Publicação
Machine learning-based insolvency prediction for Siemens’ extended payment terms
| authorProfile.email | felix.otter@web.de | |
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | |
| dc.contributor.advisor | Obermeier, Daniel | |
| dc.contributor.author | Otter, Felix | |
| dc.date.accessioned | 2026-06-19T10:21:32Z | |
| dc.date.available | 2026-06-19T10:21:32Z | |
| dc.date.issued | 2026-01-29 | |
| dc.date.submitted | 2026-01-29 | |
| dc.description.abstract | 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. | eng |
| dc.identifier.tid | 204242568 | |
| dc.identifier.uri | http://hdl.handle.net/10362/203890 | |
| dc.language.iso | eng | |
| dc.relation | UID/00124/2025 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Insolvency prediction | |
| dc.subject | Machine learning | |
| dc.subject | Explainable AI | |
| dc.subject | Extended payment terms | |
| dc.subject | Behavioral payment data | |
| dc.subject | Credit rirsk | |
| dc.title | Machine learning-based insolvency prediction for Siemens’ extended payment terms | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics |