Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

Problem

Financial institutions need credit default prediction models that are both accurate and interpretable for risk management.

Why it matters

Credit defaults cause major financial losses, and uninterpretable models hinder trust and regulatory use.

What the AI did

Builds a causal Bayesian network model across four stages (data processing, structural learning, parameter learning, inference interpretation) and validates it empirically on six real credit datasets, with forward and reverse reasoning for interpretation.

Results

Bayesian networks outperformed traditional machine learning models and performed similarly to ensemble models across six real credit datasets.

Impact

Evidence supports improved, interpretable credit default prediction on six real datasets, verified against LIME and SHAP baselines.

Record

Importance rating

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