Automated Code Review for Secure Banking Applications Harnessing AI for Security, Performance, and Regulatory Compliance in Financial Software Engineering

Abstract

This research considers the integration of artificial intelligence into automated code review particularly for banking applications. The world of finance has some niche challenges of software development like stringent security expectations, transactional high volume performance demands, and complex regulatory compliance models. This is a detailed comparison of current automated code review models and their inefficiencies in offering specialized banking solutions. This research presents a novel multi-layered review process that combines static analysis, machine learning, and domain knowledge for detecting security bugs, performance hotspots, and compliance issues. This approach delivered a 37% boost in vulnerability detection and a 42% reduction in false positives compared to traditional tools on five large bank codebases. Implementation considerations, integration paths into existing CI/CD pipelines, and governance approaches are discussed in detail. The research suggests that employing AI-powered code review processes can enhance the security posture of banking applications significantly without reducing regulatory risk exposure and optimizing development effectiveness.