The retail banking and payments industry in Asia-Pacific is undergoing a significant transformation driven by the demands of mobile-oriented consumers and new technologies.
Here we explore three related trends that pose tricky questions for bank executives but could also unlock incredible business opportunities: modernizing bank core systems in the right way; responding to the fast-evolving demands of convenience-seeking consumers; and surefootedly deploying the power of GenAI.
For many banks, an imperative through 2024 and beyond will be modernizing core systems to meet next-generation requirements, at a time when bank capital expenditure is under pressure and the appetite to make risky ‘big bang’ core transformations remains low.
The need for core transformations is pressing. Legacy cores cannot keep up with banks’ need to access and merge data in ways that speed up and personalize customer services and support rapid product innovation. Banks are also aware they need infrastructures that can promote the roll out of GenAI, Web 3.0, Open Finance, central bank digital currencies, and a host of other competitive opportunities that are on their way – or already banging on the door.
The problem arises because today’s legacy cores are being used for purposes for which they were not originally designed, with banks having bolted on various functions over the years. These functions can now be better serviced through a more layered, component-based approach that makes use of API strategies, microservices, and the cloud to deliver less monolithic, better-connected architectures.
The answer for many banks is not wholesale replacement of the legacy core, but progressively transforming how the core is used. This means reversing the historic trend to build ever more functions into the core, and instead beginning to abstract key functions away from the legacy system of record.
Modern technologies and the cloud can then be used to build customer-facing processes outside a simplified core, with transactions written back to the core to preserve the master record. But achieving this while maximizing customer benefits and meeting strategic considerations involves careful prioritization and banks may need to use 2024 to:
For a smaller subset of banks, 2024 may provoke an even more radical rethink. This will involve considering partnership models with technology and serviced-based organizations to accelerate the modernization and transformation of core platforms, obviating the need for bank-managed core transformations and large capital expenditures.
In these models, both the funding of technology modernization and the implementation risk is largely lifted away from the banking institution and borne instead by the broader partnership. The new partner provides future-ready infrastructure and ongoing technology skills while the bank provides its business models, customer base, balance sheet and regulatory relationships.
Deciding to re-engineer the bank’s business model rather than its core systems is not a trivial undertaking. However, in addition to system modernization, the partnership model could help banks refocus on their unique skillset and overcome the perennial problem of how to hire the best technology talent at a competitive market rate.
For a while now, retail banks have invested heavily in improving the range and quality of their customers’ ‘omnichannel experience’. They have worked hard to improve banking apps and to allow customers to pick their preferred channel of communication with the bank – in-person or digital – for each banking activity and then switch between these channels at will.
Investing in the omnichannel experience continues to make sense. However, in APAC the mobile now lies firmly at the center of that experience and, increasingly, convenience-seeking customers want to achieve daily tasks and lifestyle goals without opening up separate apps or communication channels with the bank – however well these are designed.
The Asia-Pacific region is ahead of other global regions in this tilt towards ultimate digital convenience. Many Covid-accelerated behaviors that promote remote interaction, including the wearing of masks and a preference for touch-free transactions, remain firmly in place. Meanwhile, across Asia, QR code instant payments via mobile have quickly become mainstream, and national identity schemes are removing authentication and onboarding barriers to seamless customer journeys.
We can expect that through 2024 more financial services including insurance and credit will be embedded into social channels, non-financial consumer product and service offers, and super apps – with the aim of further simplifying and speeding up the customer experience.
The step change towards cashless lifestyles and seamless in-app digital journeys has implications for bank business models. The most obvious is the need to change the shape of physical footprints as the shift away from cash reduces the need for branches and transactional bank staff.
This means closing some branches, finding new uses for others, and redeploying staff towards activities that add the most in-person customer value such as financial planning and wealth management. Successful redeployments will themselves leverage digitalization, for example, through supplying staff with suites of AI-enabled digital tools that support a more human, personalized approach while reducing the time spent on information-gathering and form filling.
Meanwhile, banks’ role in payments and other transactional services seems likely to shift in the other direction, as banks move further towards becoming an invisible, omnipresent enabler of customer experiences rather than a visible, branded owner of the customer relationship. Becoming enablers rather than relationship owners will, however, require a conscious effort to adapt.
Banks need to become more actively involved in designing embedded banking services. So far, they have tended to create and make available large volumes of functional API services at a relatively technical level. API strategies now need to offer retailers and other non-financial partners more ‘experiential’ service bundles to help embed financial services and ease specific customer tasks.
For instance, banks can begin to group and layer APIs so that they more easily support the international travel experience by offering embedded balance checks at the point of airline ticket purchase, instant transfers from other accounts to fund the purchase, options such as BNPL, and context-specific offers such as usage-based travel insurance.
As customers fulfil more tasks without directly interacting with their bank, the banking industry must develop a new set of skills to remain relevant – and turn the strategic threat from instant payments and embedded finance into a competitive and transformational opportunity.
All over the Asia-Pacific region, banks are developing GenAI initiatives in response to the huge potential benefits of the new technology and the excitement that accompanied ChatGPT’s launch in late 2022. Use cases are emerging across bank activities including speeding up and automating back-office processes; generating rapid business insights; wrangling large data sets such as transaction reconciliation; and customer communications from real-time responses to marketing and sales initiatives.
However, some GenAI projects have now been running for 12 months and internal sponsors are impatient about when they might see a positive return on their investment. Bank technology groups are often responding that they need more time to develop use cases and deploy GenAI safely and effectively in ways that can answer key risk and governance issues. These include GenAI’s tendency to ‘hallucinate’ false answers and misinformation in addition to data privacy, security, bias and transparency concerns.
Risk management and governance frameworks continue to evolve to help banks manage these risks1. However, banks should consider two key dimensions when refining how they select use cases and deploy GenAI:
Bank infrastructure: GenAI arrived looking like the development work had already been done, leaving banks with the task of pointing it in the right direction. However, game-changing general-purpose technologies always require infrastructure to support local applications and GenAI is no exception, particularly regarding data. To make the most of GenAI, banks must consider using their own curated datasets to allay concerns around training data, address bottlenecks in their legacy systems and technology architectures, upgrade data management and data flows, and put in place the right technology and data talent. Unlike other industries racing to deploy GenAI, banking is highly regulated, so banks must build out this infrastructure in ways that allow them to track and comply with fast-emerging data and AI/GenAI regulations.
Generative versus ‘traditional’ AI: Some business goals within areas such as anti-money laundering (AML) and know your customer (KYC) routines may be better approached through longer-established AI technologies, often labeled ‘deterministic’ or ‘predictive’. These are designed to accomplish more narrowly defined goals than GenAI and are bounded more closely by predefined statistical models, rules and algorithms. Some of the models have special strengths in the banking environment in terms of how easy it is to incorporate human professional expertise into the model and the avoidance of inaccurate results.
With all kinds of AI models advancing fast in terms of their capabilities and power, choosing the right model has become a critical task for banks. This choice depends on a range of factors such as the job in hand, the degree of transparency required, the nature of the data available, and the practicality of keeping a ‘human in the loop’ – alongside how prepared the bank and its data environment are to support the new AI technologies.
Gen AI will play a critical role in shaping the banking landscape through 2024. However, often the best approach may prove to be a hybrid solution that combines the predictive strength of more established approaches with the newly established ‘human-like’ communicative and creative powers of GenAI.
References
1 For a detailed discussion, see Gerhardt Scriven et al., Applied Generative AI Governance, The Capco Institute Journal of Financial Transformation, pp. 24-33, November 2023. Link.
