Enhancing Loan Approval Systems with Deep Learning

Loans are a cornerstone of the banking sector, generating revenue through interest while posing risks due to non-performing assets (NPAs). Efficient loan approval and risk assessment are essential to optimizing profitability and minimizing defaults. Traditional evaluation methods rely on manual assessments, leading to inefficiencies and biases. In this paper, we present a comparative analysis of three deep learning techniques-Convolutional Neural Networks (CNNs), TabNet, and Autoencoders-to evaluate their performance in loan approval prediction and risk assessment. CNNs are analyzed for their capacity to capture complex, nonlinear relationships between applicant features, TabNet for its dynamic feature selection and interpretability, and Autoencoders for their effectiveness in anomaly detection. Based on our experimental results, CNN outperformed the other models across all key evaluation metrics, including accuracy, precision, recall, and F1-score. Our findings offer valuable insights into the strengths and limitations of each model, guiding the application of deep learning in modern banking systems to enhance decision-making and alleviate risk in loan approval processes.