Prediction of Operational Risk in Rural Credit Banks Based on CNN and LSTM Methods

With the rapid advancement of artificial intelligence (AI), its implementation in financial institutions has raised concerns regarding new risk management policies, particularly for rural credit banks. This research proposes the development of a predictive system for operational risks in rural credit banks based on two deep learning techniques namely (1) Convolutional Neural Networks (CNN) and (2) Long Short-Term Memory Networks (LSTM). While CNN identifies and captures video footage to analyze risky commands issued by internal staff, LSTM processes real-time server logs to detect patterns that could lead to system failures. This system aims to enhance the prediction of risks associated with internal operations, including staff errors, equipment/system failures, and environmental risks. The results demonstrate that this approach significantly improves the effectiveness of risk management in rural credit cooperatives and offers valuable insights into other institutional risk management strategies.