A Cloud-Based Framework Combining LSTM and Attention Mechanism for Comprehensive Financial Risk Prediction in Banking

Abstract

With the real-time high-speed financial transactions and digitalization of the modern fast-paced finance age, there is an urgent need for intelligent systems that can forecast financial risks at high speed and precision. This paper introduces a cloud-based system that is based on the integration of Long Short-Term Memory (LSTM) networks and Attention Mechanisms to provide robust financial risk predictions for the banking industry. With increasing complexity and magnitude of financial transactions, sophisticated predictive models that are capable of processing in real-time are needed. The conventional statistical techniques and rule-based systems have been found lacking in meeting the demands of dynamic finance. Through the use of the capacity of LSTM to best identify temporal dependencies in sequence data and complementing it with an Attention Mechanism to select the most pertinent features, the model improves the accuracy and interpretability of financial risk prediction. Scalability, safe data handling, and efficient storage are also achievable using the cloud-based system, allowing for cost-effective real-time risk calculation. Comparison of the performance of the proposed model with important metrics like accuracy, recall, F1-score, and AUC-ROC shows its better performance compared to traditional models. The paper presents a strong solution to financial institutions and banks to proactively detect and manage risks and to attain long-term stability and development in a more sophisticated financial system.