Publicação
Using machine learning to Solve Real Banking challenges at Banco Primus
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | |
| dc.contributor.advisor | Batikas, Michail | |
| dc.contributor.author | Rudolf, Nils | |
| dc.date.accessioned | 2026-05-29T12:22:48Z | |
| dc.date.available | 2026-05-29T12:22:48Z | |
| dc.date.issued | 2026-01-19 | |
| dc.date.submitted | 2026-01-19 | |
| dc.description.abstract | This thesis examines how machine-learning models and explainable AI can be used to analyze two distinct use cases: loan conversion and cross-selling in retail banking. Using proprietary data from Banco Primus, logistic regression, random forest, and XGBoost models are evaluated using business-oriented back-testing. SHAP is applied to explain predictions and identify key drivers. Approved loan conversion is mainly associated with partner characteristics, process timing, and communication availability. Personal loan cross-selling is mainly associated with external credit profiles and behavioral history, revealing campaign fatigue. The findings support process optimization in loan origination and propensity-based targeting frameworks for cross-selling. | eng |
| dc.identifier.tid | 204242584 | |
| dc.identifier.uri | http://hdl.handle.net/10362/203582 | |
| dc.language.iso | eng | |
| dc.relation | UID/00124/2025 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Business analytics | |
| dc.subject | Machine learning | |
| dc.subject | Marketing | |
| dc.subject | Retention | |
| dc.title | Using machine learning to Solve Real Banking challenges at Banco Primus | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | Value Lang Edit 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 |