Banking is at a pivotal moment. Technology disruption and consumer shifts are laying the basis for a new S-curve for banking business models, and the COVID-19 pandemic has accelerated these trends. Building upon this momentum, the advancement of artificial-intelligence (AI) technologies within financial services offers banks the potential to increase revenue at lower cost by engaging and serving customers in radically new ways, using a new business model we call “the AI bank of the future.” The articles collected here outline key milestones on a path we believe can lead banks to deeper customer relationships, expanded market share, and stronger financial performance.
The opportunity for a new business model comes as banks face daunting challenges on multiple fronts. In capital markets, many banks trade at a 50 percent discount to book, and approximately three-quarters of banks globally earn returns on equity that do not cover their cost of equity.1 Traditional banks also face diverse competitive threats from neobanks and nonbank challengers. Leading financial institutions are already leveraging AI for split-second loan approvals, biometric authentication, and virtual assistants, to name just a few examples. Fintech and other digital-commerce innovators are steadily disintermediating banks from crucial aspects of customer relationships, and large tech companies are incorporating payments and, in some cases, lending capabilities to attract more users with an ever-broader range of services. Further, as customers conduct a growing share of their daily transactions through digital channels, they are becoming accustomed to the ease, speed, and personalized service offered by digital natives, and their expectations of banks are rising.
To compete and thrive in this challenging environment, traditional banks will need to build a new value proposition founded upon leading-edge AI-and-analytics capabilities. They must become “AI first” in their strategy and operations. Many bank leaders recognize that the economies of scale afforded to organizations that efficiently deploy AI technologies will compel incumbents to strengthen customer engagement each day with distinctive experiences and superior value propositions. This value begins with intelligent, highly personalized offers and extends to smart services, streamlined omnichannel journeys, and seamless embedding of trusted bank functionality within partner ecosystems. From the customer’s point of view, these are key features of an AI bank.
The building blocks of an AI bank
Our goal in this compendium is to give banking leaders an end-to-end view of an AI bank’s full stack capabilities and examine how these capabilities cut across four layers: engagement, AI-powered decision making, core technology and data infrastructure, and a platform-based operating model.
In our first article, “AI-bank of the future: Can banks meet the challenge?,” we take a closer look at the trends and challenges leading banks to take an AI-first approach as they define their core value proposition. We continue by considering a day in the life of a retail consumer and small-business owner transacting with an AI bank. Then we summarize the requirements for each layer of the AI-and-analytics capability stack.
The second article, “Reimagining customer engagement for the AI bank of the future,” examines the capabilities that enable a bank to provide customers with intelligent offers, personalized solutions, and smart servicing within omnichannel journeys across bank-owned platforms and partner ecosystems.
In our third article, “AI-powered decision making for the bank of the future,” we examine how machine-learning models can significantly enhance customer experiences and bank productivity, and we outline the steps banks can follow to build the architecture required to generate real-time analytical insights and translate them into messages addressing precise customer needs.
The fourth article, “Beyond digital transformations: Modernizing core technology for the AI bank of the future,” discusses the key elements required for the backbone of the capability stack, including automated cloud provisioning and an API and streaming architecture to enable continuous, secure data exchange between the centralized data infrastructure and the decisioning and engagement layers.
As we discuss in our final article, “Platform operating model for the AI bank of the future,” deploying these AI-and-analytics capabilities efficiently at scale requires cross-functional business-technology platforms comprising agile teams and new technology talent.
Starting the journey
To get started on the transformation, bank leaders should formulate the organization’s strategic goals for the AI-enabled digital age and evaluate how AI technologies can support these goals.
Once bank leaders have established their AI-first vision, they will need to chart a road map detailing the discrete steps for modernizing enterprise technology and streamlining the end-to-end stack. Joint business-technology owners of customer-facing solutions should assess the potential of emerging technologies to meet precise customer needs and prioritize technology initiatives with the greatest potential impact on customer experience and value for the bank. We also recommend that banks consider leveraging partnerships for non-differentiating capabilities while devoting capital resources to in-house development of capabilities that set the bank apart from the competition.
Building the AI bank of the future will allow institutions to innovate faster, compete with digital natives in building deeper customer relationships at scale, and achieve sustainable increases in profits and valuations in this new age. We hope the following articles will help banks establish their vision and craft a road map for the journey.