Fintech remains one of the fastest-growing sectors in tech. Lending is a key driver, and we’re heading toward both a lending challenge and a lending opportunity as the nature of work and employment continues to shift. The numbers are likely to grow significantly over time. So the question becomes: how do we serve these potential borrowers when they may not fit into traditional underwriting buckets or come with clear, conventional credentials? Three Emerging Borrower Segments Three Emerging Borrower Segments One — Salaried individuals who become small business owners later in life. While many may retain certain privileges from their earlier careers, such as a credit card, they often struggle to acquire these privileges in their new circumstances. A home mortgage or a new credit card is now likely out of reach. Two — Young start-up owners. Some will secure VC funding; many will not. If they fail and return to traditional employment, they remain business owners in spirit and in financial reality. Their numbers will continue to rise in India, the Philippines, and Indonesia. Three — People who are living and working longer than before. Once you are beyond 55 or 60, access to loans or credit cards becomes increasingly difficult. How do we support these segments—segments that will not only grow over the next decade but also become key contributors to GDP? The Solution The Solution Let’s make use of data. A question that may arise here is about the availability and quality of data for small businesses, especially small-scale retail. This is no longer as difficult as it might have previously been. This is because of the following reasons: a. Consumer payments have moved to cashless in a big way across the board; b. A growing percentage of business payments are shifting from cheques to electronic bank transfers as well as real-time or next-working-day payments. This means that there is digital data that can be extracted, optimized, and transferred for processing. The availability of this kind of data can change long-held assumptions about risk profiles and creditworthiness. How would this kind of data be seamlessly available to lenders and their decisioning engines? One usually hears about venture capital in the context of start-ups. But credit cannot be underestimated. It offers choice, and it should be easier to tap. Easier borrowing helps small firms and entrepreneurs cover manufacturing costs, ship orders on schedule, and expand capacity. Some possibilities include real-time loans with limited shelf-life of offer; flexible tenors tied to varying risk levels; multi-tier options for family-run businesses to spread risk among members; hybrids of secured and unsecured finance; and lending models that leverage new IP or nascent cash flows. Above all, AI can be used to predict business performance—especially cash flows. A natural question arises about the availability and quality of data for small businesses, particularly small-scale retail. This is no longer as difficult as it once was. Consumer payments have moved to cashless in a big way across the board, and a growing percentage of business payments are shifting from cheques to electronic bank transfers, as well as real-time or next-working-day settlements. This means digital data now exists that can be extracted, optimized, and transferred for processing. The availability of such data can reshape long-held assumptions about risk profiles and creditworthiness. How would this data be made seamlessly available to lenders and their decisioning engines? That may not be straightforward, given regulations around risk and required due diligence. The framework for data collection will have to evolve. An ideal approach could be to establish a consent regime in which a lender can request permission from a borrower to access data from his or her existing banking relationships. This consent framework will require updates to legislation and regulations, some of which are already underway. Ease of credit access can be designed as a key outcome of such consent. This may also require changes to the credit-decisioning infrastructure itself. It is likely that mid- to large-sized financial institutions will call for intermediaries to play a more active role in providing real-time outputs and managing their decisioning engines as these systems grow more complex. If all of the above comes together, we could see the foundation of entirely new categories of lending products. Easier access to borrowing helps small firms, early-stage start-ups, and entrepreneurs cover manufacturing and procurement costs, meet operational expenses, ship orders on schedule, and expand business capacity. By leveraging data, lending institutions may offer new products that balance business growth with risk control. Some possibilities include: real-time loans with limited offer windows; flexible tenors tied to changing risk levels; multi-tiered offers for family-run businesses to distribute risk among members; hybrids between secured and unsecured financing; and products that leverage new IP or nascent cash flows. The flow of credit to the end borrower, along with repayment behavior, can be used to generate real-time scores for all relevant parties, accompanied by appropriate incentives and penalties.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Using Data and Consent Architecture to Address Emerging Borrower Segments

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