Beyond Predictive Accuracy Sensitivity analysis for behavioural credit assessment - policy paper

Behavioural and AI-based credit assessment increasingly uses transaction histories, repayment sequences and other dynamic signals. Validation usually focuses on predictive accuracy, calibration and fairness, but behavioural variables raise an additional issue: their economic meaning can change with feature definitions, financial state, portfolio scope and disclosure to consumers. This Policy Note proposes a sensitivity-analysis framework for model governance, drawing on methodological work developed within the WiseCredit research programme while reserving unpublished empirical results for academic papers. The framework emphasises portfolio-level validation, alternative variable constructions, stress and recovery tests, disclosure effects, subgroup stability and cautious treatment of psychometric data. Acknowledgement. This work was funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under project No. 09I03-03-V04-00502.