercial venues was the primary trigger in this We expect pressure on both fee and processing dramatic shift, other actions (such as countries like margins to continue in many regions, while Argentina, Poland, and Thailand increasing ATM recovery in interest margins is expected to be slow withdrawal fees, and the continued downsizing and moderate at best. These combined forces of ATM networks in Europe) reinforced and disproportionally affect incumbent players reliant accelerated behavioral changes already under way. on traditional revenue streams, such as card issuers We expect cash usage to rebound to some extent in and banks holding significant commercial and 2021, due to a partial return to past behaviors, fewer consumer deposit balances, and thus spur a need lockdowns, and a broader economic recovery, but to rethink payments revenue models and identify evidence indicates that roughly two-thirds of the alternative paths to value. decrease is permanent. As might be expected given 2021’s uneven global The reduction in cash demand is leading to economic recovery, payments trends are showing increasing unit servicing costs for its distribution similar disparity by country and region; for instance, and collection, prompting banks to review ATM revenues in Asia-Pacific and Latin America are footprints and rethink their cash cycle management. expected to grow in the 9 to 11 percent range, One response has been growth in ATM sharing compared to EMEA and North America at 4 to 6 between network banks and greater outsourcing percent. In aggregate, a likely solid increase in 2021 of ATM servicing to specialized cash-in-transit (CIT) should leave global payments revenues equivalent players—first observed in Northern Europe and now in Latin America (for example, a joint 7 Global payments 2021: Transformation amid turbulent undercurrents

venture between Euronet and Prosegur Cash which point 40 percent of combined debit/credit to provide comprehensive ATM outsourcing contactless volume originated via digital wallets.² services). In Indonesia, the value of e-money transactions grew by nearly 39 percent between 2019 and 2020, Regulators in countries with dramatic reductions fueled primarily by an increase in digital adoption.³ in cash usage are preparing strategies to ensure continued availability of central bank currency and Real-time payments are playing an increasingly access to resilient and free payments systems important role in the global payments ecosystem, for all—including the un- and underbanked. The with the number of such transactions soaring situation is driving heightened interest in central by 41 percent in 2020 alone, often in support of bank digital currencies (CBDCs), as discussed in contactless/wallets and e-commerce.⁴ Over the chapter 2. last year growth in instant payments varied widely across countries—from Singapore at 58 percent to Retailers, particularly digital commerce the United Kingdom at 17 percent. marketplaces, have elevated their competitive position, moving from traditional credit-card and Asia-Pacific continues to lead the way in real-time consumer-finance solutions to pursue deepened payments: India registered 25.6 billion transactions customer engagement leveraging payment in 2020 (a 70 percent-plus increase over 2019), solutions. For example, MercadoLibre, Latin followed by China and South Korea. Real-time America’s largest e-commerce player, owns the functionality also fueled mobile wallet adoption online payments network MercadoPago, and has in Brazil, which introduced its national real-time built an ecosystem encompassing marketplace, payments system, PIX. Fifty-six countries now have payments, shipping, software-as-a-service, and active real-time payment rails, a fourfold increase advertising. The enhanced customer experience, from just six years earlier. In many cases these new as well as revenue and valuations generated by clearing and settlement systems took some time retailers, have challenged banks to up their game to build momentum but are now delivering long- in order to preserve their market position. One promised volumes. example is the collective launch of mobile payments platform Modo by more than 35 Argentine financial The introduction of applications capitalizing on institutions in December 2020, offering a solution instant payments infrastructure in recent years for account-to-account money transfers and (PhonePe and GooglePay in India, PayNow in in-store QR payments. Singapore) has given added impetus to growth. Regional solutions are also staking out ground New form factors, faster payments between global networks (such as Visa and Mastercard) and incumbent domestic schemes. For As expected, both the pandemic’s impact and the example, the European Payments Initiative (EPI) is resulting economic environment led to significant building a unified pan-European payments solution shifts in spending patterns. Globally, the number of leveraging the Single Euro Payments Area (SEPA) non-cash transactions grew by 6 percent from 2019 Instant Credit Transfer (SCT Inst) scheme for point to 2020. of sale as well as online usage. In the United States, The Clearing House’s RTP clearing and settlement Digital-wallet usage surged, as consumer system has been steadily building volume since its preferences evolved even within contactless 2017 launch, with Visa Direct and Mastercard Send forms. In Australia, an early success story in “tap offering related in-market functionality, and the to pay” adoption, digital-wallet transactions grew Federal Reserve’s FedNow Service scheduled to 90 percent from March 2020 to March 2021—by launch in 2023. 2“Digital wallets poised to overtake contactless cards as instore payment of choice in Australia,” Finextra, May 19, 2021, finextra.com. 8 3Janine Marie Crisanto, “Indonesia e-wallet transaction to reach $18.5 billion in 2021 amid fierce competition,” The Asian Banker, April 9, 2021, theasianbanker.com. 4 “Global Real-Time Payments Transactions Surge by 41 Percent in 2020 as COVID-19 Pandemic Accelerates Shift to Digital Payments - New ACI Worldwide Research Reveals,” ACI Worldwide, March 29, 2021, investor.aciworldwide.com. Global payments 2021: Transformation amid turbulent undercurrents

Initial real-time payment growth has been primarily facilitator for expanding e-commerce volumes and in peer-to-peer settings and online transactions. as a means for governments to rapidly disburse The next tests will be the consumer-to-business welfare and other social payments. Examples point-of-sale and billing spaces (the latter proliferated across the globe: a digital ID system representing a B2B opportunity as well), and their enabled Chilean authorities to swiftly pre-enroll more straightforward paths to monetization. millions of beneficiaries in social programs and allowed potential recipients to confirm eligibility and, The pandemic has pushed businesses to where necessary, appeal their support status online.⁵ reorient their payments operations and customer In Thailand, more than 28 million people applied interactions. Small and medium-size enterprises for a new benefit for informal workers affected (SMEs) are increasingly aware of the payment by the pandemic: a digital ID system enabled the solutions available to them and are motivated to government to efficiently filter out those eligible for encourage the use of those that best serve their assistance through other programs. needs and those of their customers. For instance, payments providers are competing to offer Digital ID–enabled payment solutions achieved customized solutions like QR code, “tap to pay,” broader usage as well. Transactions through and link-based payments (processes initiated by India’s bank-led and real-time Aadhaar Enabled merchants sharing a URL) that make the payment Payments System (AEPS) more than doubled over experience seamless, pleasant, and increasingly the two years ending in March 2021, while the value contactless. Simplification in the merchant conveyed more than tripled over the same period. onboarding process can also help in attracting more sellers, reducing cost, and elevating the merchant Cross-border payments remain a significant growth experience. area (Exhibit 3). In 2020, even with travel and trade volumes in decline, cross-border e-commerce For example, Mastercard in India launched Soft transactions grew 17 percent. Volumes for cross- POS, a multiform-factor white-label solution for border network provider SWIFT were 10 percent banks and payments facilitators that enables a higher in December 2020 compared to the prior smartphone to function as a merchant acceptance year: not only has the “re-shoring” of production device. Other examples include value-added chains and related shift in trade flows we expected services like virtual shops and solutions that record last year so far failed to materialize, but increases and store credit transactions. Network-based in non-trade payment flows have more than offset marketing enables SMEs to reach a larger pool of lower transaction volumes in trade, driven by customers. increased volatility in treasury, FX, and securities. These dynamics are leading to growth in volumes Social-media platforms have embedded payment as well as record market valuations for a growing features, enabling SMEs to execute sales through list of payments specialists such as Currencycloud networks such as Instagram. Venmo’s social- (recently acquired by Visa), Banking Circle, and Wise. commerce platform helps build SME brand awareness as users can see, like, and comment on The B2B payment arena is also showing strong each other’s purchases—a useful feature for street growth internationally, especially when viewed vendors and small-business owners who often lack in conjunction with invoicing and accounts funds to invest in marketing and promotions. receivable/accounts payable (AR/AP) management solutions. The largest transaction banks continue to New opportunities in payments invest in innovative solutions; and Goldman Sachs, a more recent entrant into the space, is developing a The push for digital identity verification systems platform including integration with SAP Ariba. Given gained momentum during the pandemic, both as a industry-wide initiatives—led by SWIFT and the 5Mari Elka Pangestu, “Harnessing the power of digital ID,” World Bank Blogs, August 20, 2020, blogs.worldbank.org. 9 Global payments 2021: Transformation amid turbulent undercurrents

Exhibit 3 Cross-border payment results were mixed, due to nuances in the underlying segments. Global cross-border payments revenues 2019 2020 Cross-border payments xx% YoY growth Counterparty revenue, $ billion Growth, 2019 20, % Key drivers XB e-com 11 15 20 • Strong growth due to changed customer preferences C2B 13 35 even post lockdown XB non e-com 15 –55 60 • Margins grew slightly with rise in value-added services • Non e-commerce severely affected with travel restrictions • Margins remained flat to boost spending Non-trade 100 5 10 • Increased flows due to volatility in treasury, FX, securities alongside capital deployment for operations B2B 110 • Usual FX spread decline took a pause amid volatility 40 • Falling demand in certain sectors and commodity price Trade –5 10 fluctuations (eg, oil) 37 • Margins remained stable with slightly wider FX spreads 1Revenues include payment and collection fees, FX spread and float revenue, and documentary business fees for relevant trade flows for 46 Payments Map countries driving approximately 95% of global GDP. Estimates, rounded. C2C and B2C not included. Source: McKinsey Global Payments Map Financial Stability Board (FSB)—aiming to further As payments become integrated into broader increase efficiency of cross-border transactions, customer journeys, the sector’s boundaries have we project 6 percent revenue growth in total cross- naturally expanded. In the 1980s, we defined border payments revenue over payments as the various instruments, networks, the next five years. We discuss this further in access and delivery mechanisms, and processes chapter 3. facilitating the exchange of value between buyers and sellers of goods and services. But this notion The next frontier of payments as a discrete experience is gradually disappearing. The payments industry now The process of reexamining long-standing encompasses the end-to-end money-movement payments value propositions is already under process, including the services and platforms way. While old tenets still hold true—scale still enabling this commerce journey. matters and “owning” the customer relationship remains important, for instance—sticking to them For example, while payments as traditionally defined is no longer sufficient to ensure success. The comprise only 5 to 7 percent of a typical merchant’s absorption of payments into the full commercial/ software and services spending, payments consumer purchase-to-pay journey has given rise to providers with solid reputations for execution and ecosystems demanding new, more robust services; innovation are well positioned to deliver solutions for example, commerce facilitation rather than a addressing needs constituting 40 percent of such discrete payment experience. expenses. Such opportunities help explain why less Global payments 2021: Transformation amid turbulent undercurrents 10

than one-third of Square’s revenue would be strictly services through reimagined front ends. Most categorized as payments. Similarly, within five examples to date have centered on consumer- years, we expect 40 percent of merchant acquirer facing solutions, but potential remains on the revenues to stem from activities other than payment commercial side as well. Other important and processing. less commoditized value-added items include digital identity, risk solutions, charge-back For players with established credibility in the mitigation, and KYC-as-a-service. provision of core payments functionality, the following areas offer attractive natural extensions, — Commerce, sales, and trade enablement. although these opportunities will not be evenly Non-bank market entrants often derive their distributed across regions: value from related services, driving down payments pricing in the process. Banks must — Payments and banking-adjacent software, consider similar approaches to avoid being infrastructure, and services. The largest shares disadvantaged. In most cases, marketplaces of payments revenue continue to accrue at have successfully cultivated an adequate stream the endpoints of the value chain, where direct of prospective buyers; attracting an ample interaction with payers and payees is central supply of sellers with distinctive wares is a more to the proposition. Even as the payment “pipes” vexing challenge—one that payments facilitators and underlying technology face potential are well positioned to solve, leveraging data commoditization, opportunities abound in analytics to reduce time to revenue. Solutions the rapidly evolving payments-as-a-service focused on automating the onboarding process, space, through which traditional players provide increasing the stickiness of users, and improving the transactional and compliance backbone the seller experience should find a ready market. that enables partners to deliver adjacent Examples include affiliate marketing, loyalty Asia–Pacific’s $210 billion payments revenue opportunity Asia–Pacific has been the largest and fastest-growing payments revenue region for the past several years. Given the consistently strong growth rate of China’s economy, this result is not surprising. More interesting, however, is the unique composition of Asia–Pacific’s payments revenue and its implications for longer-term growth. It is illuminating to consider the payments characteristics of the rest of Asia-Pacific apart from China. Whereas China accounts for roughly three-fourths of the region’s revenue—and indeed generates more payments revenues than any of the individual major global regions—a disproportionate share of its payments revenue is generated by net interest margins earned on deposit balances—particularly those in commercial accounts. As a consequence, the majority of China’s pay- ments economics are inaccessible to institutions and providers domiciled outside the country. The payments dynamics for the rest of Asia–Pacific stand in stark contrast (exhibit). In fact, these characteristics bear a striking resemblance to Latin America—not only in terms of total revenue (its $210 billion is roughly 35 percent higher than Latin America’s)—but more importantly in its relative focus on consumer activity and credit cards. Only a third of Asia–Pacific’s revenues outside of China are derived from account liquidity, as compared to 50 percent for China. The pandemic has accelerated reductions in cash usage, particularly in key markets like Indonesia and Thailand, creating new digital revenue opportunities. While some transactions will return as physical storefronts reopen, a solid majority has likely moved permanently to card and wallet-based forms, as well as to emerging online categories such as telemedicine and online yoga and fitness. 11 Global payments 2021: Transformation amid turbulent undercurrents

Exhibit Payments revenue dynamics vary across Asia–Pacific. Payments revenue, 2020, % (100% = $ billion) 100%= 690 210 Cross-border 7 12 transactions 13 11 Account-related 35 4 liquidity 4 Commercial 19 Consumer Domestic 22 12 transactions 4 26 Credit cards Cross-border 1 14 Asia-Pacific transactions 5 excluding China Account-related 11 liquidity China Domestic transactions Credit cards 1Cross-border payment services (B2B, B2C). Net interest income on current accounts and overdrafts. Fee revenue on domestic payments transactions and account maintenance (excluding credit cards). Remittance services and C2B cross-border payment services. Source: McKinsey Global Payments Map Although China has served as Asia–Pacific’s primary growth driver over the past decade, India’s payments revenues are now growing at a faster rate, and in 2020, surpassed Japan as the region’s second-largest revenue generator. Indonesia is another impressive growth story, posting a 2014–19 CAGR of nearly 9 percent, coinciding with multiple payments-related reforms launched by the regulator. A decline in NIMs reversed this trend for 2020, but indicators point to a return to rapid growth in 2021. We project India and Indonesia alone will generate $34 billion of incremental annual revenue by 2025, rep- resenting annual growth of nearly 8 percent. Despite low single-digit revenue growth in mature payments countries such as Japan and Australia, we forecast the Asia– Pacific region excluding China to grow at nearly 7 percent between 2021 and 2025—a rate only slightly slower than China’s. The growth rates of strategically important payments categories like cross-border and instant payments are also expected to remain on similar trajectories. The region is filled with opportunity: from rapidly expanding B2B activity to an explosion in digital wallets supporting small businesses as well as consumers, accelerated digitization fueled by rapid infrastructure developments, and integrated platforms providing access to multiple ecosystems. Increased access to real-time payment rails has fueled rapid growth in bilateral cross-border payment activity: notable early successes span the Singapore, Indonesia, and Thailand corridors—an area with significant potential for value-added services. Players interested in the Asia–Pacific market should not overlook growth engines in countries beyond China, many of which offer clearer paths to foreign participation. Global payments 2021: Transformation amid turbulent undercurrents 12

solutions, e-invoicing platforms, and B2B trade opportunities for incumbents and new entrants directories. alike to participate in emerging adjacent revenue streams, further brightening the future picture. — Balance-sheet-based offerings. Banks are similarly well equipped to introduce new These benefits will not flow evenly to all, however. solutions based on emerging payment methods Players electing not to adapt their strategies— such as instant payment and “buy now pay later” whether by choice, inaction, or lack of investment (BNPL) models, or to integrate new solutions and capacity—are likely to endure below-peer technologies into existing value propositions. growth and risk being displaced on key customer Financing and deposit models with significant experiences. regulatory requirements or higher risk profiles (including credit cards, BNPL, supply chain and In the remainder of this report, we outline the SMB financing) are among the promising areas. opportunities—as well as the threats—emerging in cryptocurrencies and CBDCs, global The payments sector is poised for a quick return transaction banking, and merchant services. to healthy 6 to 7 percent growth rates, with fresh Philip Bruno is a partner in McKinsey’s New York office, Olivier Denecker is a partner in the Brussels office, and Marc Niederkorn is a partner in the Luxembourg office. The authors would like to thank Sukriti Bansal, Baanee Luthra, Glen Sarvady, Yash Jain, Aparna Tekriwal, Diksha Arora, Vaibhav Dayal, and Ratul Nagpal for their contributions to this chapter. Copyright © 2021 McKinsey & Company. All rights reserved. 13 Global payments 2021: Transformation amid turbulent undercurrents

Global Banking & Securities CBDC and stablecoins: Early coexistence on an uncertain road With the rapid rise in circulation of stablecoins over the past couple of years, central banks have stepped up efforts to explore their own stable digital currencies. by Ian De Bode, Matt Higginson, and Marc Niederkorn October 2021 © Sunyixun/Getty Images 14

Cryptocurrency has been touted for its potential (blockchain-based) ledger for transaction to usher in a new era of financial inclusion and execution and record keeping, and by creating a simplified financial services infrastructure globally. (now) widely traded currency outside the control To date, however, its high profile has derived more of any sovereign monetary authority. Thousands of from its status as a potential store of value than as similar decentralized cryptocurrencies now exist, a means of financial exchange. That disconnect is collectively generating billions of dollars in global now evolving rapidly with both monetary authorities transaction volume every day. and private institutions issuing stabilized cryptocurrencies as viable, mainstream payments Although the aggregate market value of such vehicles. cryptocurrencies now exceeds $2 trillion, extreme price volatility, strong price correlation to Bitcoin, The European Central Bank announced recently it and often slow transaction confirmation times was progressing its ‘digital euro’ project into a more have impeded their utility as a practical means of detailed investigation phase.¹ More than four-fifths value exchange. Stablecoins aim to address these of the world’s central banks are similarly engaged shortcomings by pegging their value to a unit of in pilots or other central bank digital currency underlying asset, often issued on faster blockchains, (CBDC) activities.² Concurrently, multiple private, and backing the coins wholly or partially with stabilized cryptocurrencies—commonly known state-issued tender (such as the dollar, pound, as stablecoins—have emerged outside of state- or euro), highly liquid reserves (like government sponsored channels, as part of efforts designed to treasuries), or commodities such as precious metals. enhance liquidity and simplify settlement across Collectively, nearly $3 trillion in stablecoins such as the growing crypto ecosystem. Tether and USDC were transacted in the first half of 2021 (Exhibit 1). Although the endgame of this extensive activity that spans agile fintechs, deep-pocketed incumbents, With the rapid rise in circulation of stablecoins and (mostly government-appointed) central over the past couple of years, central banks have banks remains far from certain, the potential for stepped up efforts to explore their own stable significant disruption of established financial digital currencies (Exhibit 2). Some efforts to processes is clear. Against this backdrop we offer a create CBDCs have been born out of reservations fact-based primer on the universe of collateralized about the impact of privately issued stablecoins on cryptocurrency, an overview of several possible financial stability and traditional monetary policy, future scenarios including potential benefits and and with the goal of improving access to central obstacles, and near-term actions that participants bank money for private citizens, creating greater in today’s financial ecosystem may consider in order financial inclusion and reducing payments friction. to position themselves. Various public statements indicate that central The digital currency landscape banks envision CBDCs as more than simply a digital-native version of traditional notes and The basic notion of a digital currency (replacing coins. Beyond addressing the challenge of greater the need for paper notes and coins as a means financial inclusion, some governments view CBDCs of exchange with computer-based money-like as programmable money—vehicles for monetary assets) dates back more than a quarter of a century. and social policy that could restrict their use to basic Early efforts at creating digital cash—such as necessities, specific locations, or defined periods DigiCash (1989) and e-gold (1996)—were issued of time. by central agencies. The emergence of Bitcoin in 2009 dramatically altered this model in two Implementing such functionality will be a complex important ways: by establishing a decentralized and multilayered undertaking. Meanwhile, central 1 “Eurosystem launches digital euro project,” press release, European Central Bank, July 2021, ecb.europa.eu. 2Codruta Boar and Andreas Wehrli, Ready, steady, go? Results of the third BIS survey on central bank digital currency, Bank for International Settlements, BIS Papers, number 114, January 2021, bis.org. 15 CBDC and stablecoins: Early coexistence on an uncertain road

Exhibit 1 The rise in circulation of stablecoins has closely tracked the volume of cryptocurrencies traded on exchanges over the past three years. Cryptocurrency volume On-chain volume of stablecoins¹ Stablecoins volume $ billion Cryptocurrency exchange volume $ billion 3000 800 2500 700 2000 600 1500 500 1000 400 500 300 0 Aug-17 Jan-18 200 100 0 Jan-19 Jan-20 Jan-21 1Volume of stablecoins exchanged represents all transactions recorded on the relevant blockchains. These volumes are distinct from the volume of crypto traded on exchanges, some of which may be transacted between accounts off-chain. Source: Theblockcrypto.com Exhibit 2 The proportion of central banks actively engaged in CBDC work is growing. Share of respondents conducting work on CBDCs, % 90 2018 2019 2020 80 70 60 50 40 30 20 10 0 2017 Source: Codruta Boar and Andreas Wehrli, “Ready, steady, go? – Results of the third BIS survey on central bank digital currency,” Bank for International Settlements, January 2021, bis.org. CBDC and stablecoins: Early coexistence on an uncertain road 16

banks face the challenge of introducing a timely By comparison, stablecoins such as the dollar- CBDC model at least on par with digital offerings denominated USDC are issued across multiple of private-sector innovators in order to establish public, permissionless blockchains. Any individual credibility with such efforts and achieve adoption. can operate a node of an issuing blockchain such While existing electronic payment systems are as Ethereum, Stellar, or Solana; and anyone can considered by some to be expensive, inefficient, transfer stablecoins between pseudonymous and at times difficult to access,³ emerging privately wallets around the world. While most exchanges issued stablecoin alternatives could raise concerns today require users to complete thorough Know over the potential for large private entities to Your Customer (KYC) identity checks, no central aggregate—and monetize—large sets of behavioral registry for users or single ledger for tracking data on private citizens. ownership of stablecoins currently exists, potentially complicating identity considerations. Potential future scenarios: Coexistence Many see the current development of CBDCs or primacy? as a response to the challenge private-sector stablecoins could pose to central bank prerogatives, It is too early to confidently forecast the trajectory and as evidence of the desire of institutions and endgame for CBDCs and stablecoins, given the to address long-term goals such as payment multitude of unresolved design factors still in play. systems efficiency and financial inclusion. Cash For instance, will central banks focus first on retail usage in many countries continues to dwindle, or wholesale use cases, and emphasize domestic while the cost to maintain its infrastructure does or cross-border applications? And how rapidly will not. Similarly, many countries’ existing electronic national agencies pursue regulation of stablecoins payment systems are relatively inefficient to prior to issuing their own CBDCs? operate and often not instantaneous or 24/7. Perhaps most importantly, proper deployment of To begin to understand some of the potential a regulated digital currency accessible through scenarios, we need to appreciate the variety and mobile devices without the need for a formal bank applications of CBDCs and stablecoins. There account could potentially enhance payments is no single CBDC issuance model, but rather a security and efficiency (ensuring transaction finality continuum of approaches being piloted in various through distributed consensus with private key countries. One design aspect hinges on the cryptography), while satisfying central banks’ goal entity holding CBDC accounts. For instance, the of increasing financial inclusion and advancing the account-based model being implemented in the public good. Eastern Caribbean involves consumers holding deposit accounts directly with the central bank. At By contrast private stablecoins have flourished, the opposite end of the spectrum, China’s CBDC perhaps in part through being unencumbered by pilot relies on private-sector banks to distribute such an expansive mission. They’ve delivered value and maintain eCNY (digital yuan) accounts for their as a source of liquidity in the crypto ecosystem, customers. The ECB approach under consideration often providing a “safe haven” for investors involves licensed financial institutions each during times of heightened volatility by obviating operating a permissioned node of the blockchain the need to enlist a regulated venue to convert network as a conduit for distribution of a digital cryptocurrency holdings back into fiat deposits. euro. In a potential fourth model popular within the Indeed, the emergence and growth of supply of the crypto community but not yet fully trialed by central prominent stablecoin Tether first coincided with the banks, fiat currency would be issued as anonymous rapid increase in cryptocurrency transaction volume fungible tokens (true digital cash) to protect the privacy of the user. 3“From the payments revolution to the reinvention of money,” speech by Fabio Panetta, Member of the Executive Board of the ECB, at the Deutsche Bundesbank conference on the “Future of Payments in Europe,” Frankfurt, November 27, 2020. 17 CBDC and stablecoins: Early coexistence on an uncertain road

on exchanges in late 2017, many of which did not providing sufficient convenience—or at minimum, a have fiat licenses. compelling vision—to create similar long-term value. Stablecoins are typically collateralized by The current state of financial infrastructure in a professionally audited reserves of fiat currency given country will play a key role in determining the or short-term securities. They play a role today speed and extent of adoption of CBDCs, stablecoins, not just as “crypto reserves” but also as a source or non-stabilized cryptocurrencies. Those of liquidity across decentralized finance (DeFi) with limited present-day capabilities are prime exchanges. Stablecoins, unlike the proposed candidates for a “leapfrog” event, similar to the rapid design of CBDCs, which are generally issued on emergence of M-Pesa as a payments vehicle in sub- private ledgers, can engage with smart contracts Saharan Africa⁵ or Alipay in China.⁶ In developed on public permissionless networks that enable economies with existing real-time payments rails, decentralized financial services. Significantly, they the near-term incremental benefits of reduced provide a medium for the instantaneous movement (even instantaneous) settlement time from CBDCs of value between exchanges and digital wallets, may be somewhat muted if financial institutions often to take advantage of short-lived arbitrage are reluctant to invest in the necessary additional opportunities, to settle bilateral over-the-counter infrastructure. In these instances, distinct benefits (OTC) trades or to execute cross-border payments. of stablecoins (such as their ability to engage with This utility as a vehicle for payments is demonstrated smart contracts) may prove to be a more compelling by the more than $1 trillion in stablecoin transaction and defensible use case over the longer term, volumes per quarter in 2021 (although this remains a depending on the exact CBDC implementation. fraction of traditional payment volumes cleared) and may grow to play an important role in the future of Residents of countries with sovereign currencies digital commerce ecosystems. lacking historical stability have been among the most active adopters of cryptocurrencies Although a solid case can be made for the as a means of exchange, especially where they coexistence of stablecoins and CBDCs (providing are perceived as less risky than the available separate services such as DeFi services and alternatives. Along with the potential for digital liquidity provisioning, and direct access to central currencies to foster financial inclusion for citizens bank money, respectively), plausible scenarios could lacking access to traditional banking services also lead to the long-term preeminence of either (utilizing a universal digital wallet instead of a instrument. Some regulatory bodies have already traditional fiat account), such an environment could expressed concern over substantial value flows serve as an indicator for a market primed for a settling via private stablecoins, implying potential potential leapfrog event (for example, the national actions to manage or curtail their use.⁴ Equally, full acceptance of Bitcoin in El Salvador⁷). digitization of sovereign currencies could facilitate easier global trade flows. Given the notable Ultimately the fate of CBDCs and stablecoins may proliferation of stablecoins over the past 12 months, be decided by the significant forces of regulation however, private-sector networks have gained and adoption. While CBDCs will be issued under “first mover” advantage, increasing expectations the auspices of central banks, stablecoins are for central banks to deliver timely solutions potentially subject to regulatory oversight from 4 Paul Vigna, “Risks of Crypto Stablecoins Attract Attention of Yellen, Fed and SEC,” Wall Street Journal, July 17, 2021, wsj.com; Tory Newmyer, “SEC’s Gensler likens stablecoins to ‘poker chips’ amid call for tougher crypto regulation,” The Washington Post, September 21, 2021, washingtonpost.com. 5 Daniel Runde, “M-Pesa and the rise of the global mobile money market,” Forbes, August 12, 2015, forbes.com. 6 Aaron Klein, “China’s Digital Payments Revolution,” Brookings, April 2020, brookings.edu. 7 Santiago Pérez and Caitlin Ostroff, “El Salvador becomes first country to adopt Bitcoin as national currency,” Wall Street Journal, September 7, 2021, wsj.com. 8 “G20 confirm their support for the FATF as the global standard-setter to prevent money laundering, terrorist financing and proliferation financing,” Financial Action Task Force, April 7, 2021, fatf-gafi.org. CBDC and stablecoins: Early coexistence on an uncertain road 18

multiple agencies, depending on their classification determined by geography (for example, central as assets, securities, or even money-market funds. banks such as China’s exerting greater influence Under scrutiny from the Financial Action Task Force, through direct control of monetary policy), by such regulation may be extended across borders.⁸ market incumbency among private institutions (for While it is too early to predict the impact of greater example, e-commerce or social media giants in regulation on stablecoins, innovation continues the United States with potential to migrate some apace with the likely emergence of many more (and user transactions to stablecoins), or by sector (for newer) varieties in coming years. In contrast, early example, use-based loyalty stablecoins). efforts to issue CBDCs have been met with only moderate adoption. For example, the equivalent Although the market is far too nascent to confidently of just over $40 million in Chinese digital Yuan has predict outcomes, constituents from all corners of thus far been distributed by lottery, and the People’s the payments ecosystem can take valuable steps to Bank of China has reported around 70 million position themselves for the inevitable changes on transactions since the launch of its limited multicity the horizon—regardless of the form such changes pilot in January 2021.⁹ While this represents a solid take: proof of concept, it compares with over two billion monthly active users reported by China’s largest — Providers of financial services infrastructure digital technology payment providers WeChat Pay should continually monitor the suitability of their and Alipay. design choices for future interoperability with digital currencies. For example, participation in Preparatory moves for an uncertain account-based CBDCs will likely involve direct landscape interaction with a permissioned node, while supporting stablecoins may require wallets Clearly these technological considerations, with cross-chain access. In particular, it may regulatory actions, and market dynamics carry be important to consider how these choices major systemic implications for banking and the support high-potential business cases (such payments industry. Sheer regulation is highly as instant disbursements), post-trade investor unlikely to suppress the demand for digital services, and rapid cross-border remittances. currencies, and innovators will continue to push the envelope by developing new uses and distribution — Retail banks, merchants, and payment models satisfying both demand and legislative service providers might consider the level of requirements. Similarly, the results of initial pilots infrastructure investment likely needed for and ongoing research of CBDCs will help shape their successful implementation of CBDCs and evolution and potential adoption. multiple stablecoin networks. Many retail banks already face extensive payments modernization It seems likely that the recent growth in circulation requirements in the coming years—tackling and transaction volume of stablecoins will infrastructure for digital currencies represents continue, at least as long as the overall size of an additional demand on limited development the cryptocurrency market continues to expand. capacity. Incorporating all such efforts into Similarly, digital-currency activities by central banks an integrated road map, reflecting potential are too widespread for current pilot efforts not to be synergies and possible triage, should promote extended. Will a two-tiered system of CBDCs and long-term efficiency and avoid duplication of stablecoins be sustainable over time? What are the effort. macroeconomic and geopolitical implications of the various scenarios? — The impact of CBDCs on private-sector banks likely depends on the speed of their adoption. Most likely there will be some form of coexistence. Specifically, if adoption of CBDCs were to Within this continuum we may see flavors happen relatively quickly, the flow of funds 9 Wolfie Zhao, “China publishes first e-CNY whitepaper, confirming smart contract programmability,” The Block, July 16, 2021, theblockcrypto. 19 CBDC and stablecoins: Early coexistence on an uncertain road

into bank deposits would be diverted, at least markets, although such limits are being built into temporarily, into digital cash, thereby limiting the some CBDC designs. ability of banks to lend and generate fee income with such deposits. Accordingly, it would seem — The task for government, central banks, and in the interest of private-sector banks for the regulators is somewhat more straightforward: introduction of CBDCs to be slower and more to some extent, their decisions will dictate the carefully orchestrated, potentially with initial moves of other parties, although any traction transaction limits. demonstrated by in-market stablecoin solutions will necessarily factor into central bankers’ — Chief risk and financial officers will benefit from approaches. We expect many will seek to assess evaluating the broad impact of digital currencies the impact of private currencies on the efficacy on bank liquidity and capital requirements of monetary policy (for instance, via value flows) given potential policy changes. They could and fiscal policy (for example, via government monitor potential increases in funding costs, the disbursements), tailoring regulatory and possibility of further erosion of payments profit supervisory changes accordingly. They will want margins (for example, given CBDC’s potential as to balance countervailing factors: extensive a frictionless “free” cash replacement), and even regulation could serve essentially to prevent safeguards against potential “digital bank runs”— stablecoin use, whereas measured approaches many of the existing “circuit breakers” that may create a safer environment in which such afford some protection for traders and investors currencies could flourish. currently do not exist in the 24/7 cryptocurrency Learning from China’s CBDC pilot The most advanced market application of CBDC to date has been the People’s Bank of China’s (PBoC) multicity pilot of its digital version of RMB, called eCNY. ¹ From late 2019 the PBoC began to pilot test eCNY in Shenzhen, Suzhou, Xiongan, and Chengdu, initially through app and wallet-based payments. The pilot gradually expanded to Shanghai, Hainan, Xian, Qingdao, and Dalian. As of June 2021, the pilot test included over 20 million personal wallets, more than 3.5 million merchant wallets, and aggregate throughput of more than 34 billion RMB ($5.2 billion). Initial focus has been on cash replacement for payment scenarios covering trans- portation, shopping, and government services. Financial inclusion is a key use case targeted to drive end-user adoption. A bank account will not be a prerequisite for consumer use of eCNY, unless a user desires to replenish a digital wallet. eCNY will carry the same legal status as cash; the PBoC will distribute the digital currency to six authorized state-owned banks, which will circulate it to consumers. Consum- ers are able to download and deploy a digital wallet from these banks without holding an account with them. Potential benefits include mitigated KYC risk and reduced compliance cost related to transaction monitoring and reporting, given eCNY’s “controlled anonymity” (only central banks will have full access to trading data). Enhanced technical under- writing capabilities are also anticipated, creating competitive differentiation for participating banks. As a social benefit, the digital currency is expected to streamline the distribution of targeted subsidies. CBDC and stablecoins: Early coexistence on an uncertain road 20

Concurrently, the PBoC has been testing cross-border payments witheCNY in Hong Kong, in a joint effort with the Hong Kong Monetary Authority. Considering the more than $500 billion of import/export trade between Hong Kong SAR and the Chinese Mainland, the combined impact of cross-border eCNY and eHKD being piloted could meaningfully impact existing financial markets and operators via lower transaction costs, more efficient (real-time) settlement, and support for product innovations such as smart contracts. Although no timelines for formal launch have been announced, plans are proceeding to feature eCNY capabilities at the 2022 Beijing Winter Olympics. 1 Formerly Digital Currency Electronic Payment or DC/EP. — Investors in highly popular and speculative emerge as a global currency? To what extent will cryptocurrencies—and their issuers—should citizens resist the full traceability of payments? And anticipate the impact of CBDCs on their assets. to what extent will citizens be comfortable The emergence of any single central-bank obtaining familiar banking services—such as high- solution and related regulation could deter yield deposits, collateralized lending, working private-sector innovation and hinder the growth capital, and payments services (all available in DeFi of crypto ecosystems, potentially unsettling today)—without reliance on a traditional bank? investors in an asset class driven so much by And finally, how quickly will we see innovation sentiment. in blockchain protocols (e.g., proof of stake) that dramatically reduces their environmental Most of all, the co-evolution of stablecoins and impact? CBDCs will directly impact society. While the future is not yet clear, certain behaviors could well signal We expect answers to many of these questions to the direction of this evolution: to what extent will become clearer over the next few years as both physical cash still be used—and accepted—in stablecoins and CBDCs become more widely society? In what medium of value will employees and available, and the payments industry confronts bills be paid? Through what means will commerce perhaps the biggest disruption in its history. While be conducted, particularly if digital currencies the use cases of CBDCs and stablecoins are still issued on public distributed ledgers lower the cost emerging, it is not too early to prepare for such of hosting accounts and speed payment delivery, disruption. and to what extent could a single digital currency Ian De Bode is an associate partner in McKinsey’s San Francisco office, Matt Higginson is a partner in the Boston office, and Marc Niederkorn is a partner in the Luxembourg office. Copyright © 2021 McKinsey & Company. All rights reserved. 21 CBDC and stablecoins: Early coexistence on an uncertain road