The rise in global interest rates after more than a decade of ultra-low rates is hammering the fortunes of startups and others in the neo-tech world. Fintech firms, being members of the same club of cash-strapped innovators, are likely to face similar challenges. Unmixed blessings are rare compared to unicorns. While fintech firms have a huge potential to transform the financial services landscape, they also present new challenges and risks. Many successful fintech players are involved in the lending operations of banks. However, in the scenario of limited equity funding, some of them will face survival issues. Banks should monitor such risks of their fintech partners and put in place contingency plans to prevent any operational disruption. Recently, Reserve Bank of India (RBI) Governor Shaktikanta Das and other central bankers have highlighted the systemic risks to banking by fintech operators despite their immense leverage. However, a specific risk requires more regulatory and institutional focus. It risks how a lack of fintech funding can erode the credit profiles of their borrowers. While the scale of this risk appears to be limited right now, under certain adverse circumstances, it could trigger a consumer credit contagion that plagues the country’s banking system.
Risk to consumer credit: Roughly one in six fintech firms is a lender. Some of these fintech lenders (FLs) may lead to deterioration in the credit profiles of their borrowers. Let us see how this can happen. Most FL consumers focus on lending. Most of such loans are in the range of 3,000 to 50,000, with a tenure ranging from 1 month to 12 months. These are short-ticket, short-term (STST) loans. Buy-Now-Pay Later (BNPL) is a subcategory of loans that have even lower ticket sizes and tenors. Technically, these are unsecured personal loans (PL). Such loans promote financial inclusion by covering new-to-credit (NTC) borrowers and those who do not have proof of income or have low income. PL portfolios of FLs show higher delinquency rates than those of banks, as the latter’s target borrowers tend to have a lower risk profile than FLs, who expect higher interest charges to make up for the additional risk they carry.
Well established FL has significant capability in using advanced analytics to facilitate credit decision making. However, some FLs may have weakened their credit policies to pursue growth. Some worrying trends are emerging which are neither new nor unique to India.
Going Beyond the ‘Repeat Customers’ Theme: Leveraging existing customer data to improve credit decision making is a global best practice. However, too much can be a good thing. Maybe some FLs are getting closer to the region’s evergreen counterpart. Suppose, the borrower is expected to pay the loan by way of cheque. Even before the check is encashed, FL can extend another STST loan of a higher amount within 36 hours. The borrower enjoys credit float and will not have to return the principal. Thus the borrower does not get a chance to default. Other lenders view this borrower from the credit bureau lens as someone who is not delinquent and is always servicing high ticket size loans. Basically, a good credit profile!
Ladder and loan stacking: If one adds a competitive dimension, it is possible for a borrower to obtain an STST loan from one lender and pay back to another. In the meantime, the previous lender will use analysis on data that shows a deceptively improved credit profile. This lender will be ready for disbursement of the next STST loan. Here the borrower climbs the ticket-sized eligibility ladder almost not because his income has improved, but because of sub-optimal and questionable credit practices. Debt stacking is just a step away, where a borrower whose distress may not be manifest in credit performance, applies for a large number of loans from different borrowers and gets majority of them.
The process by which such pockets of leverage are maintained till the liquidity position changes and the FL itself faces funding challenges. It will then focus on improving the quality of its loan book and seek to increase cash collections and liquidate new loans more prudently. Borrowers, some of whom were new to credit and thus immature credit, would then be taken seriously as their on-tap credit dried up. Borrowers whose bureau credit scores are improving all this while will suddenly display ‘jump-to-default’ behavior.
Shock to the banking sector: Borrowers who used credit float to service bank loans from FL loans are likely to default with their banks. These banks would then go into risk-off mode and further disrupt credit. However, such a situation can be avoided if timely action is taken. Banks need to rethink their credit model and lending policies. Typical lending rules, such as 30+ or 60+ days-past-due payments in the last 6 months, may fail to capture the inherent risk of such borrowers as they avoid defaults. Risk enhancers such as stair climbing may also be missed. Since the advent of STST loans, banks have relaxed or eliminated leverage-based limits such as ‘two loans in the last three months’ because too many loan applicants were flagged. But such rules may have to come back. Risk management is an art form, where science precedes art. Machine learning won’t help if the data doesn’t capture every risk. This is where good judgment on risk management should come in.
Deep Mukherjee is a Quantitative Risk Management professional and is on the visiting faculty of Risk Management at IIM Calcutta
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