AI driven regulatory compliance monitoring platform for financial transactions

Abstract

With the increasing transaction volume, emerging fraud, complex money laundering schemes and a growing number of regulatory requirements, financial institutions have to deal with ever-increasing compliance pressures while dealing with legacy rule-based systems that create too many false positives and do not work in real-time and privacy-conscious or decentralized environments. To fill this void in integrated AI solutions, the authors introduce a novel solution a hybrid regulatory compliance monitoring platform that combines supervised ensemble models for detecting suspicious transactions, federated learning for privacy-preserving risk assessment, double-debiased machine learning for causal analysis and blockchain-based smart contracts for immutability, data integrity and auditability. The platform has 93.5% accuracy and 92.8% F1 score in transaction monitoring, with up to 4.8% systemic risk reduction with improved performance in tail risks and low latency of 3.7 seconds. These results are significantly better than the rule-based baselines and stand-alone ML methods and also are robust against heterogeneity tests and datasets. The platform’s ability to integrate centralized banking and DeFi markets through clear, scalable and secure solutions brings transformative opportunities for regulatory compliance, operational effectiveness and financial stability.