Building Ensemble Models for Enhanced Credit Scoring: A Case Study from a Bosnian MicroFinancial Institution

This research paper presents the development of a comprehensive credit scoring model for a MicroFinancial institution in Bosnia and Herzegovina. The study employs diverse machine learning and deep learning algorithms to analyze a real-life dataset, aiming to establish a reliable and high-performing credit scoring model. We explored the efficacy of single classifiers, including Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks, alongside advanced ensemble models such as XGBoost and CatBoost. To enhance predictive accuracy, we also experimented with creating additional ensemble combinations of these models. The performance of each model was rigorously evaluated using a range of metrics: Accuracy, Precision, Recall, F1 score, Type I error, and Type II error. The findings reveal that integrating multiple, varied classifiers significantly boosts the performance compared to individual classifiers. This approach not only contributes to the field of credit risk assessment but also offers practical insights for financial institutions seeking robust credit scoring solutions. The results underscore the potential of ensemble methodologies in harnessing the strengths of different machine and deep learning models for improved decision-making in credit scoring.