Credit-risk management with machine learning: more accuracy, faster decisions

1Introduction

Credit risk is the largest balance-sheet risk for banks; traditional logistic-regression scorecards miss non-linear behaviour.

2Discussion

Random forests and gradient boosting analyse hundreds of variables and their interactions, often separating good and risky borrowers significantly better.

Accuracy is not enough: regulators and customers need explanations. Interpretability methods such as SHAP reveal each factor’s contribution and expose hidden bias.

3Conclusion

Economic behaviour shifts, especially under inflation, so models must be continuously monitored, validated and retrained.