AI Application in Anti-Money Laundering for Sustainable and Transparent Financial Systems

TL;DR

This paper explores how AI can modernize anti-money laundering efforts by improving detection, transparency, and operational efficiency, and proposes innovative AI-driven KYC tools to support sustainable financial systems.

Contribution

It introduces a novel RAG-Graph architecture for KYC processes, enhancing transparency and efficiency in AML workflows, and discusses future AI research directions for AML.

Findings

RAG-Graph architecture achieves high faithfulness and relevancy in KYC tasks.

AI applications improve detection accuracy and reduce manual investigation efforts.

Proposes AI-driven KYC system that supports sustainable compliance practices.

Abstract

Money laundering and financial fraud remain major threats to global financial stability, costing trillions annually and challenging regulatory oversight. This paper reviews how artificial intelligence (AI) applications can modernize Anti-Money Laundering (AML) workflows by improving detection accuracy, lowering false-positive rates, and reducing the operational burden of manual investigations, thereby supporting more sustainable development. It further highlights future research directions including federated learning for privacy-preserving collaboration, fairness-aware and interpretable AI, reinforcement learning for adaptive defenses, and human-in-the-loop visualization systems to ensure that next-generation AML architectures remain transparent, accountable, and robust. In the final part, the paper proposes an AI-driven KYC application that integrates graph-based retrieval-augmented generation (RAG Graph) with generative models to enhance efficiency, transparency, and decision support in KYC processes related to money-laundering detection. Experimental results show that the RAG-Graph architecture delivers high faithfulness and strong answer relevancy across diverse evaluation settings, thereby enhancing the efficiency and transparency of KYC CDD/EDD workflows and contributing to more sustainable, resource-optimized compliance practices.