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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zilong Liu & Hongyan Liang, 2025. "Do Fintech Lenders Align Pricing with Risk? Evidence from a Model-Based Assessment of Conforming Mortgages," FinTech, MDPI, vol. 4(2), pages 1-16, June.
    • Handle: RePEc:gam:jfinte:v:4:y:2025:i:2:p:23-:d:1674734
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    References listed on IDEAS

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