Bringing Change To Rural and Community Banks

Rural and small community banks are where large numbers of people and small businesses find access to lending and wealth creation. This is as much true in Asia as it is anywhere else.

Community banks in non-metropolitan areas have to contend with high penetration of smartphones and varied product offers from neo or digital banks. At the same time, depositors and borrowers  are vulnerable to extreme weather(and other) events, vagaries of farming and supply chains, and fallouts of regional conflicts. The impact  falls on capital adequacy, earnings, quality of assets, liquidity and the ability to respond to risks. These banks and financial institutions, therefore, need a strong measuring, forecasting and inferencing capability with a technology architecture that is de-layered, agile and focuses on the basics. While siloed, multi-layered, out-of-the-box software approaches have worked well for larger organizations with very significant compliance demands, small institutions need tech to make rapid and meaningful impact with low overheads. 

To put it simply, there needs to be very strong competence on the basic functions side, very high flexibility on the ability to integrate with third parties and an innovation capability for metrics.  The core banking technology part of the bank has to be entrusted with facilitating and enabling healthy deposit and lending flows on one hand and providing signals for future growth and present prudence on the other. In other words, technology is not important for itself but as a key policy and strategic tool. Hence we believe that the the technological stack has to be insightful and predictive rather than just technically competent.

How can this happen? One, the layers that have traditionally separated customer interaction interfaces from the back-office have to be reduced.  Two, the stack has to capture customer or account holder conversational streams and use those to create insights as well as actionable routines through an AI-based back-office. It has to indicate key possibilities and real-time incidents to managers inside the bank, since the staff strength will be small. Three, this requires a significant overhaul of the way data sources are integrated, data is stored and data processing outcomes are displayed. Let us keep in mind that a lot of data will be external to the bank, from diverse sources and accessed via APIs. Four, as digitization happens, the bank infrastructure and customers will face growing matters of security. This may be somewhat dampened at low per capita transactional levels. However, anomaly detection is becoming accessible and affordable and will be of use as the numbers grow. The processing of data gathered through natural language interfaces can help. There is no doubt that the way conversations happen between the banks and their customers digitally will be quite consequential in many ways. 

Let us pause for a moment and consider the supply side of technology. One, there are the traditional provider of licenses and large platforms who works with big budgets. The others are the smaller tech vendors who do bespoke work or provide regular technical services. The challenge for both in case of community banks is this- many of the banks cannot afford large platforms and frequent change requests. The old school of tech economics is increasingly problematic for some highly relevant segments, such as this. On the other hand,  banks are being forced to face the inevitability of digital banks encroaching on their turf. Then, there is the freight train of AI. How long before LLMs and their partners want a piece of the rural bank action? 

Keeping all these in mind, it is time to offer rural and community banks a new paradigm of instant lending and deposit banking capability , elimination of physical processes and cumbersome change requests and the ability to architect live conversations with their publics right into the heart of their set-ups. This is possible. Today.