AN AI-DRIVEN SALARY MANAGEMENT FRAMEWORK: LEVERAGING GENERATIVE AI DETECTION TO ENSURE COMPLIANCE WITH ANTI-MONEY LAUNDERING GOVERNMENT POLICIES

Abstract

The rapid proliferation of highly capable generative artificial intelligence has introduced unprecedented vulnerabilities into global financial systems, particularly in the realm of corporate payroll and salary management. Malicious actors increasingly exploit large language models (LLMs) to synthesize highly convincing employment contracts, fake employee profiles, and deceptive financial justifications to launder illicit funds through legitimate corporate channels. To combat this emerging threat, this paper proposes a novel salary management system integrated with state-of-the-art AI detection mechanisms designed to enforce compliance with government anti-money laundering (AML) policies. By adapting advanced text detection methodologies—such as rewriting analysis and inverse prompting—the proposed framework automatically flags synthetic documents that evade traditional rule-based compliance checks. Ultimately, this research bridges the gap between cybersecurity innovations in generative AI detection and critical financial regulatory compliance, offering a scalable, privacy-conscious solution to modern financial fraud.