AICorner OfficeExpert Opinion

AI-led Customer-Centricity Reshaping the Transaction Banking Landscape


Yuri Misnik, CTO of First Abu Dhabi Bank and with an enviable banking-plus-technology pedigree with HSBC, National Australia Bank, CBA, Microsoft, and Amazon Web Services, says “Technology is one of the few differentiators banks have.”

There are point technologies that are differentiators at first but eventually, all players adopt and they become the norm – omnichannel UX, cloud, instant payments, virtual accounts, operational account management, and using enhanced ISO20022 remittance information are some.  Then there are the game changers, notably AI and, especially, AI in the context of MACH and contextual banking.

AI-powered contextual banking is based on the dream: “Wouldn’t it be great if your banking systems understood – even anticipated – what the business is trying to achieve?”  That requires a thorough understanding of the persona’s real objectives – and application of AI and ML, where needed, to achieve it.

For example, just in payments, using knowledge of the bank’s products and the client’s situation to offer to cross-sell and up-selling opportunities; reapplying, or suggesting reapplication of, data based on prior or frequent use; cashflow support based on many data items including previous regular payment patterns; or simply advising on the best payment rails given the context of the payment, balances, the companies, and history.

Another example is the AI-driven technology which can turn any paper form, even a complex waybill, into XML recognizing and correctly interpreting multi-part and structured data such as item lists, dates, airports (with linkages to international airport codes), complex location data – all automatically!  This is revolutionizing industries like trade finance, still heavily paper-based but under huge environmental pressure not only to remove paper but to provide track’n’trace provenance.

And some related technology shifts will fuel a huge increase in the use of AI and ML.

This leads to ‘invisible’ banking, or ‘embedded’ banking, where industry specialist systems carry out banking actions on behalf of a firm in the background, for example, a lawyer’s system creating a new virtual account automatically whenever a new legal case is created, allowing individual bank statements.

MACH (Microservice-based, API-first designed, Cloud/SaaS-delivered and Headless, i.e. can be used by a front end of a client system.) empowers the banks to become a player in a larger ecosystem (which they may or may not have curated) rather than a one-size-fits-all, take-it-or-leave-it provider. MACH also resolves the build-or-buy dilemma: banks now can do both.

These technology shifts increase the demand for AI and ML as banks seek higher customer centricity, closer to the ‘shop floor’ of innovation at their client firms – without the perils and costs of customization – and as they start to leverage not only the bank’s data for the benefit of their corporate client but also the industry data these ecosystem alliances will bring into play.

(The author Manish Maakan is Chief Executive Officer of iGTB and the views expressed in this article are his own)

Leave a Response