Gender as a Risk Factor: A Test of Gender-Neutral Pricing in Lithuania’s P2P Market

Mindaugas Jasas and Aiste Lastauskaite ()
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Mindaugas Jasas: Faculty of Business, Kaunas Kolegija Higher Education Institution, Pramonės av. 22, 50387 Kaunas, Lithuania
Aiste Lastauskaite: Faculty of Business, Kaunas Kolegija Higher Education Institution, Pramonės av. 22, 50387 Kaunas, Lithuania

Risks, 2025, vol. 13, issue 12, 1-15

Abstract: European Union legislation, particularly Council Directive 2004/113/EC, mandates gender neutrality in credit scoring to prevent discrimination. However, this creates a regulatory paradox if gender is a statistically relevant predictor of default risk. This study investigates this “fairness-through-unawareness” approach by empirically testing for systematic mispricing. We employ a twofold econometric analysis on a dataset of consumer loans from a Lithuanian peer-to-peer platform. After data preparation for the regression, the sample consists of 9707 loans. First, logistic regression is used to model actual default risk, controlling for credit rating, age, loan amount, and education. Second, Ordinary Least Squares (OLS) regression is used to model the interest rate set by the platform. The Logit model finds that gender is a highly significant predictor of default ( p < 0.001), with male borrowers associated with a higher probability of default. Conversely, the OLS model finds that gender is not a statistically significant factor in loan pricing ( p = 0.263), confirming the platform’s compliance with EU law. The findings empirically demonstrate the regulatory paradox: the legally compliant, gender-blind pricing model fails to account for a significant risk differential. This leads to systematic risk mispricing and an implicit cross-subsidy from lower-risk female borrowers to higher-risk male counterparts, highlighting a critical tension between regulatory intent and outcome fairness. The analysis is limited to observed loan-level characteristics; it does not incorporate household composition or the internal structure of the platform’s proprietary scoring model.

Keywords: credit risk; gender bias; peer-to-peer lending; algorithmic fairness; regulatory paradox; proxy discrimination; Logit model; econometric analysis (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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