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Publicação
Using machine learning to Solve Real Banking challenges at Banco Primus
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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.
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Business analytics Machine learning Marketing Retention