An Ensemble Learning Approach to Improve Credit Scoring Accuracy for Imbalanced Data

The assessment of loan risks, known as credit scoring, is a critical procedure within the financial industry. However, this process often faces difficulties as a result of uneven data distributions. Imbalances are evident when one class has a much greater representation than the other, resulting in standard machine learning techniques displaying a bias towards the more prevalent class. The present work presents a new ensemble learning approach that aims to enhance the accuracy of credit scoring in situations when there are imbalanced class distributions. The proposed methodology incorporates a combination of several fundamental classifiers that have been trained on separate subsets of the initial dataset, resulting in a cohesive ensemble model. A series of experiments were done on a credit scoring dataset with unbalanced classes, exploring several ensemble methodologies like as bagging, boosting, and stacking. The results indicate that the ensemble strategy proposed in this study outperforms traditional techniques, exhibiting greater accuracy and F1 score. Furthermore, we conduct an analysis on the effects of various ensemble tactics on performance, offering significant insights into the process of ensemble learning for credit scoring data that is uneven. The technique proposed in this study offers a viable alternative to address the problem of imbalanced credit scoring data, hence improving the accuracy and fairness of credit scoring models in the financial industry.