Multi-Agent AI Systems for Secure, Transparent, and Compliant Fraud. . .
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 (MAAI) framework designed to autonomously monitor, detect, and respond to anomalous financial behaviors across decentralized financial infrastructures.
The system integrates reinforcement learning, blockchain-based audit trails, and explainable AI (XAI) to enhance transparency, trust, and regulatory compliance.
Experiments conducted on real-world FinTech datasets demonstrate a 92 % detection accuracy and 38 % reduction in false-positive rates compared to traditional rule-based systems.
The framework lays the groundwork for AI-governed, self-regulating financial systems capable of safeguarding digital economies at scale.
This research presents a multi-agent artificial intelligence (MAAI) framework designed to autonomously monitor, detect, and respond to anomalous financial behaviors across decentralized financial infrastructures.
The system integrates reinforcement learning, blockchain-based audit trails, and explainable AI (XAI) to enhance transparency, trust, and regulatory compliance.
Experiments conducted on real-world FinTech datasets demonstrate a 92 % detection accuracy and 38 % reduction in false-positive rates compared to traditional rule-based systems.
The framework lays the groundwork for AI-governed, self-regulating financial systems capable of safeguarding digital economies at scale.
Type
Publication
International Journal of Research Publication and Reviews (IJRPR)
This paper proposes a multi-agent AI surveillance framework capable of identifying, auditing, and responding to fraudulent activities across digital financial systems in real time.
By combining AI governance principles, blockchain transparency, and autonomous decision-making, the research establishes a foundation for a next-generation FinTech security model that balances innovation with regulatory accountability.
