AI-first banking is a mindset, not a technology

• Business model disruption is rising
• Secular trends to modern tech stacks
• Data and AI at the core of these shifts

Customers expect more than just digitised versions of existing products and services, and according to a May 2022 Salesforce report, about 73% of them want brands to understand and respond to their unique needs. This is the new age of relevance. Loyalty is fast fading, as customers demand more personalised choices. Banks that do not meet customer needs must prepare to face ‘critical ignoring’.  

For banks, personalisation has become table stakes, representing risk as well as opportunity. The Asian Banker’s Emmanuel Daniel, author of The Great Transition: The Personalisation of Finance is Here said: “Almost all digital banks are not designed to make money even in the current regime. It will become worse as customers personalise finance.”

Banks could tap into the $800 billion potential incremental revenues generated by the personalisation revolution. Or lose the same numbers.

Business model disruption is rising

Mike Lazaridis, founder of Blackberry, predicted the emergence of super-apps back in 2010, when he suggested “imagining a digital shopping mall of sorts, with convenience as the key selling feature”. This has gone from vision to reality in 12 years.

Today, everyone wants to be a super-app. It is one of 2023’s top 10 strategic technology trends, according to Gartner. Global estimates show that 15 top super-apps—Gojek, Tata Neu and Grab, to name a few—have 2.68 billion monthly active users. Gartner predicts that by 2027, more than 50% of the world will use a super-app every day.

Banks have to prepare for this future. They can choose to promote their own products or they can take the white-label route and embed digital financial services with the help of platforms. Another option is to go toe-to-toe with super-apps.

Many bankers say that banking is necessary, but banks are not. In a similar vein, the fintech revolution is here to stay, although individual fintechs may not be.

Secular trend towards modern tech stacks 

In the past year, banks were slated to spend around $623 billion on IT products and services. There have been a few emerging trends from this spending. According to the Harris Poll and IBV study: "A slight majority of companies have now embraced hybrid cloud (56%).”

With users embracing hybrid cloud as their model of choice, banks can maximise the flexibility and scalability of public cloud, while maintaining the security and latency of private networks.

Banks need to navigate a labyrinth of privacy regulations that other companies don’t bother with as much. Since the General Data Protection Regulation came into force in the European Union in 2018, countries like Australia, Switzerland and Thailand have enacted similar laws. Even tech giants are grappling with the growing calls for more regulation when it comes to consumer rights.

Regulators and governments across the world have woken up to the fact that their citizen’s data is an asset. They have picked up the pace on data localisation, ensuring that their consumer-citizen’s data is kept firmly within the geographical and regulatory limits they control.

Digital targeting is changing. Apple’s iOS update put users in control of their own data. Cookies and unique identifiers are out of favour, and first-party data is becoming more critical. This shifts power from platforms hawking third-party data solutions to enterprises harnessing their own first-party data. Each of these represents an opportunity and a challenge.

Data and AI are at the core of these shifts

Modern tech stacks throw off exponentially large amounts of data that enable better customer engagement that drives disruptive business models that provide even more data. 

The growing centrality of data and emerging trends in artificial intelligence (AI) means banks can scale faster and at a lower cost. They can now profile and engage customers better and innovate products and businesses. 

Banks need to harness the power of data and make it work to stay relevant. They must have the ability to use, ingest, curate and enrich data and build AI models for data monetisation—plug into any digital ecosystem and analyse their data, and convert these insights into compelling customer experiences. 

A bank is no slouch in collecting data. They get information from billions of wallet, banking, credit and debit card transactions each day across the globe. In India, Unified Payments Interface alone processes 10 billion transactions every month. It’s not just transactions—banks also collect a wealth of demographic, location, preference and other data.

However, when it comes to exploiting their data asset, far too often banks are their own slowest-mover enemy. They operate traditionally with ineffective data silos, a lack of accurate, high-quality enriched data, and a ‘we can do it all and this is my core differentiator’ mindset. They risk repeating what happened over the last decade in digital—and lose ground to fast-paced AI-first companies.

They need to think differently and focus on accelerating value from data. McKinsey estimates that being AI-first can drive up to $1 trillion in new value every year. And this starts with their mindset.

Suresh V Shankar is the founder and CEO of Crayon Data, an AI, big data and analytics company, headquartered in Singapore.