Accurately predicting bank loan defaults is a critical task for financial institutions, as it directly impacts their risk management and decision-making processes. Traditional methods for credit risk assessment often rely on statistical models and historical financial data, which may not fully capture the complexities and patterns associated with loan defaults. This paper proposes the use of deep learning techniques to enhance the prediction accuracy of bank loan defaults. By leveraging the capabilities of neural networks, particularly deep neural networks (DNNs) and recurrent neural networks (RNNs), the proposed model aims to identify intricate patterns and relationships in the data that traditional models might overlook.The model is trained on a comprehensive dataset comprising various borrower attributes, loan characteristics, and historical default information. Feature engineering techniques are employed to extract meaningful features from raw data, enhancing the model's predictive power. The proposed system integrates multiple deep learning architectures, including convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) networks for sequential data analysis, to capture both static and dynamic aspects of borrower behavior. Evaluation metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic (ROC-AUC) curve are used to assess the model's performance. The results demonstrate that the deep learning approach outperforms traditional statistical methods, providing more accurate and reliable predictions of loan defaults.
