Fintech’s capital constraints can impact credit profiles (2024)

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The risk to consumer credit: Roughly one in six fintech firms is a lender. Some of these fintech lenders (FLs) may cause a deterioration in the credit profile of their borrowers. Let’s see how this may happen. Most FLs focus on consumer lending. Most such loans are in a range from ₹3,000 to ₹50,000, with tenures from 1 month to 12 months. These are small-ticket, short-tenure (STST) loans. Buy-now-pay-later (BNPL) is a subcategory of such loans, with even lower ticket sizes and tenures. Technically, these are unsecured personal loans (PLs). Such loans do drive financial inclusion by including new-to-credit (NTC) borrowers and those who have no-income-proof or low income. The PL portfolios of FLs show higher delinquency rates than those of banks, as the latter target borrowers with lower risk profiles than do FLs, which expect higher interest charges to make up for the extra risk borne by them.

Well established FLs have significant competence in using advanced analytics to facilitate credit decision making. However, some FLs may have weakened their credit policies to chase growth. Certain worrisome trends are emerging which are neither new nor unique to India.

Overdoing the ‘repeat customers’ theme: Leveraging data of existing customers to improve credit decision making is a global best practice. However, there can be too much of a good thing. Some FLs may be coming close to the sector’s equivalent of evergreening. Say, the borrower is expected to pay back the loan via cheque. Even before the cheque is encashed, the FL may extend another STST loan of a higher amount within 36 hours. The borrower enjoys a credit float and will not have to pay back the principal. The borrower thus does not get a chance to default. Other lenders see this borrower from a credit bureau lens as someone who is not delinquent and has been servicing loans of ever-higher ticket sizes. Basically, a good credit profile!

Laddering and loan stacking: If one adds the competitive dimension, it is possible for a borrower to get an STST loan from one lender and pay back another. In the meantime, the previous lender will use analytics on data which shows a deceptively improving credit profile. This lender will be ready to disburse the next STST loan. Here the borrower almost climbs up the ticket size eligibility ladder not because his income has improved, but because of suboptimal and questionable credit practices . Loan stacking is only a step away, where a borrower whose distress may not have been revealed in loan performance applies for a large number of loans from various borrowers and gets most of them.

The process by which such pockets of leverage build up continues till liquidity conditions change and the FL itself faces funding challenges. Then it will focus on improving the quality of its loan book and try to enhance cash collection and also underwrite new loans more prudently. Borrowers, some of whom were new to credit and thus credit immature, would then be seriously taken aback to see their on-tap credit drying up. Such borrowers whose bureau credit score may have been improving all this while will all of a sudden exhibit ‘jumps-to-default’ behaviour.

Shock transmission to the banking sector: Borrowers who had used credit floats from FL loans to service bank loans are likely to start defaulting with their banks. These banks will then swing into risk-off mode and constrain credit further. Such a situation can be avoided, though, if timely action is taken. Banks need to relook at their credit models and lending policies. Typical loan-gating rules, such as instances of 30+ or 60+ days-past-due payments in the last 6 months, may fail to capture the inherent risk of such borrowers, since they avoided defaulting. Risk enhancers like laddering may also be missed. Ever since the advent of STST loans, banks have relaxed or done away with leverage-based limits such as ‘two loans in the last three months’ because a lot of loan applicants were flagged. But such rules may need to come back . Risk management is an art form, one where science precedes art. If data doesn’t capture every risk, machine learning will not help. This is where fine judgement on risk management must come in.

Deep Mukherjee is a quantitative risk management professional and on the visiting faculty of risk management at IIM Calcutta.

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