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Machine learning-based insolvency prediction for Siemens’ extended payment terms
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This thesis investigates short-term insolvency prediction using solely behavioral payment data and explainable AI in the context of Siemens' Extended Payment Terms program. A dual modeling approach combines interpretable baselines (logistic regression, random forest, and
XGBoost) with a sequence-based Long Short-Term Memory (LSTM) model. The LSTM achieves moderate discrimination at natural default prevalence, while SHAP-based explanations and interviews with risk managers show that cross-model consistency and local explanations
strengthen trust in the model’s outputs.
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Insolvency prediction Machine learning Explainable AI Extended payment terms Behavioral payment data Credit rirsk