Fairness in Credit Scoring: Assessment, Implementation and Profit Implications. (arXiv:2103.01907v4 [stat.ML] UPDATED)

The rise of algorithmic decision-making has spawned much research on fair
machine learning (ML). Financial institutions use ML for building risk
scorecards that support a range of credit-related decisions. Yet, the
literature on fair ML in credit scoring is scarce. The paper makes three
contributions. First, we revisit statistical fairness criteria and examine
their adequacy for credit scoring. Second, we catalog algorithmic options for
incorporating fairness goals in the ML model development pipeline. Last, we
empirically compare different fairness processors …

arxiv credit fairness implementation ml profit scoring