The AI-Digital Bank Business Model
Today’s customers are empowered with real-time information, and they want the best of everything, everywhere and at any time. When they use banking services, they compare their experience with world-class standards delivered by service leaders. Companies that deliver intuitive experiences continuously reimagine how value is created and delivered. A business model describes how an organisation creates and delivers value to its customers and stakeholders.
In the past when the world changed slowly, business models remained static for decades. However, nowadays companies are compelled to continuously future-proof their business models to remain relevant. Factors such as customer needs, competition, technology and regulation are driving change. Artificial Intelligence (AI) technology has emerged as a dominant disruptive factor. AI refers to computer systems that mimic general intelligence found in human beings. The business model of a purpose-led bank is more than an approach to making money, but a construct to improve human life. Such a bank can therefore leverage AI to be ubiquitous in people’s lives. A bank that embraces AI for enterprise-wide use can be referred to as AI-Digital bank and this article presents the envisioned business model.
Global trends show that new digital players are fragmenting legacy and vertically integrated banking value chain to cherry-pick profitable parts for their operations. They unbundle traditional products into micro-offerings and using AI, they package internally and externally produced components into innovative value propositions. Incumbents should therefore design fit-for-context AI-Digital bank business models to defend vulnerable areas. The required features are customer centricity, agility to create value from diverse offerings, individualisation at-scale and a wide customer reach at a low cost.
A three-step process can be used to design the AI-Bank Business Model. The first step is defining the value creation strategy using the vision, purpose and goals to identify strategic opportunities for viable capital investment and sustainable growth. Then, determine how differentiation and are created at scale in segments such as Business to Consumer (B2C), Business to Business (B2B) and Business to Business to Consumer (B2B2C). Lastly, evaluate how critical success factors such as bank and non-bank ecosystems, balance sheet strength, regulations and risk appetite impact value creation.
The second step is to figure out how value will be delivered by delayering the value chain to its minimum viable parts and selecting the roles the bank is best placed to play. Define agility principles to enable playing different roles in the value chain where unique value propositions are offered. Develop a process that breaks down composite products and services to determine what to produce internally or source from best partners and what can be sold to other banks or non-banks. The channel options to distribute products and services should be identified. The third step is checking if the proposed value creation and delivery approach is aligned with the envisioned operating model. An operating model is the implementation in terms of what, when, how work is done and by whom. Check how the culture, leadership, technology and strategic capabilities required to support the business model. The business model impact on the value map indicators such as income, profitability and assets should be assessed. Lastly, do an acid test on how value is created and delivered in the high-end and low-end segments.
Low-end consumers, small businesses and the underbanked are a lucrative market for banks. However, these segments are prices sensitive and require innovation to create and deliver value at low cost to protect margins. Retaining theses customers is a challenge because they are susceptible to cherry-picking by competitors with new propositions. AI can be used to create hyper-personalised micro-lending and micro-savings bundles which are delivered using low cost digital channels at-scale.
Banks use various business models for high net-worth customers whose need is sustainable wealth growth. However, if the business model is product driven, the clients are offered superior customised products such as credit cards, loans which are not necessarily wealth growth drivers. Similarity, banks integration to corporates is products centric instead of value creation because it just allows corporates to make payments. The AI-Digital Bank Business model allows value co-creation and seeing business from the client’s perspective. The bank can be embedded in the corporate’s ecosystem to play different roles in the non-linear value chains. Imagine a bank whose target is to double and triple client’s income and assets respectively such that products like credit cards become a means to an objective.
The AI-Bank Business Model can withstand untraditional competitors. For instance, technology companies that entered financial services market (Techfins) as the next adjacency to their core business and have been successful in payments, lending and insurance eg Amazon. Their strength are a huge global customer base and data assets which provide detailed insights on near-real time basis. The AI-Bank Business model is at par with Techfins by using data and technology to build market advantage. The financial services firms that use technology to create new businesses (Fintech) 2030 annual revenue forecast is $1.5 trillion which will be 25% of global banking value. They generally plug into the bank’s value chain as enablers or select services to deliver cost effectively. Their strategy is coopetition and operate under the regulatory radar until they achieve meaningful scale. The AI-Bank Business Model allows the bank to either work with an existing Fintech or to establish its own.
Generic business models include digital bank, Banking as a service (BaaS), a market place and embedded finance. While a greenfield bank has the liberty of choice for implementation, incumbents transform what they have using various approaches. The first approach is a gradual evolution into the best possible digital version of a vertically integrated institution. Banks still driving profitability from vertically integrated business models may not transform and prefer to sweat their balance sheet to enter new markets and to defend their established businesses. However, the continued revenue shrinkage and value dilution due to disruption bring sustainability challenges.
The second approach is transformation of the current configuration to an AI-Digital Bank model. This makes it is easy to align the business model to existing vision and purpose. The changes required to the downstream operations are also implemented. Achieving the target business model is quick and running costs are relatively low. However, this requires robust change management because it entails big changes to business while simultaneously running operations.
The third approach is to maintain vertical business model and build a new entity which is 100% AI-Digital Bank. The new adaptive and non-linear model co-exists alongside the traditional vertically integrated model which will eventually disappear. When opportunities arise from value chain delayering and product fragmentation the new entity which has a competitive advantage is used. However, the high cost of running two models is disadvantageous.
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Business model implementation challenges involve data, technology and human resources. A bank can take the easier but disastrous route of force-fitting a new model into the existing core banking system instead of investing in the requisite technology and data. Evaluating technology from a business model perspective rather than products solves this issue. A new business model can suffer a stillbirth if it’s shrink-wrapped into an existing organisational structure where value creation and delivery carry different meanings. Appropriate skills, experience and organisation structure should be provided.
Banks should include a business model continuous transformation a strategic objective in the board, executive and management structures. Performance score cards should designed for multi-faceted and adaptive business models and ever-changing customer needs.
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