An experimental study on fairness-aware machine learning for credit scoring problem
This work addresses fairness issues in credit scoring for financial organizations, but it is incremental as it focuses on evaluating existing methods rather than introducing new ones.
The paper tackles the problem of bias in machine learning models for credit scoring by conducting a comprehensive experimental study on fairness-aware methods and measures, evaluating their performance on financial datasets.
Digitalization of credit scoring is an essential requirement for financial organizations and commercial banks, especially in the context of digital transformation. Machine learning techniques are commonly used to evaluate customers' creditworthiness. However, the predicted outcomes of machine learning models can be biased toward protected attributes, such as race or gender. Numerous fairness-aware machine learning models and fairness measures have been proposed. Nevertheless, their performance in the context of credit scoring has not been thoroughly investigated. In this paper, we present a comprehensive experimental study of fairness-aware machine learning in credit scoring. The study explores key aspects of credit scoring, including financial datasets, predictive models, and fairness measures. We also provide a detailed evaluation of fairness-aware predictive models and fairness measures on widely used financial datasets.