“But with tens of millions of consumers left out of traditional credit scoring and the pandemic exposing potential problems in the current system, [lenders] are collecting and crunching all manner of other data to determine who ought to get a loan and how much they should pay.”

“We’re seeing the need to be smart and effective in this moment to make sure that the recovery is stronger and broader than after the last financial crisis.”

“Non-traditional data is an important way to give consumers access to better rates and open up borrowing to more consumers and businesses. This data can include things like rent and cell phone payments, giving lenders a broader range of information to consider.”

“Use of AI in government is still a burgeoning field, with promising potential in detecting fraud, increasing efficiency and responsiveness, and generally improving service to citizens. To reach these goals and avoid missteps, governments have a series of lessons to learn: they must adopt policies that protect privacy, train public officials to understand the technologies being implemented, including the risks, and implement guardrails to prevent biases in the data from further disenfranchising disadvantaged groups.”

“Today, roughly 50% of U.S. consumers are estimated to have signed up for financial apps or other products that frequently rely on data aggregators to collect information via customer-authorized transfers, with substantial growth in the first months of the COVID-19 pandemic.”

As recent developments have renewed interest in harnessing new data and analytical techniques for credit underwriting, stakeholders are asking questions about potential benefits and risks particularly for addressing racial equity issues. FinRegLab Deputy Director Kelly Thompson Cochran wrote an article summarizing recent initiatives and issues for an edition of the Community Development Innovation Review titled “Fintech, Racial Equity, and an Inclusive Financial System” that has been published by the Federal Reserve Bank of San Francisco and the Aspen Institute.

“‘[T]he predictiveness of the cash flow scores and attributes was generally at least as strong as the traditional credit scores and credit bureau attributes,’ suggesting it’s a reliable complement to or replacement for traditional scoring.”

“Policymakers are actively building their understanding of the implications of AI/ML on model governance, fairness, explainability, and financial inclusion”

“AI adoption and data maturity within enterprises have seen significant growth in the past decade. With each passing day, new enterprise AI use cases come to life in more organizations and more industries.”

“Research suggests artificial intelligence and machine learning can create measurable benefits including financial inclusion, yet we also know it can bake in and even exacerbate historical bias and exclusion.”