Synergistic Approaches to Credit Scoring: Enhancing Predictive Performance with Attention Mechanisms and Ensemble Learning

Accurate evaluation of credit risk is essential while making financial decisions. This paper presents an ensemble strategy that combines the predictive capability of random forests with the complex features of attention-enhanced neural networks and gradient boosting to increase credit scoring accuracy. The model uses a synergistic approach to credit scoring by utilizing the unique strengths of each model, highlighting their collaborative contributions, and offering insights into the interpretability of complex ensemble methods—an important consideration when making credit decisions with substantial ramifications—this approach offers promising potential to outperform individual models.