AI-Digital Bank Operating Model
Dr Dennis Magaya, ?[email protected] , March 2024
The future bank will be ubiquitous in human lives and continuously re-imagines how to engage with customers so that it remains relevant in a world of disruptive change. Meanwhile, firms that are pioneers in digital experience are consistently raising the service individualization bar to levels where they accurately predict customer needs. This enables them to offer tailored services at the precise time, and using the right channel at-scale. In addition, the traditional customer journeys to discover evaluate, and purchase goods and services have been disrupted such that some vertically integrated companies like banks become disintermediated.
Incumbent banks are exposed to services, and revenue migration to new entrants such as Techfins, Fintechs and non-banks that are digital. Banking services such as payments and lending are becoming invisible, as since they integrated in customer journeys which can begin and end on touchpoints beyond the bank’s proprietary environment. The resultant value attrition depends on the market’s digital maturity level, regulation and consumer profile.
Banks will no longer rely on branch network and lots of staff that cross-and-up sell for growth. Any advantages of such vertically integrated operations won’t match intelligent operations that offer hyper-customised services using a variety of frictionless channels. Artificial Intelligence (AI) coupled with other technologies deliver such an operation. AI are computer systems that mimic general intelligence found in human beings. AI closes the gap between humans and technology which brings to life a world of new possibilities. Banks’ AI adoption to transform operations is now a must-do strategic initiative. A bank with an AI-powered operating model can be referred to as an AI-Digital Bank and its architecture comprise of four layers.
Firstly, the top has the vision, purpose and strategic goals. Secondly, the business model which describes how value is created and delivered. It answers the question, what does the bank do to make money? Thirdly, the strategy which describes how the goals are achieved. Fourthly, the operating model which links strategy with day-to-day work activities and decisions. It describes value creation and delivery implementation in terms of what, when, how work is done and by whom.
Ai-Digital Banks are transforming business models in responses to market, regulatory and technology changes. For instance, non-linear business model which deliver in the Business-to-Consumer (B2C), Business-to-Business (B2B) and Business-to-Business-to-Consumer (B2B2C) markets are being rolled out. Non-linear business models enable the playing of different roles in the value chain and ecosystem. This is a fundamental departure from the traditional vertically integrated business models. Based on the four-layered architecture presented above, these new business models causes the downstream layer, namely the operating model to change.
An outmoded operating model, causes business model misalignment, value attrition and high operational cost. The first step in redesigning an operating model is to review the vision, purpose, business model and the strategy. The second step determines the customer segments, value proposition and the top five end-to-end customers journeys so that customer centricity is achieved. The third step defines the principles that are used to determine operating model building blocks and to implement changes without dislocating business operations. The final step determines the envisioned seven operating model building blocks namely governance, organisation structure, channels, customer, products and services, data and technology.
The company governance and organisation structure should be customer centric instead of the product or regulatory licenses biased. Digital transformation, business model and operating model and AI are new roles for a board committee. The organizational structure includes a Chief Customer Experience Officer responsible for company-wide customer experience. A Chief Data Officer or Chief AI Officer are new addition to the C-Level Executive team since the AI-Digital bank operating model is less about plumbing out-of-the-box technical systems which can be sourced from the cloud or ecosystem as a service. New job titles such as Business Model Designer, Data Scientist, Customer Experience Consultant, AI Architect, Cloud Engineer emerge. AI skills are a requirement for all marketing roles because the data volumes, content hyper-personalisation can’t be done by humans alone.
Digital transformation and AI collapse organisational structure siloed departments to a flat and agile structure made up of functional departments, capabilities and mission teams. A capability is a unit comprising of people, technology and processes designed for a customer need eg onboarding. The branch back-office, administration, compliance, risk, HR, call centre and reconciliations use AI which reduces headcount. In addition, intelligent automation and digital capabilities eliminate role duplications.
The multiple and product biased channels such as branch, mobile app and call centre cause duplicated costs and long time-to-market. These multiple channels are blind to each other, don’t share context data, offer different products and services and when a customer changes a channel in the middle of a service interaction, they restart from scratch. A change to an omni-channel which has razor-sharp focus on customer needs is required.
The Omni-channel allows customers to start a service and move seamlessly across the physical (branch), virtual (Call centre) and Digital (mobile app) channels without restarting from scratch because the three channels share the same technology and database. Sharing of data and context in real-time enables frictionless customer experiences and individualised process orchestration.
The call centre is upgraded to a virtual service centre that offers everything found in a branch except cash dispensing. As such, most branch HR roles and technologies are added to the virtual service centre. All products and services in a branch are delivered on digital touchpoints to reduce costs and maintain a consistent omni-channel experience. The staff is available to support customers navigate the digital touchpoints.
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The AI-Bank operating model uses data and automation to extract value from a wide ecosystem. It seamlessly embeds customer journeys in partner platforms to enable customer engagement at any point-of-use. For instance, the customer journeys for buying airtime or electricity can include banking products like loans and account opening. Banks should participate in super apps which are all-in-one platforms offering diverse non-banking and banking products and services. For example, Tencent and WeChat provide super apps which is a one-stop-shop. An alternative is embedded banking is where non-banking firms with strong distribution reach integrate banking services in their products to create value for customers.
AI enables intelligent processes, services and automation implementation. Intelligent processes have in-built capability to make decisions using data and context without interrogating another system. Intelligent products and services autonomously package components from internal and ecosystem partners such as other banks, Fintechs, Techfins and non-banks in near-real-time depending on customer needs. The time-to-market for products and services is reduced from months to days.
Data is the lifeblood of the digital operating model. As such, data should be captured from multiple internal and external sources into an enterprise data-lake which is supported by deep analytics and visualisation functionality. Big data technology is incorporated for processing of unstructured data such as video, audio and pictures. In fact, a data transformation project is a prerequisite for AI deployment.
The AI functionality can be built as a standalone system, incorporated as a module in an existing system or obtained from the cloud. Robotics process automation is used for routine processes such as reconciliations or data capturing. Symbolic AI is used in structured and predictable environments where expert knowledge and rules are coded into software to automate decision making. Machine learning is used for scenarios that require detailed analysis, decision making and autonomous learning from huge data volumes. Deploying intelligent process, services and AI capabilities that deliver complex use cases require a scalable, resilient and adaptable core-technology components. Cloud computing, microservices architecture and APIs enable open banking and ecosystem integration.
AI Digital Bank operating models increase customer engagement, product usage and sales which increases per-customer income. Digital channels are up to fifteen times cheaper to operate than the physical branch which therefore reduces the cost to income ratio. With the new operating model, banks don’t have to own every capability or product which enables scaling at minimal incremental investment. A sustainable operating model transformation requires the staff performance score cards and financial reporting to change accordingly. For instance, customers open bank accounts and transact on digital platforms for the entire life-time. However, if revenue and profit are allocated to branches because the bank is still pivoted on the branch the business case for the new operating model will be distorted.
#Banking
#Digital
#AI Institute Africa
#AI
#Operating Model