AI Model Security and Adversarial Techniques in Finance and Accounting Analytics

Abstract

AI integration in finance and accounting domain is revolutionizing core areas such as fraud detection, algorithmic trading, credit underwriting, and audit analytics. So, while they make operations more efficient and improve decision-making, and this introduce unique challenges and security threats specific to AI systems. This paper aims at giving a complete rundown of such vulnerabilities which are fast emerging for financial AI models, such as data poisoning, adversarial inputs, model extraction, and membership inference attacks, and provides examples based on real-world financial scenarios to show how these attacks could counter fraud detection, influence market behavior, or compromise sensitive data. The study also considers practical defense mechanisms-including adversarial training, input validation, privacy-preserving methods, and live model monitoring-that could be instituted at any point along the AI value chain. Recognizing the urgent need of robustness and regulatory compliance, the paper advocates “security-by-design” approach facilitated by cross-functional teams. These insights are intended to help both practitioners and policy makers work towards the creation of secure and trustworthy AI systems that meet operational and regulatory demands required by the modern financial ecosystem.