Beyond Digital Transformation Frontiers: AI Banking

Dr Dennis Magaya, [email protected] , April 2024

Digital Transformation maturity has reached levels adequate to provide a prerequisite foundation for AI implementation in several markets across the world. Digital transformation refers to the profound changes that happen to people, operations, products, services, systems and processes as a result of the application of digital technologies and data. It disrupts the organizations’ end-game, goals, business model, operating model and financial sustainability. A business model is how value is created and delivered while the operating model describes what work is done and by who. Being digital is not about technology but a way of life, a culture and an approach to do business. As such, Digital Transformation is broader and deeper than IT-enabled change.

Although digital transformation doesn’t change the fundamentals of commercial banking, it lays a new foundation for how banking, customer experience, services, assets, income and net come are delivered. A key question to ask is what kind of bank will emerge beyond the digital transformation journey. As a minimum, such a bank should deliver hyper-personalised services on real-time basis and at-scale in a rapidly changing digital environment. It should comprise of systems and processes that are intelligent and use data to learn and to make decisions with minimal human intervention. Several technologies can deliver this end-game, but Artificial Intelligence (AI) is a dominant option. AI refers to computer systems that mimic the general intelligence found in human beings. AI is the “killer” technology that propels banking to horizons not envisioned before. This article presents an AI-powered bank that could emerge beyond the frontiers set by digital transformation.

The six areas for digital transformation are business model, customer experience, products and services, people, technology and processes. The overarching transformation requirement is delivery of business models appropriate for a given time and space. AI is increasing the integration of financial services into people’s daily lives in unprecedented ways. As such, banks can develop multi-faceted and adaptive business models that create new value for different markets.

Digital-only banks generally adopt completely AI-powered non-linear business models while incumbents selectively apply AI to their vertically integrated businesses components to defend positions in core markets and use non-linear models in new markets. Incumbent banks transformation roadmaps optimise value generation from pragmatic execution of change-the-bank and run-the-bank strategies. Banks’ business models differ depending on vision, markets and how they choose to deploy their balance sheet. Mid-sized banks have higher cost-to-asset ratios than the larger banks, hence their business models are biased towards using their unique capabilities to reduce costs, improve efficiency and ultimately grow their balance sheet. Whichever business model is adopted, AI enables banks to gain speed and agility to play a range of different roles in the value chain at scale.

The business model directly determines what kind of operating model is required downstream. Digital transformation disrupted one of banking’s long-standing foundations which is its operating model centred around the branch. The operating model beyond digital transformation frontiers uses AI to deliver a bank that is ubiquitous in customers’ lives. As consumers use banking services, they compare their experience with the world-class standards they get from experience leaders globally. Experience leaders use AI to predict customer needs and offer tailored services using the right channel and price. This raises the service individualisation bar to frontiers beyond basic digital transformation.

Historically, customers’ engagement with the bank was two branch visits per month, with websites and nowadays customers have daily interactions using AI -powered chatbots and virtual assistance. The relationship between the bank and the customer is now digitally glued. Surveys in USA show that that 40% of customers have an account with a digital only bank and 36% check their banking app at least once daily. About 67% said they would use an AI-assisted system to manage their bank account.

The branch has moved to the mobile device, the customer is now branch manager and AI is becoming the co-branch manager. All services available in the branch are also on digital channels and the call centre except for cash withdrawals. Digital transformation collapsed siloed multiple bank channels through implementation of omni-channel journeys across the physical (branch), virtual (call centre) and digital (mobile App, USSD, WhatsApp). AI takes the current omni-channel customers journeys to new frontiers of individualised service orchestration at-scale. Banking services such as payments can start and end on touchpoints outside the bank’s proprietary perimeter.

The future bank’s value proposition is underpinned by a world-class digital products and services. These are products where the creation, delivery and consumption cannot happen outside of a digital technology. AI goes a step further by creating intelligent products and services which use data and autonomous learning to adjust parameters without human intervention. Imagine an App that uses AI to anticipate your bank transfers every day, adjusts the menu accordingly and recommends the lowest cost option. If a transaction to buy airtime fails due to insufficient funds, the process has intelligence to activate micro-credit from the bank. When a customer does a balance enquiry, the bank doesn’t just give an amount but uses your expenditure pattern to provide advise how long the amount would last, projected cashflows, including list of due payments.

Some banks are yet to implement digital transformation in pricing and revenue assurance. Products and services price are manually calculated using regulatory, competitive and economic factors. Unit costs of service such as internal transfers or account opening are unknown because margins are calculated at a consolidated financial reporting level. On average a bank has about 500 revenue lines and each has up to three price points which makes price management complex. AI is a game changer for price management and revenue assurance. Large quantities of data can be analysed to extract deep customer, and product insights in near real-time basis for the full customer base. Deep learning and predictive analysis are used to tailor prices to channels, promotions, usage and the required margins. Unit costs per revenue line can be determined which makes it possible to embed bank products and services into ecosystem partners’ journeys. Man-in-the loop AI tools empower staff and governance committees with decision-making capacity for pricing and revenue assurance.

Banking operations require a large number of general ledger (GL) accounts which are labour intensive and costly do reconciliations. As non-linear business models and complex operating models are introduced, the GL accounts management require digitisation and intelligent automation. The future bank will use Robotics Process Automation (RPA) and AI to address reconciliations, reversals or resolutions which are a costly and a risky pain point.

Deposit mobilisation is key performance matter for banks which is managed through increased customer acquisition, better client relationships and offering compelling value propositions. Digital transformation to improve deposit mobilisation isn’t advanced yet apart from digitisation of channels to automate account opening and bank transfers, corporate integration and Point of Sale (POS). AI solutions that analyse segments, competition and the macro-economy can identify potential depositors, and design offerings to entice them. Banks can use internal and external data sets to develop tailored marketing campaigns with incentives to increase core deposits and minimise transitory deposits. The bank deposits can be linked to treasury operations using AI for seamless operations that maximise bank profits without compromising clients demand for withdrawals.

Marketing is an advanced area of AI application in most banks and there are lots of open source AI tools available on the internet. AI can analyse customer behaviour, preferences and demographics to produce hyper-personalized content at-scale. Customers are supported by contextual chatbot which follows their journeys providing immediate answers to their questions. When a new customer joins, AI can provide a personalised welcome and training content. In fact, AI can provide continuous support from lead identification to deal closure.

AI enables banks to lower operational, regulatory, and compliance costs while simultaneously providing accurate credit decision making capabilities. Compliance and regulatory reporting use intelligent automation and AI to improve accuracy and reduce costs. Reports are available on real-time basis while summaries and recommendations can be extracted. Auditing in banking is systematic examination of financial records, transactions, internal controls and operational processes to assess accuracy and adherence to regulatory standards. As the complexity and data volume grows, traditional auditing practices are insufficient. AI performs journal entry testing by identifying unusual transactions among a large pool of unstructured data and analysing those transactions for patterns and anomalies. AI can process large amounts of data, read bank statements, and legal contracts and reconcile accounts faster than a human auditor.

Data constitute the bank’s fundamental raw material today. In fact, without data, the bank isn’t future-fit. The future bank requires data liquidity as the foundation for advanced analytics, insights and decisions. Data liquidity is the ability to access internal and external sources such as ecosystem partners and the cloud, to ingest data into an enterprise-wide data lake, clean and label the data for AI-powered use cases in real-time basis. All humans and AI robots in the bank should operate at a single version of truth.

The increasing demands to support complex service use cases, analytics and real-time insights impact the overall technology function. In fact, deploying AI capabilities across the organization requires intelligent, flexible and scalable set of core-technology components. With the rapid increase of customer engagement across both bank and non-bank platforms, deploying hyper-scalable infrastructure to process high-volume transactions in milliseconds is crucial.

Innovation is arguably the biggest AI beneficiary in a bank. The bank can deliver rapid innovations of AI driven business models, operating models, products, processes which were deemed impossible in recent years. Beyond digital transformation frontiers, innovation is not a building with hi-tech computers or just a tech-savvy product development team. Innovation is a virtual capability comprising data, technology, APIs, staff and interconnected ecosystems which delivers cutting-edge stuff.

Dr Dennis Magaya is the CEO and Founder of rubiem, a Digital Solutions firm. He is reachable on [email protected]

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