Improving Fairness in Credit Lending Models using Subgroup Threshold Optimization

arXiv:2403.10652v1 Announce Type: new
Abstract: In an effort to improve the accuracy of credit lending decisions, many financial intuitions are now using predictions from machine learning models. While such predictions enjoy many advantages, recent research has shown that the predictions have the potential to be biased and unfair towards certain subgroups of the population. To combat this, several techniques have been introduced to help remove the bias and improve the overall fairness of the predictions. We introduce a new fairness …

abstract accuracy advantages arxiv credit cs.lg decisions fairness financial lending machine machine learning machine learning models optimization predictions q-fin.rm research threshold type