Year: 2026
Type: dissertation
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.
Author: Rudolf, Nils
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7203852664