Future of Banking Security: Using Neural Networks to Predict and Prevent Loan Defaults

This paper proposes using Artificial Neural Networks to enhance banking security by predicting loan defaults. It advocates integrating advanced technology to address challenges in risk management. The research focuses on using neural networks to analyze historical data and predict default probabilities, aiding proactive risk management. The methodology involves training a model on diverse financial parameters, demographics, and economic indicators. Leveraging neural networks' ability to identify complex patterns, the model improves creditworthiness assessments for informed decision-making. It details the network architecture, feature selection, and model performance evaluation. The study emphasizes AI's transformative potential in risk assessment amid changing financial landscapes. Results show ANN integration significantly improves default predictions, offering valuable insights for banks seeking robust risk management strategies.