AI Application in Anti-Money Laundering for Sustainable and Transparent Financial Systems
Year: 2025
Type: conference-paper
Abstract: Money laundering and financial fraud remain major threats to global financial stability, costing trillions annually and challenging regulatory oversight. Recent research demonstrates that financial compliance—when supported by RegTech, artificial intelligence, and digital automation—can reduce KYC/AML processing time, strengthen customer trust, and contribute to long-term sustainable development outcomes in the digital economy. These findings highlight the increasing strategic importance of technology-enabled compliance and motivate the need for AI-driven solutions that improve efficiency while supporting transparency and responsible governance. 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. To the best of our knowledge, no prior research has combined graph-based RAG architectures with generative AI specifically for KYC Customer Due Diligence (CDD)/Enhanced Due Diligence (EDD) in AML. 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.
Authors: Chuanhao Nie, Yunbo Liu, Chao Wang
Institutions: Georgia Institute of Technology, Duke University, Rice University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7127594073