Do Fintech Lenders Align Pricing with Risk? Evidence from a Model-Based Assessment of Conforming Mortgages

Zilong Liu and Hongyan Liang ()
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Zilong Liu: Gies College of Business, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Hongyan Liang: Gies College of Business, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

FinTech, 2025, vol. 4, issue 2, 1-16

Abstract: This paper assesses whether fintech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages (2012–2020). We estimate default probabilities using machine learning (logit, random forest, gradient boosting, LightGBM, XGBoost), finding that non-fintech lenders achieve the highest predictive accuracy (AUC = 0.860), followed closely by banks (0.857), with fintech lenders trailing (0.852). In pricing analysis, banks adjust the origination rates most sharply with borrower risk (7.20 basis points per percentage-point increase in default probability) compared to fintech (4.18 bp) and non-fintech lenders (5.43 bp). Fintechs underprice 32% of high-risk loans, highlighting limited incentive alignment under GSE securitization structures. Expanding the allowable alternative data and modest risk-retention policies could enhance fintechs’ analytical effectiveness in mortgage markets.

Keywords: fintech mortgage lending; risk-based pricing; default prediction; machine learning; credit risk modeling (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2025
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