Deep Learning-Powered Resilience for Stability in Financial Networks using Anomaly Detection and Predictive Analytics

Network resilience is crucial for the financial industry to maintain services and avoid expensive inefficiency in a time when financial networks are becoming more and more susceptible to interruption. To improve network resilience in finance, this study introduces a deep learning technique and extracts applications of hybrid deep learning models. The model uses network traffic statistics, historical transaction records, and RNNs for failure prediction, CNNs for anomaly detection, and RL for self-healing to reduce interruptions and speed up recovery times. The model utilizes real-world data results to showcase significant advancements that enhance its effectiveness for financial networks. It achieves accuracy in fraud detection and anomaly identification, with an overall precision of 92% and recall of 89%. These promising advances in deep learning techniques will not only make financial networks more resilient and anomaly-free but also pave the way for future innovations in a secure and dependable financial system.