Deep Learning Architectures for Credit Risk Assessment

This study investigates the effectiveness of advanced deep learning architectures including Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Transformer models for predicting credit default using sequential borrower behavior data. Traditional credit scoring systems often rely on static features and overlook temporal dependencies inherent in financial activity. To address this limitation, we develop and evaluate a suite of deep learning models that learn from time ordered transaction histories, payment behaviors, and utilization patterns. In addtion, we explore ensemble strategies that combine neural representations with classical statistical approaches, such as logistic regression and gradient boosting, to improve robustness and interpretability. Comprehensive experiments are conducted on both publicly available datasets (e.g., LendingClub) and proprietary microfinance loan portfolios. Evaluation metrics include AUC-ROC, log loss, and calibration error. Results show that Transformer-based models consistently outperform both traditional models and recurrent neural networks in terms of predictive accuracy and reliability. We also observe that ensemble models offer a favorable balance between performance and explainability, making them more suitable for production environments in regulated industries. Our findings underscore the transformative potential of deep learning in credit risk assessment, especially when deployed with fairness aware practices and explainability tools. This research contributes to the growing body of work advocating for AI driven credit scoring frameworks that are not only data driven and accurate but also operationally viable and ethically responsible.