Alternative credit metrics are poised to revolutionize underwriting

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Globalization has made the world smaller. At the same time, it has made consumer lending bigger and far more complex. Gaps in traditional credit reports and increased unpredictability from the diversification of borrowers have complicated many credit decisions and changed the way we think about underwriting models. 

This complexity means that banks are looking or should be looking for novel approaches to shoring up the safety and soundness of their lending practices. In other words, they're eager to both de-risk their investment decisions while also expanding their target addressable market to include new cohorts of borrowers. To achieve both of these objectives without compromising their credit standards, lenders are turning to alternative data to do so.

Lenders have every right to be vigilant about risk in what can only be described as a challenging environment. Persistently elevated interest rates (that likely won't begin to fall again until later in the year, if then) coupled with record-breaking consumer debt and rising delinquencies across most categories all threaten credit quality and performance. With the U.S. bank card balance up 13% and transitions into serious credit card delinquencies up 59% year over year, though admittedly starting from a low base in most cases, it's no wonder that lenders have tightened their standards.

But the credit outlook also seems promising, or at least not so dire, in many respects. The job market has remained strong despite fears of a recession, and at least some of the balance growth can be explained as an effect of inflation. Which is to say, lenders should be cautious about setting broad risk-mitigation measures, like simply setting higher minimum-required credit scores, that immediately disqualify huge swaths of the market.

Alternative data, which has already disrupted practices and workflows across financial services, offers a more surgical approach to curtailing credit risk. Despite this disruption being for the better in many functions, credit risk remains an area that has yet to fully harness alternative data's potential. It has been left behind in the race for innovation, leaving valuable, risk-mitigating data sources out of reach for financial services firms and, specifically, the risk managers and lending officers on the front lines of navigating an intricate landscape. Of particular concern are the delicate regulatory and compliance matters that come into play when handling and using consumer credit data.

This lack of alternative data sources perpetuates a reliance on outdated, incomplete metrics such as traditional credit scores. These are riddled with inaccuracies due to soft inquiry data, COVID government stimulus programs, buy now/pay later gaps, among others, leaving risk leaders flying blind and vulnerable to high default rates and potential losses. Alternative data, such as bank transaction (cash flow) data, has demonstrated the ability to fill the gaps in traditional credit risk data but has yet to be incorporated broadly into financial institutions. However, lenders should not let these challenges deter their mission for better risk management. Credit risk may be the last frontier of the alternative data movement in finance, but it is finally poised for its moment.

Banks are inherently information businesses, constantly seeking to capitalize on data disparity to drive strategic decision-making. The ability to incorporate new data sources into credit risk models to make better decisions, as they have done in other functions like equity research and financial analytics, has been a longtime dream. By harnessing advanced analytics and technology, banks can unlock a host of insights that not only mitigate risk but also level the playing field for all borrowers.

Fueling the momentum is the regulatory environment, now on the precipice of change with the impending Consumer Financial Protection Bureau (CFPB) 1033 ruling expected to eliminate data-sharing obstacles for consumers. This streamlines the way banks collect and consider alternative data points in their credit risk assessments and creates immense opportunities for the widespread adoption of cash flow data within credit underwriting practices. While at first glance these regulations might be challenging for banks to adopt, they are well positioned to be long-term beneficiaries of credit product innovations and fintech infrastructure and analytics improvements that open banking facilitates.

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One sizable barrier to incorporating this data is the lack of existing infrastructure needed to onboard and leverage this amount of data in a compliant way. Building a robust credit data supply chain that can scale ingestion and analytics of consumer credit data is a critical puzzle for banks to solve — but it is happening.

Of course, no editorial on the future of alternative data in consumer lending would be complete without mention of artificial intelligence, which practically all financial services organizations are feeling pressured to embrace. But before we can reasonably expect AI to have a future in credit decision-making, lenders must first build the rails for efficient external data integration and analysis.

With the right tools and technologies in place, lenders can harness the power of data to drive unprecedented insights, improve risk management practices and ultimately unlock the full potential of AI in lending. As we chart a course toward the future, it is evident that the utilization of alternative data is not mere speculation — it is a strategic imperative for the advancement of credit assessment. By embracing new alternative data sources, we stand at the cusp of a paradigm shift in the industry, one that promises to deliver more accurate decisions, transform the way banks evaluate creditworthiness and drive better outcomes for borrowers and lenders alike.