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The law of equal opportunities or unintended consequences?: The effect of unisex risk assessment in consumer credit

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  • Galina Andreeva
  • Anna Matuszyk

Abstract

Gender is prohibited from use in decision making in many countries. This does not necessarily benefit females in situations of automated algorithmic decisions, e.g. when a credit scoring model is used as a decision tool for loan granting. By analysing a unique proprietary data set on car loans from a European bank, the paper shows that gender as a variable in a credit scoring model is statistically significant. Its removal does not alter the predictive accuracy of the model, yet the proportions of accepted women/men depend on whether gender is included. The paper explores the association between predictors in the model with gender, to demonstrate the omitted variable bias and how other variables proxy for gender. It points to inconsistencies of the existing regulations in the context of automated decision making.

Suggested Citation

  • Galina Andreeva & Anna Matuszyk, 2019. "The law of equal opportunities or unintended consequences?: The effect of unisex risk assessment in consumer credit," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1287-1311, October.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1287-1311
    DOI: 10.1111/rssa.12494
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