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Pandemic exposes limits of lenders' traditional credit metrics

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Despite economic crisis spurred by the global coronavirus pandemic, there is a consensus that it will be of finite duration.

Yet traditional credit models don’t account for this “black swan” event, so lenders need to build credit models that complement the traditional with alternative data. Strong underlying pre-crisis economic fundamentals together with the current unique consumer circumstances reinforces the vital need for an expanded approach to consumer credit underwriting for banking and financial services institutions.

Unfortunately, credit markets are going in the opposite direction. Stressed loan books and an uncertain economic forecast are causing consumer lenders to resort to the usual crisis playbook: increasing rates and tightening standards.

Underlying these actions is a reliance on credit models based on traditional data — income, debt levels and employment — to assess creditworthiness. In an era of record unemployment that uprooted responsible borrowers’ financial lives overnight, it has created a vicious cycle where credit is least accessible to qualified borrowers who most need it. This ultimately deepens the financial crisis.

A broader approach to credit underwriting is by using alternative data sources to expand credit access without increasing risk.

Examples include using utility data, banking data, social media information and academic data. The predictive power of this data can often be as quantitatively strong as traditional data, and potentially stronger when traditional data is missing.

Utility bills provide a picture of payment behavior, while banking data provides a picture of cash-flow patterns. Academic data — transcripts, degrees, enrollment patterns — are an indicator of interests, behavior, skillsets and income potential. Social media data, meanwhile, provides measures of an individual’s social connections, which in turn can correlate to income potential, interests and behavioral traits.

Taken together, these alternative data sources paint a picture of historical and current behavior and financial strength, which are the underpinnings of a predictive model that leads to creditworthiness.

Several fintech companies are starting to demonstrate the efficacy of this data. Prior academic performance is already being used to determine the creditworthiness for skills-based, health care and other educational loans.

These decisions rely on credit models that quantitatively demonstrate the correlation between previous academic achievements and future credit performance. These correlations are often subtle, but unmistakable once the data science is complete.

For example, data can show that students who transfer between universities deliver higher credit performance than those students who do not transfer.

Sourcing alternative data is possible through innovative technology platforms, and virtually any data is certainly available with consumer consent. Such technology platforms are making previously inaccessible consumer data readily available to prospective lenders.

These platforms leverage consumer consent to provide access to data that can then be shared with prospective lenders. Lenders gain access to previously unknown data and prospective borrowers are able to use an expanded set of data to demonstrate creditworthiness.

This consent-based alternative data approach also carries regulatory benefits. Such data is provided with explicit consumer consent, eliminating privacy concerns. Moreover, alternative data can expand access to credit to underbanked populations, alleviating Fair Credit Reporting Act concerns.

Great innovations are often birthed in times of crisis. The current coronavirus crisis will undoubtedly be no different.

This is already an unprecedented time for medical and scientific innovations whose legacy and impact will go far beyond the immediate need for a virus vaccine. It is time for the consumer credit markets to embrace innovation in the form of an expansion of credit underwriting data, providing necessary liquidity to Americans and opportunities for growth to the capital markets.

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Employment data Data integration Alternative lending Underwriting Consumer banking Consumer lending Coronavirus
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