Obermeier, DanielOtter, Felix2026-06-192026-06-192026-01-292026-01-29http://hdl.handle.net/10362/203890This 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.engInsolvency predictionMachine learningExplainable AIExtended payment termsBehavioral payment dataCredit rirskmaster thesis204242568