Abstract
An AI-based machine learning method is used in this study to find cybersecurity holes in financial institutions. Because they handle a lot of personal data and do most of their business online, financial institutions are particularly vulnerable to cyberattacks. Most of the time, complex, dynamic threats are not detected in real time by typical security processes. This is how threat detection and mitigation could be significantly aided by AI and ML. Large data sets can be analyzed by machine learning systems to find patterns and possibly suspicious activity that suggests an attack is in progress. By putting the suggested approach into practice, financial institutions may monitor network traffic, detect suspicious activity, and prevent fraud more effectively. When compared to previous approaches, it also increases threat recognition speed and accuracy. The use of different machine learning algorithms can be quite beneficial for security systems. The capacity to identify and categorize unusual patterns is one such method. As the system gains knowledge from fresh data, it becomes more adept at identifying possible threats. The goal of this research is to find a responsible and reliable way to safeguard the banking system. By using AI-powered security solutions, financial institutions may improve their safety management and lower risks. The study highlights how crucial it is to use cutting-edge technologies to protect personal financial information.