Enhancing Digital Banking Security: Applying Deep Learning for Real Time Threat Detection in Financial Electronics Systems

Power outages, military equipment failures, and sensitive data leaks are all possible outcomes of cyberattacks. The banking industry is particularly at risk because of the significant financial worth of the data it contains. When banks have more digital footprints, hackers have a larger attack surface to work with. The purpose of this research is to examine the advantages and disadvantages of integrating deep learning algorithms into real-time threat detection systems in the context of digital banking. First, we give a broad introduction to digital banks and the specific security issues that set them apart from traditional banks. Next, we investigate how machine learning can improve cybersecurity defences. Among the issues with traditional methods of network security are their incapacity to detect sophisticated and insider threats, slow response times, and lack of scalability. These shortcomings draw attention to the necessity for research into more comprehensive and effective security techniques to protect against the growing range of network threats. The conclusion has significant implications for the financial industry, suggesting that given the ongoing evolution of fraud tactics, combining cutting-edge big data analysis and deep learning techniques is essential. Financial institutions can benefit from the study's conclusions, particularly with regard to future planning along with action in the ongoing development of cutting-edge fraud detection systems.