Post-Pandemic Credit Decisioning: Be Careful!!!
Consumer credit is one of the many financial services that continues to change since the pandemic making credit risk decisioning and data analytics even more compelling. Consumers have responded in many different manners on the road to recovery where some are ready to spend again and borrow again (Buy Now, Pay Later), or just continue to save and minimize financial stress.
Considering the varying consumer habits in a post-pandemic environment, previous credit models need to be recalibrated to consider a large number of lender accommodations and deferrals, the impact of other payment assistance programs such as rent relief, and the overall spending and savings habits causing anomalies in traditional approaches to decisioning.
This brave new world of consumer lending is moving traditional decisioning to consider new machine learning models and increasing the need for digital acquisitions to better understand their customer base and increase the value to existing customers through automated decisions. To be successful against the competition, lenders will need to leverage data and advanced analytics to understand the value of newly acquired customers plus their existing portfolios. Those customers ready to re-engage in obtaining credit should be presented with new and exciting credit products delivered in an omni-channel environment. At the same time, lenders must be prepared for a wave of new delinquencies (chronic and habitual past due borrowers and those falling behind for the first time) and avoid payment holidays or other offerings that will mask the true status of the portfolio.
The pandemic moved consumers to the digital market and lenders need to support and continue this trend with older consumers as well as millennials. Digital interaction has become the preference for consumers in all financial and lending markets, especially auto finance and mortgage lending. The online omni-channel experience and credit risk management have become intertwined from acquisition, onboarding, loan management, collections, and refinancing. To support this sea change of behavior, lenders must improve their digital experience. They should enable customers to move through the lending process quickly, easily, without redundant tasks, and integrate solution providers that expedite delivery and customer satisfaction.
One solution is to engage with an omnichannel solution provider that utilizes automated decisioning to deliver fast, accurate, consistent, intelligent, and compliant lending decisions. Lenders need software that is scalable for a diverse borrower base, with the configurability to move the complicated cases to human support for a personal touch.
While the current credit lending landscape requires advanced data and analytic approaches, be sure that the AI vendor of choice has sufficient post-pandemic data scenarios and validation that supports the Interagency Statement on the Use of Alternative Data in Credit Underwriting guidelines. As stated in the guidelines, the use of alternative data must be validated in credit underwriting, as well as in fraud detection, marketing, pricing, servicing, and account management. To comply, lenders need to recognize data limitations and evaluate analytics models for potential bias to ensure credit access to all eligible customers. As with prior developments in the evolution of credit underwriting, including the advent of credit scoring, the use of alternative data and analytical methods also raises questions regarding how to effectively leverage new technological developments that are consistent with applicable consumer protection laws. Applicable consumer protection laws include, as appropriate, fair lending laws, prohibitions against unfair, deceptive, or abusive acts or practices, and the Fair Credit Reporting Act.
Be aware as well of the CFPB Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process (RFI) uncertainty about how AI fits into the existing regulatory framework may be slowing its adoption, especially for credit underwriting. One important issue is how complex AI models address the adverse action notice requirements in the Equal Credit Opportunity Act (ECOA) and the Fair Credit Reporting Act (FCRA). ECOA requires creditors to provide consumers with the main reasons for a denial of credit or other adverse action. FCRA also includes adverse action notice requirements. For example, when adverse action is based in whole or in part on a credit score obtained from a consumer reporting agency (CRA), creditors must disclose key factors that adversely affected the score, the name, and contact information of the CRA, and additional content. These notice provisions serve important anti-discrimination, educational, and accuracy purposes. There may be questions about how institutions can comply with these requirements if the reasons driving an AI decision are based on complex interrelationships.
Be sure your loan decisioning tools account for disposable income calculations and integrate with the market-leading vendors supporting the guidelines “for the evaluation of a borrower’s income and expenses to help determine repayment capacity is a well-established part of the underwriting process. Improving the measurement of income and expenses through cash flow evaluation may be particularly beneficial for consumers who demonstrate reliable income patterns over time from a variety of sources rather than a single job. Cash flow data are specific to the borrower and generally derived from reliable sources, such as bank account records, which may help ensure the data’s accuracy. Consumers can expressly permission access to their cash flow data, which enhances transparency and consumers’ control over the data.”
Now is the time to be extra vigilant in credit decisioning as two weeks into the job, Consumer Financial Protection Bureau Director Rohit Chopra laid out his vision and priorities as part of the CFPB’s required semi-annual report to Congress. In his testimony, Chopra made it clear that the CFPB will zero in on wide-scale damages from big businesses.
Amongst other concerns,
- he raised the issue of transparency in how credit decisions are made in our era of big data, automation, and algorithms;
- CFPB will sharpen its enforcement focus on “repeat offenders,” or those companies who operate under consent decrees;
- the agency will carefully monitor conditions in the mortgage market and take steps to minimize avoidable foreclosures; and
- we should expect further scrutiny on Fair Credit Reporting Act (FCRA) issues, especially on how companies are investigating disputes.
But the new era of governance at the CFPB is not the only powerful agency taking on a new direction. Leadership changes at the Federal Trade Commission (as well as at the Federal Reserve, Justice Department, and Office of the Comptroller of the Currency) will bring renewed scrutiny to our industry.
Invest in the technology that will deliver a powerful combination of machine learning tools, advanced data analytics, and automation technology. With the possibility of upcoming regulatory changes, lenders need an omnichannel, end-to-end decisioning solution that has a single platform and single database for the entire lending life cycle. Minimize your risk by enabling intelligent, compliant, insight-led decisions that maximize customer value across their lifecycle.