Multi-Agent AI Systems for Secure, Transparent, and Compliant Fraud Surveillance in Cross-Border FinTech Operations
Abstract
The global expansion of FinTech ecosystems has introduced complex challenges in detecting cross-border fraud, regulatory non-compliance, and digital money laundering. This research presents a multi-agent artificial intelligence framework designed to monitor, detect, and respond to anomalous financial behavior across decentralized financial infrastructures. The system integrates reinforcement learning, blockchain-based audit trails, and explainable AI to improve transparency, trust, and regulatory compliance.
Type
Publication
International Journal of Research Publication and Reviews (IJRPR)
This paper proposes a multi-agent fraud surveillance framework for identifying, auditing, and responding to suspicious activity across digital financial systems.
