Macro Uncertainties, Technology and Community Banks-A possible path ahead.

I had an hour-long conversation with my peers at the World Finance Council panel discussion in Singapore recently. While we were talking about ESG and fintech, it is becoming increasingly clear that we need to consider a whole-of-ecosystem approach rather than fitting issues within silos. The key challenge in all this is uncertainty. Global supply chains make uncertainty more palpable and consequential because different countries and actors are closely inter-linked. This, in turn, affects the liquidity position of all parties along the chain. Consequently, credit possibilities and growth prospects are affected. As many of these players are employers on a fairly large scale, payroll is likely to face impact and reduce spends in local consumer markets. Social issues immediately come to mind when we talk through all these. There are people, families and communities involved. Society also influences how policymakers design interventions and vice versa. The trade-offs between immediate societal priorities and longer-term, hard-to-describe benefits of sustainability, are not new. Unfortunately, it is only when communities face real problems that sustainability becomes a matter of interest in the public domain. By then, negative impacts may be real and unstoppable. Different players in society play different roles in matters concerning sustainability. It is impossible and impractical for finance professionals and technology companies to address everything. However, the inter-linkages between different stakeholders does make the actions of one meaningful for another.

Financial institutions have a long history of analysing multiple variables and how they influence key economic indicators. Technology firms, arising from varied backgrounds, now provide the know-how to project multiple scenarios based on alternative variable mixes, in ways that are easy to visualise. While there is a lot of hype around generative AI, the hard work in optimizing data sets and applying algorithms to get deeper insights continues. The crucial shift that we may see now are in the field of query and response. In other words, we can ask of machines what we ask one another. This may have been catalysed by generative AI but does not necessarily involve that per se. There is a lot around data graphs and what one may call traditional AI(an oxymoron of sorts) that we have not fully explored yet. One may venture to say that reasoning is very well served by AI while the reasoning itself remains in our hands.

How should community banks and financial institutions take advantage of this? In many ways, the absence of a complex legacy stack is advantageous. It is possible for bank owners and senior executives to discuss and agree on low-cost, optimal database design and deployment. That is the first step. The overall stack should be-within reason-one of conversations between end-customers and the bank, a configurable banking layer (as opposed to a heavy core-banking platform) and APIs that link treasury and compliance to lending origination, management and third party partners. Over time, wealth may be added as well. One might suggest this could be faster than currently envisaged.

Now, this is not at all prescriptive-and cannot be. There are very well-endowed community banks including those which are owned by large commercial institutions. They will leverage their in-group resources. Others may choose to remain in a space where they intensively service a small and loyal community. But some will look for smart ways to transform. There are three key issues which can bring fundamental change beyond the above(and related to these). One, real-time risk assessment of each potential borrower(and especially, those who are in the middle of supply chains). Two, the variables that determine credit rating; not the usual alternatives but core changes in local weather, soil conditions, land availability, consumer base, infrastructure realities and liquidity. Three, tap conversations to get insights from customers for demand generation. The last issue here is a vast one but  quite useful and I will go into details in a follow-up post. I will pause here and not go into the larger areas of data graphs and interactive visual scenario building.  Those do not necessarily-at least for now-fit the constraints and needs of community banks. However, the transformational processes described here are sufficiently deep.  Once implemented, community banks will be in a particularly resilient position in the face of uncertainty.