Advanced Machine Learning Techniques for Predictive Modeling of Non-Performing Assets in Public Sector Banks

Abstract

This inquiry delves into the application of Long Short-Term Memory (LSTM) networks for forecasting Non-Performing Assets (NPAs) in the context of public sector banking. Traditional methods of credit monitoring often falter, struggling to adapt to the ever-evolving behaviors of borrowers and the vicissitudes of economic conditions. This study seeks to remedy this shortcoming: by formulating a framework that employs LSTM—a formidable deep learning paradigm. The investigation scrutinizes a myriad of data preprocessing techniques, feature engineering processes, model training protocols and evaluative strategies (utilizing Python and Scikit-learn). The results illustrate a marked enhancement in accuracy of predictions and robustness in comparison to traditional methodologies. Future endeavors will pivot toward the real-time deployment of these models, integrating Explainable AI (XAI) to augment transparency and foster trust in the predictive framework; however, challenges remain.