BNPL was a wake-up call to traditional lenders. Perhaps existing models could handle more risk. Maybe branch banking finally lost its appeal to mobile devices. How to compete with well-funded fintechs that did not carry the burden of risk/reward lending or safety and soundness mandates?
However, business requires growth, and financial institutions must consider how the world changes.
Here is an interesting article from Tearsheet by an Artificial Intelligence company that specializes in consumer lending. If you are one of the many bankers still scratching their head on why BNPL is so appealing, take notice.
- The business case for sub-prime borrowers and increasing financial inclusion is solid — it’s now all about the execution.
- According to Accenture, it is estimated that banks could generate up to $380 billion in annual revenue by closing the small business credit gap and bringing unbanked and underbanked adults into the formal financial system.
- And wider access to credit could boost global GDP by $3.7 trillion, and engender $4.2 trillion in new deposits and $2.1 trillion in additional loans, according to a report from McKinsey.
- Citibank’s recent report echoed the business imperative and revealed that closing the racial inequality gaps could add $5 trillion of GDP to the U.S economy.
The challenge requires lenders to balance their underwriting strategies with both traditional judgmental lending and machine learning.
The article oversimplifies the shift to machine learning and alternative data:
- A lack of credit history doesn’t make someone riskier than someone with a robust file. It just makes them harder to score using the traditional credit scoring system, which has been limited to a couple of dozen factors such as credit score, income, and current debt outstanding.
Credit history is undoubtedly an indicator of future performance. The trick here is to create an effective, risk-controlled method to address low scores and weak files. As BNPL showed, if bankers do not do it, someone else will.
But despite the excitement of machine learning, there are some downsides to consider. The article does not present a view that pricing to risk is essential.
- If I am an “A” graded borrower, and you have no credit experience, should we pay the same price for unsecured borrowing? It might bring you into the world of credit, but should it be at my expense?
- Bringing in large volumes of risky credit types, those with high debt or never previously able to handle debt becomes a different proposition when the economy shifts. Consider how COVID 19 affected other groups, particularly low wage service workers, many of whom would have benefited from alternative scoring before the pandemic took hold.
But there is something to machine-driven lending, and banks must consider it. Risk management and pricing, however, are critical to success. Lenders still need to keep a keen eye for controlling the dollars at risk.
Sometimes, it is better to have an unregulated fintech take a chance before creating an environment that falls outside what regulators would call prudential lending. The unproven path for financial institutions is to take advantage of value-added processes, such as lending that embraces a broader audience but maintains a rigorous approach to risk management.
Overview provided by Brian Riley, Director, Credit Advisory Service at Mercator Advisory Group
