Credit risk prediction remains both a challenging and high-interest problem due to the inherently unbalanced nature of financial datasets and the continuous drive for higher predictive precision. In this work, I build upon previous advancements in credit risk modeling and introduce an ensemble-based Artificial Neural Network (ANN) architecture designed to enhance classification performance. By leveraging a selective ensemble of decision networks, this approach not only improves prediction accuracy but also mitigates the challenges posed by imbalanced data distributions. While the primary focus is on credit risk prediction, my analysis demonstrates that the proposed model can be effectively applied for both dimensionality reduction and classification of unbalanced datasets more broadly. The results reinforce the potential of ensemble deep learning strategies in financial risk assessment, offering a scalable and precise solution for real-world credit risk evaluation.
