Why 2022 is the year for data exploration at banks

It goes without saying that the rate at which banks have embraced digitization in the last 24 months has been extraordinary and unprecedented. Before the COVID-19 pandemic, programs that were expected to take a decade or longer to complete were finished in less than two years. [caption id="attachment_221236" align="alignright" width="225"] Michael Yesudas, chief technology officer at SunTec[/caption] But in 2022 the banking sector still faces a core challenge: they lack the technological sophistication of their fintech counterparts. A recent report by Accenture revealed that out of nearly 2,000 directors at more than 100 of the world’s largest banks, only 10% of board directors and 10% of CEOs had any professional technology experience. While banks have achieved significant progress with digital transformation, they have a long way to go to learn how to use data intelligently, in a secure way, to reap the benefits that data utilization can bring. Their inability to get the most from artificial intelligence (AI) may impede further progress and put them at a competitive disadvantage. To overcome this challenge, data exploration may prove a turning point for banks this year. We’ll see banks undergo large-scale data exploration programs to scrutinize and examine the data they retain so they can fully understand their data assets. This includes everything from transaction and payroll data to foreign exchange data. Reluctance to Abandon Rightly so, banks are reluctant to abandon their core banking focus but don’t have the capabilities to unlock customer data and use it optimally. In the coming year, however, banks will need to step up their game and adopt a technology-first approach to data to fully realize the benefits — both financially and competitively — that it can bring. That said, the aforementioned strategy may entail a certain degree of risk. Once data moves to third parties, it is beyond the bank’s control and could lead to data misuse and mishandling. It’s vital that banks find new ways to mitigate risks that may compromise customer data and, in turn, trust. Ultimately, banks must strive to connect with customers on a deeper, hyper-personalized level and evolve into trusted advisors to remain relevant. Despite a wealth of data, banks still experience significant technology gaps. That’s where AI comes in. Banks have begun to use AI for middle-office tasks, such as fraud prevention, customer segmentation, know-your-customer (KYC) verification, credit underwriting, lending risk management and much more. It can also help banks drive optimal pricing decisions and bring more transparency in pricing by clarifying the variables behind each decision, doing away with the “black box” effect, which can foment mistrust on price recommendations. ‘Endless’ Use Cases The possibilities can be endless. For example, banks can leverage AI to shorten the KYC and anti-money laundering (AML) compliance requirements by conducting the necessary checks and following all the processes, just like digital bank Monzo did for its onboarding. The bank can focus on optimizing verification accuracy, lowering signup abandonment rates, reducing manual review and improving verification speed. Banks can even create a new bank brand-driven extensively by algorithms that nudge its customers at the right time to make the right decisions based on customer financial goals. Today, a bank’s ecosystem must remain highly robust while simultaneously becoming more agile. The current lack of flexibility creates an opportunity for application programming interface (APIs) and niche challenger players to compete in the banking landscape. If banks continue to rely solely on these robust but rigid core systems, will they remain relevant to customers? And what are the different ways in which large banks with a rich heritage of applications can attract today’s young customers? What banks must do in 2022 is deploy a middle layer that has the intelligence to translate data available in their core ecosystem into actionable insights. This middle layer will amplify the effectiveness of the robust core system, making the bank more agile and flexible to meet customer needs while leveraging the benefit of a robust core. This can help banks effectively deliver value while making informed decisions to remain profitable, as well as ensure there is an effective value exchange between banks and their customers and partners. Michael Yesudas serves as chief technology officer at SunTec, where he heads the company’s technology and engineering functions. He has previously served in technology leadership roles at IBM, Sterling Commerce, GE and HP.

Why 2022 is the year for data exploration at banks

Expect a host of new, large-scale data exploration programs at FIs

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Why 2022 is the year for data exploration at banks

Expect a host of new, large-scale data exploration programs at FIs

Mambu and Volante partner for cloud-enabled payments integration

5 Questions with … TD Bank CIO Judy Dinn

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