Fairness in credit scoring: Assessment, implementation and profit implications

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 credit-related decisions. Yet, the literature in credit scoring is scarce. paper makes three contributions. First, we revisit statistical fairness criteria and examine their adequacy scoring. Second, catalog options incorporating goals model development pipeline. Last, empirically compare different processors profit-oriented context using real-world data. empirical results substantiate evaluation measures, identify suitable to implement scoring, clarify profit-fairness trade-off lending We find multiple can be approximately satisfied at once recommend separation as proper criterion measuring scorecard. also in-processors deliver good balance between profit show discrimination reduced reasonable level relatively low cost. codes corresponding are available GitHub.