Banks can meet rising customer expectations by applying AI to offer intelligent propositions and smart servicing that can seamlessly embed in partner ecosystems.
From instantaneous translation to conversational interfaces, artificial-intelligence (AI) technologies are making ever more evident impacts on our lives. This is particularly true in the financial-services sector, where challengers are already launching disruptive AI-powered innovations. To remain competitive, incumbent banks must become “AI first” in vision and execution, and as discussed in our previous article,1 this means transforming the full capability stack, including the engagement layer, AI-powered decision making, core technology and data infrastructure, and operating model. If fully integrated, these capabilities can strengthen engagement significantly, supporting customers’ financial activities across diverse online and physical contexts with intelligent, highly personalized solutions delivered through an interface that is intuitive, seamless, and fast. These are the baseline expectations for an AI bank.
In this article, we examine how banks can take an AI-first approach to reimagining customer engagement. We focus on three elements with potential to give the bank a decisive competitive edge:
- The value of re-imagined customer engagement: By reimagining customer engagement, banks can unlock new value through better efficiency, expanded market access, and greater customer lifetime value.
- Key elements of the re-imagined engagement layer: The combination of intelligent propositions, seamless embedding within partner ecosystems, and smart servicing and experiences underpins an overall experience that sets the AI bank apart from traditional incumbents.
- Integrated supporting capabilities: As banks rethink and rebuild their engagement capabilities, they need to leverage critical enablers, each of which cuts across all four layers of the capability stack.
The value of reimagined customer engagement
In recent years, many financial institutions have devoted significant capital to digital-and-analytics transformations, aiming to improve customer journeys across mobile and web channels. Despite these big investments, most banks still lag well behind consumer-tech companies in their efforts to engage customers with superior service and experiences. The prevailing models for bank customer acquisition and service delivery are beset by missed cues: incumbents often fail to recognize and decipher the signals customers leave behind in their digital journeys.
Across sectors, however, leaders in delivering positive experiences are not just making their journeys easy to access and use but also personalizing core journeys to match an individual’s present context, direction of movement, and aspiration.
Creating a superior experience can generate significant value. A McKinsey survey of US retail banking customers found that at the banks with the highest degree of reported customer satisfaction, deposits grew 84 percent faster than at the banks with the lowest satisfaction ratings (Exhibit 1).
Superior experiences are not only a proven foundation for growth but also a crucial means of countering threats from new attackers. In particular, three trends make it imperative for banks to improve customer engagement:
1. Rising customer expectations.
Accustomed to the service standards set by consumer internet companies, today’s customers have come to expect the same degree of consistency, convenience, and personalization from their financial-services institutions. For example, Netflix has been able to raise the bar in customer experience by doing well on three crucial attributes: consistency of experience across channels (mobile app, laptop, TV), convenient access to a vast reserve of content with a single click, and recommendations finely tailored to each profile within a single account. Improving websites and online portals for a seamless experience is one of the top three areas where customers desire support from banks.2 Innovation leaders are already executing transactions and loan approvals and resolving service inquiries in near real time.
Nonbank providers are disintermediating banks from the most valuable services, leaving less profitable links in the value chain to traditional banks. Big-tech companies are providing access to financial products within their nonbanking ecosystems. Messaging app WeChat allows users in China to make a payment within the chat window. Google has partnered with eight US banks to offer cobranded accounts that will be mobile first and focus on creating an intuitive user experience and new ways to manage money with financial insights and budgeting tools.3
Beyond access, nonbank innovators are also disintermediating parts of the value chain that were once considered core capabilities of financial institutions, including underwriting. Indian agtech company Cropin uses advanced analytics and machine learning to analyze historical data on crop performance, weather patterns, land usage, and more to develop underwriting models that predict a customer’s creditworthiness much more accurately than traditional risk models.
3. Increasingly human-like formats.
Conversational interfaces are becoming the new standard for customer engagement. With approximately one third of adult Americans owning a smart speaker,4 voice commands are gaining traction, and adoption of both voice and video interfaces will likely expand as in-person interactions continue to decline. Several banks have already launched voice-activated assistants, including Bank of America with Erica and ICICI bank in India with iPal.
If reimagined customer engagement is properly aligned with the other layers of the AI-and-analytics capability stack, it can strengthen a bank’s competitive position and financial performance by increasing efficiency, access and scale, and customer lifetime value (Exhibit 2).
Key elements of the AI-first engagement layer
For banks, successfully integrating core personalization elements across the range of touchpoints with customers will be critical to deliver a superior experience and better outcomes. The reimagined engagement layer should provide the AI bank with a deeper and more accurate understanding of each customer’s context, behavior, needs, and preferences. This understanding, in turn, enables the bank to craft an intelligent, personalized offering. To support this, banks need to analyze customer data in real time and embed analytical outcomes within customer journeys for fast execution of customer transaction requests and service queries, enabling instant fulfilment. These two objectives should guide the design of the engagement layer, which comprises three pillars: Intelligent propositions, seamless embedding within partner ecosystems, and smart service and experiences (Exhibit 3).
To craft and deliver intelligent propositions, banks must take an entirely new approach to innovation. First and foremost, they need to free themselves from a product-centric view, where they develop new products and features and “push” them to customers through product bundles and discounted pricing. Instead, they should adopt a customer-centric view, which starts with understanding customer needs. Achieving this close alignment between bank capabilities and customer needs requires time and capital to develop a realistic, evidence-based understanding of actual customers’ time-critical needs. The capability to gauge customers’ expressed needs and anticipate latent needs in real time requires that AI and analytics capabilities be integrated with diverse core systems and delivery platforms across the enterprise.
Customer propositions can no longer be static and one-size-fits-all—they should be intelligent and tailored, and go beyond banking to address customer needs that may involve both banking and non-banking products and services.
Across diverse markets, recent innovations in messaging and financial-management tools are already helping customers simplify banking activities and improve their financial position—for example, with fee-reduction recommendations, budgeting tools, savings and liquidity management, and planning tools to help customers achieve their life goals.
- Fee reduction recommendations. Rapid analysis of transaction history enables banks to inform individual customers about their potential to reduce fees. The mobile app Empower highlights duplicate services and high bills and suggests possible actions, such as reducing the number of subscriptions or negotiating for more competitive mobile-phone fees, and recommends options for reducing bank fees. (E.g., “You can potentially reduce your telephone bill by 30 percent. We can negotiate with your service provider on your behalf and get you a better plan.”)
- Budgeting tools. Budgeting tools can help customers improve financial discipline. Acorns, for example, allows people to set budgets and sends them alerts to help them stay on track (“You have spent 75 percent of your dining limit this week”). It also delivers reminders based on past transactions (“You paid your credit card bill on the 10th last month. Would you like to pay now?”). Wally and Spendee automatically allocate expenses to different categories and show the proportion of monthly expense in a particular category (e.g., dining out or fuel) in comparison with the previous month’s spending.
- Planning for life goals. Finally, by integrating systems across the enterprise, banks can analyze relevant data to generate a comprehensive view of a customer’s total inflows and outflows and offer advice for balancing daily and annual spending with wealth-building goals. Wealthfront, a digital wealth-management tool, proposes an investment plan to customers based on their answers to a few questions. The process allows customers to define their goals in practical terms, such as learning how much to invest to buy a home in five years, take a year off to travel next year, or retire at 40. Chinese wealth-management fintech Snowball offers a cross-platform app with a Twitter-like feature that enables investors to exchange investment ideas.
- Debt simplification. Some fintech companies are helping customers who grapple with the challenge of managing multiple credit cards. For example, Fintech Tally helps solve a number of pain points, and decisions such as which card to pay first (based on a forecast of their monthly income and expenses), when to pay, and how much to pay (minimum balance vs. retiring principal), while optimizing their credit scores.
Embedding in partner ecosystems
As banks design and offer intelligent propositions they need to make them accessible not only on their own platforms but also in other ecosystems that their customers are part of. McKinsey research has identified 12 distinct ecosystems that have begun to form around end-to-end customer needs within distinct service domains. We estimate that these integrated networks will generate approximately $60 trillion in global annual revenues by 2025.5
Just a few years ago, the most prominent examples were tech giants such as Alibaba, Baidu, and WeChat in China, and Amazon, Facebook, and Google in the United States. In the past two years, however, both traditional companies and tech startups have contributed to significant expansion of ecosystem activity globally. Well-established banks have led the formation of digital ecosystems, often in one of five areas: B2C commerce, housing, B2B services, transportation, and wealth and protection. Examples include RBC’s Ownr, a digital solution for entrepreneurs launching a business, and DBS’s digital marketplace for automobiles, electricity, housing, and travel.
Ecosystem strategies. Financial institutions can leverage their own and/or partner ecosystems to create value in diverse ways, including increased access, higher efficiencies, and stronger offerings:
- Increased access and scale. By embedding their services within ecosystems, banks have the potential to access customer segments beyond their traditional footprint and to scale new solutions rapidly. For example, BBVA’s Valora, a real estate and mortgage advisory platform, is an important channel for customer acquisition.
- Higher efficiencies. Participation in one or several ecosystems typically leads to lower customer acquisition costs, lower cost to serve, and better credit risk management. In China, for example, co-lending ecosystem partners rely on advanced diagnostic models to analyze ecosystem data to monitor potential changes in borrowers’ risk profiles and to manage early-stage collection in case of default.
- New value propositions. Deniz Bank has launched Deniz Den, a platform for agricultural consulting and financial services, supporting farmers with timely information about agricultural best practices and advice on small-business finance and investments.
- More convenience. In India, SBI has launched YONO, designed as a one-stop solution to meet a broad range of a retail customers’ banking and nonbanking needs. It has more than 100 merchants embedded in the online marketplace, enabling customers to complete diverse tasks, such as ordering groceries and booking tickets, through a single app.
How to move forward. The gradual shift of commercial activity toward digital ecosystems has far-reaching implications for practically every sector of the economy, and each financial-services organization should build a detailed strategy for competing in these new contexts.6 At present, however, only a few banks have successfully tapped the potential of ecosystems to create value. To avoid common pitfalls and maximize the value of their ecosystem partnerships, banks need a clear ecosystem strategy, end-to-end integration of internal capabilities, and ways of working that are compatible with technology partners’ methods.
Banks need a clear understanding of their strengths, local context, and current customers, which they should use to select an ecosystem strategy that fits the organization’s ambition and market position. These are top priorities for the board and should not be left entirely to the chief digital officer.
End-to-end integration of internal capabilities is necessary to support real-time analytics and messaging. From the collection and processing of customer data to accurate customer-profile analysis, banks must upgrade their technology architecture and analytical capabilities. Further, as discussed in the following section, they should establish a consolidated, enterprise-wide platform for managing customer data. They should also establish robust links with partner ecosystems to support instantaneous data exchange.
Organizational culture and processes also matter. The bank should work in a way that matches the way technology partners work. This typically entails changes in organizational mindset and culture. One approach is to organize a team of top talent from multiple departments that speak the language of the tech partners, work at a compatible speed, and are empowered to make and implement decisions swiftly. Another key area is performance measurement. Traditionally, a bank’s key performance indicators (KPIs) focus on growth and profitability. The core KPI for internet companies, by contrast, is user experience. If partners are not aligned in evaluating progress toward agreed-upon goals, tension can arise and diminish the impact of the collaboration.
Smart servicing and experiences
The third pillar of the reimagined engagement layer is smart servicing facilitated by fast, simple, and intuitive interactions with customers. Banks that leverage AI and analytics to deliver smart servicing and superior experiences stand to increase customer satisfaction and loyalty. Research shows that the stronger the experience and the more satisfied the customer, the more likely it is that the bank will generate higher revenue: a more satisfied customer typically accounts for approximately 2.4 times more revenue than a neutral customer.7 What is more, we have seen that companies scoring high on a scale of customer satisfaction tend to generate higher total shareholder returns than lower-scoring companies do (Exhibit 4).
Along with the significant impact of customers’ overall experience, customers’ expectations also influence their level of satisfaction—and, by extension, may affect the company’s value. Given the rising trend in customers’ expectations for online, offline, and hybrid journeys, disruptive companies in diverse markets are creating customer-centric interactions and journeys that are fast, simple, and intuitive. Guided by a relentless commitment to customer satisfaction, Amazon has achieved a high level of customer loyalty through value, convenience, and reliability in online shopping. Uber has set a high bar for speed, safety, and amicable service supported by frictionless end-to-end customer journeys. Netflix has created a highly differentiated experience by analyzing the viewing choices of hundreds of millions of subscribers to create highly personalized recommendations from its stock of diverse content.
The challenge for banks is to examine each crucial element in the design of differentiating customer experiences. First among these is the ability to open a service request on the device of choice anytime, anywhere. Second, each interaction should build on previous history and continue without interruption or repeated steps when the customer shifts from one device to another. The service interface should also be capable of recognizing the customer’s context and adjust messaging accordingly. A third crucial element is speed: For example, a customer requesting a higher credit limit through a chatbot should receive a response within seconds, supported by real-time analysis of the customer’s risk profile. If the request cannot be met at once, the time frame for fulfilling the request should be stated clearly.
Fourth, chatbots, voice assistants, and live video consultations make it possible to dispense with long, detailed forms and questionnaires. Insurance provider Lemonade offers a chatbased application form that follows a carefully designed conversation to generate an insurance quote. Likewise, self-serve journeys can offer prompt access to assistance through chatbots, with the ability to shift instantaneously and seamlessly to a live video chat with a service representative or adviser as soon as the request exceeds machine capabilities.
Finally, it is crucial to personalize journeys in just the right way. For example, customers appreciate recommendations that they would not have thought of themselves. They often do not want more examples of what they have already bought. They need to be given the recommendations at the right time, when they are in “shopping mode.” For example, sending a customer a reminder for repeating an order for flowers based on a purchase made on a special date last year, like an anniversary, may work very well. At the same time, organizations must be careful not to be “creepy” and offer instead recommendations that are highly relevant without crossing lines.8
Reimagined engagement requires integrated capabilities
To successfully design and implement their engagement layer to become AI-first, banks need to develop five capabilities:
1. Adopt a holistic, data-driven approach to understanding how customers engage with the bank.
Best-in-class players achieve this in three major steps:
- Implement a real-time, enterprise-wide data infrastructure that captures virtually all data points for a given customer’s relationship with the bank’s various divisions and supports a unified customer view encompassing all channels, journeys, and products. (The traditional siloed analyses undertaken by any one of various teams have little relevance in an AI-first organization.)
- Consolidate data on a central platform: To ensure that these enterprise data sets are utilized effectively and widely across teams, AI-first banks aggregate the data captured from multiple internal and external sources into a central customer data platform.
- Automate governance and controls to ensure business-and-technology teams have ready access to appropriate data sets, with the necessary controls for security and permission where needed. It is also important to ensure that the appropriate data are available for decisioning, at the right time and in the right form, to the various AA/ ML models used by internal teams (from customer service to product management) to support intelligent, highly personalized interactions with customers.
2. Embed next-generation talent within traditional teams.
Creating superior customer experiences in the digital era requires a new set of skills and capabilities centered on design, data science, and product management. An individual product manager, for example, may focus primarily on technical solutions, customer experiences, or maximizing business performance, but in an AI-first environment, all product managers will need a foundation in diverse areas, including customer experience, advanced analytics and machine learning, market analysis, business strategy, as well as leadership and capability development.9 Design leaders require a similar foundation as well as deep expertise in extracting user insights to guide business strategy and innovation.10 The data, analytics, and AI skills required to build an AI-bank are foreign to most traditional financial services institutions, and organizations should craft a detailed strategy for attracting them. This plan should define which capabilities can and should be developed in-house (to ensure competitive distinction) and which can be acquired through partnerships with technology specialists.
Furthermore, our experience suggests that it’s not enough to staff the teams with new talent. What really differentiates experience leaders is how they integrate new talent in traditional team structures and unlock the full potential of these capabilities, in the context of business problems. Several organizations have built an internal talent pool of data scientists and engineers. However, most treat data as an operational function and leverage data-and-analytics talent primarily to generate and automate reports required by traditional business teams. A few leaders treat data management as a strategic function, and embed data scientists/engineers within agile product and customer service teams, each focused on a discrete journey or use case, such as small business lending, home financing, or digital wealth advisory for the mass affluent. These organizations have been recognized as leaders in creating superior experiences that give them a competitive edge, measured in customer satisfaction and value creation.
3. Institute formal top-down mechanisms to support coordination across traditional product and channel silos.
While financial services institutions take various measures to align working teams with groups focused on serving a specific customer segment, these measures typically take a long time to yield results (and often fail). The product and channel silos through which banks have traditionally sought to address the needs of diverse market segments can be very complex, and this complexity makes it difficult to break out of the product-centric mindset and assume a genuinely customer-centric view throughout the organization.
In our experience, bottom-up efforts to organize teams around customer segments often fall short of expectations if they are not complemented by a top-down approach consisting of cross-department senior management teams. While these teams are empowered to act (that is, they have resources and budgets, along with autonomy in deciding how to deploy these to meet strategic goals), they also take an integrated view of various siloed efforts across the organization and prioritize a limited number of high-impact cross-cutting initiatives that require central coordination (as opposed to spreading the organization’s resources thin on several smaller initiatives). Finally, they develop and track progress against a coordinated plan executed through the traditional team structure.
4. Institutionalized capabilities to strike new partnerships at-scale with a heterogenous set of non-financial services institutions.
Partnerships are becoming increasingly critical for financial services players to extend their boundaries beyond traditional channels, acquire more customers, and create deeper engagement. Most institutions understand the importance of having a clear strategic rationale (including a “win-win” value creation thesis for partners), and a strong governance model to oversee the partnership. It is also important to establish teams responsible both for setting up partnerships and for adapting the technology infrastructure to support the efficient and speedy launch of the partnership.
- Setting-up dedicated teams that are focused on establishing partnerships. These teams constantly scan the market for potential partners and assess their relevance to the institution’s growth strategy. They engage effectively with a broad range of non-bank partners—beginning with a review of differences in culture and technology—and gauge the flexibility required to align with the partners’ ways of working (e.g., profile and seniority of people participating in discussions, decision-making styles, responsiveness to requests, adherence to timelines) to enable faster, smoother, and more productive collaboration.
- Making the technology infrastructure partnership-friendly hinges to a significant degree on API contracts identifying the functionalities that must be developed to meet the partner’s requirements. Another crucial step is altering the technology infrastructure to facilitate fast integration with partner capabilities. This includes creating sand-box environments to enable rapid experimentation and proof-of-concept trials, as well as modern data-sharing and storage options compatible with the partner’s data-stack.
5. Deep integration with the remaining layers of the AI bank—that is, the AI-enabled decisioning layer and the core-tech and data layer.
The journey to become an AI bank entails transforming capabilities across all four layers of the capability stack: engagement, AI-powered decisioning, core technology and data infrastructure, and operating model. The layers should work in unison, and investment in each layer should be made in tandem with the others. Underinvesting in any layer will create a ripple effect that hinders the ability of the stack as a whole to deliver enterprise goals.
As traditional banks observe the rapid advancement of AI technologies and the success of digital innovators in creating compelling customer experiences, many recognize the need to reimagine how they engage their customers. By adopting an AI-first approach in their vision and planning, innovative banks are building the capabilities that will enable them not just to deliver intelligent services but also to design intuitive, highly personalized journeys spanning diverse ecosystems, from banking to housing to retail commerce, B2B services, and more. To realize this vision requires new talent, a robust mechanism for managing partnerships, and a progressive transformation of the capability stack. Throughout this expansive undertaking, leaders must stay attuned to customer perspectives and be clear about how the AI bank will create value for each customer.