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The law of equal opportunities or unintended consequences?: The effect of unisex risk assessment in consumer credit
Author(s) -
Andreeva Galina,
Matuszyk Anna
Publication year - 2019
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12494
Subject(s) - proxy (statistics) , loan , actuarial science , instrumental variable , unintended consequences , context (archaeology) , variables , variable (mathematics) , credit risk , economics , business , econometrics , computer science , law , finance , political science , machine learning , mathematics , paleontology , mathematical analysis , biology
Summary 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.