Recent Studies from Federal Reserve Bank of Boston Add New Data to Machine Learning (How Magic a Bullet Is Machine Learning for Credit Analysis? an Exploration With Fintech Lending Data): Machine Learning

2025 JUL 17 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News -- Current study results on Machine Learning have been published. According to news originating from Boston, Massachusetts, by NewsRx correspondents, research stated, “Fintech lending to consumers has grown rapidly since the 2007-9 Great Recession. This study applies machine learning (ML) methods to loan-level data from the largest fintech lender of personal loans, to assess the extent to which these methods can produce more accurate out-of-sample default predictions relative to standard regression models, as argued by fintech lending’s advocates.”

Our news journalists obtained a quote from the research from the Federal Reserve Bank of Boston, “To explain loan outcomes, this analysis accounts for the economic conditions faced by a borrower after origination, which are typically absent from other ML studies of default. For the given data, the ML methods indeed improve prediction accuracy, but more so over horizons within a year. Having more data up to but not beyond a certain quantity enhances the relative predictive accuracy of the ML methods, likely because there has been data or model drift over time, so that more complex models can suffer more out-of-sample misses.”

According to the news editors, the research concluded: “Prediction accuracy rises, but only marginally, with additional standard credit variables beyond the core set, suggesting that unconventional data needs to be sufficiently informative as a whole to help consumers with little or no credit history.Finally, in this data, we find little statistically significant evidence that ML methods yield unequal benefits across subgroups of borrowers defined by their risk attributes, income or where they live.”

This research has been peer-reviewed.

For more information on this research see: How Magic a Bullet Is Machine Learning for Credit Analysis? an Exploration With Fintech Lending Data. Journal of Credit Risk, 2025;21(1). Journal of Credit Risk can be contacted at: Incisive Media, Haymarket House, 28-29 Haymarket, London, SW1Y 4RX, England.

The news correspondents report that additional information may be obtained from J. Christina Wang, Federal Reserve Bank of Boston, 600 Atlantic Ave, Boston, MA 02210, United States.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.21314/JCR.2025.005. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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