FINREGLAB ANNOUNCES NEW RESEARCH TO EVALUATE INCLUSION IMPACTS OF MACHINE LEARNING CREDIT MODELS WITH AND WITHOUT ALTERNATIVE DATA

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May 29, 2024, 13:00 ET

It will also co-chair an OCC technology working group to further address inclusion barriers in financial services

WASHINGTON, May 29, 2024 /PRNewswire/ -- FinRegLab announced that it is conducting new empirical research to evaluate the inclusion impact of machine learning credit models, including those built with bank account data as well as traditional credit report information. The organization has also been invited by the Office of the Comptroller of the Currency (OCC) to co-chair a new Technology Working Group within the OCC's Project REACh initiative.

While large banks and fintechs are increasingly adopting machine learning techniques and cash-flow data sources for credit underwriting, no publicly available research directly compares their separate and combined effects on lending models' predictiveness, fairness, and inclusion. This unique research will build on FinRegLab's prior evaluations of the potential for cash-flow data to increase financial inclusion and explainability and fairness tools to help manage machine learning credit models by assessing the financial inclusion benefits of using machine learning models with and without bank account data.

"Machine learning analytics combined with more representative data have the potential to significantly expand access to financial products and services that improve people's financial wellbeing, but guardrails for responsible use are critical to protect historically underserved populations as artificial intelligence transforms the financial sector," said FinRegLab CEO Melissa Koide. "I am thrilled to announce these new initiatives to further our mission of filling knowledge gaps and creating platforms for stakeholders to discuss critical issues to spread best practices and increase consumer benefits from these innovations."

Credit plays a critical role in helping households bridge short-term financial gaps and make long-term investments in homes, reliable transportation, and small business formation. Yet millions of consumers and entrepreneurs—including disproportionate numbers of Black, Hispanic, and low-income applicants—struggle to access affordable credit because they are difficult to assess using traditional data sources and analytical techniques.

In addition to the empirical research, FinRegLab has been invited by the Office of the Comptroller of the Currency to co-chair the new Technology Working Group as part of its Project REACh (Roundtable for Economic Access and Change) initiative. The initiative brings together leaders from banks, community and civil rights organizations, and technology firms to facilitate solutions to address barriers that prevent full, equal, and fair participation in the nation's economy.

The Technology Working Group will focus on machine learning, artificial intelligence, and digitalization topics. Among other projects, the group will seek to build a shared understanding of safe, responsible, and inclusive practices in deploying machine learning in credit underwriting. The working group will build on FinRegLab's deep engagement with industry, advocates, and regulators to analyze policy issues such as explainability, fairness, and model risk management when lenders use new types of data and more complex, or even "black box," analytic techniques.

About FinRegLab
FinRegLab is an independent, nonprofit research organization that conducts research and experiments with new technologies and data to drive the financial sector toward a responsible and inclusive marketplace. We also facilitate discourse across the financial ecosystem to inform public policy and market practices.

Contact:
Kelly Cochran
FinRegLab
[email protected]

SOURCE FinRegLab