Anti-Money Laundering Detection

Latest papers

20 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Money-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset
Oct 15, 2026Owais Ahmad, Anirudh DeodharFinancial Fraud Detection Anti-Money Laundering Detection
Applied Research, Quantiphi, Marlborough, MA 01752, USA
Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption
Oct 13, 2026H. Çağrı Bilgi, Kemal Mutluergil, Ceren Yıldırım +2Anti-Money Laundering Detection Privacy-Preserving ML
Sabancı University, Istanbul, Türkiye
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis
Oct 9, 2026Natapong Nitarach, Phume Ngampornsukswadi, Pittawat Taveekitworachai +3Stratified Sampling Anti-Money Laundering Detection
SCB DataX, SCBX Group · Independent Researcher
KONTOGRAPH: Verified Point-in-Time Feature Consistency and Amortised Explanation for Real-Time Anti-Money Laundering under a 200 ms Decision Budget
Aug 23, 2026Ahmed AbolfadlAnti-Money Laundering Detection Anti-Money Laundering
Faculty of Media Engineering and Technology German University in Cairo Cairo, Egypt
Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework
Aug 15, 2026Emmanuel Nahimana, Yaé Ulrich GabaAnti-Money Laundering Detection Anti-Money Laundering
African Institute for Mathematical Sciences (AIMS) Senegal, KM 2, Route de Joal, Mbour, Senegal · AI Research and Innovation Nexus for Africa (AIRINA Labs), AI.Technipreneurs, Cotonou, Bénin · Sefako Makgatho Health Sciences University, Pretoria, South Africa
Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification
Jul 20, 2026Yuge Zhang, Yuanxing Zhang, Yichao Jin +7Natural Language Explanation Generation Financial Services
OCBC, Singapore
SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention
Jul 11, 2026Lidia Losavio, Francesco Sovrano, Dario Fenoglio +2Graph Attention Networks Graph Neural Networks
Università della Svizzera italiana (USI) Lugano, Switzerland
An Autonomous AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts
Jun 16, 2026Joseph Walusimbi, Joshua Benjamin SsentongoFinancial Fraud Detection Cybersecurity
Department of Electronics and Computer Engineering, Soroti University, Soroti, Uganda
Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance
Jun 15, 2026Dara Goldar, Geir Kjetil Ferkingstad Sandve, Martin JullumFinancial Fraud Detection Anti-Money Laundering Detection
Fremtind Insurance · University of Oslo · Norwegian Computing Center
Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks
May 13, 2026Rong Liu, Xiaojun Xiao, Zhanqing SuTemporal GNNs Heterogeneous GNNs
Sol Price School of Public Policy, University of Southern California, Los Angeles, USA · Boston University, Boston, USA · Johns Hopkins University, Washington, USA
Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph
Apr 26, 2026Ankur MalikFinancial Fraud Detection Anti-Money Laundering
Hudson Data, New York, NY, USA
TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering
Apr 19, 2026Keyang Chen, Mingxuan Jiang, Yongsheng Zhao +9Financial Fraud Detection Synthetic Benchmark Generation
Fudan University · Peking University
Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation
Apr 3, 2026Haseeb Tariq, Marwan HassaniAnti-Money Laundering Detection Anti-Money Laundering
ING Bank, Amsterdam, The Netherlands · Eindhoven University of Technology, Eindhoven, The Netherlands
Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling
Apr 1, 2026Haseeb Tariq, Alen Kaja, Marwan HassaniAnti-Money Laundering Detection Anti-Money Laundering
ING Bank Amsterdam, The Netherlands · Eindhoven University of Technology Eindhoven, The Netherlands · Identity & Risk Intelligence Adyen Amsterdam, The Netherlands +1
LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks
Mar 24, 2026Chung-Hoo Poon, James Kwok, Calvin Chow +1Anti-Money Laundering Detection Financial Fraud Detection
Logistics and Supply Chain MultiTech R&D Centre, Level 11, Cyberport 2, 100 Cyberport Road, Hong Kong · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks
Mar 22, 2026Dorothy Torres, Wei Cheng, Ke HuAnti-Money Laundering Anti-Money Laundering Detection
School of Science, Technology, Engineering and Mathematics 263 Academy Street, Room S204 Jersey City, NJ 07306 · School of Electrical Engineering and Computer Science, The Pennsylvania State University, 207 Electrical Engineering West, University Park, PA 16802, USA
DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning
Mar 14, 2026Renuga Kanagavelu, Manjil Nepal, Ning Peiyan +6Anti-Money Laundering Detection Differentially Private Federated Learning
A*STAR IHPC Singapore · SRM University-AP Andhra Pradesh, India · EVYD Singapore
Algorithmic Compliance and Regulatory Loss in Digital Assets
Mar 4, 2026Khem Raj Bhatt, Krishna SharmaAnti-Money Laundering Anti-Money Laundering Detection
Tide: A Customisable Dataset Generator for Anti-Money Laundering Research
Mar 2, 2026Montijn van den Beukel, Jože Martin Rožanec, Ana-Lucia VarbanescuAnti-Money Laundering Detection Synthetic Data Generation
University of Amsterdam, The Netherlands · Jožef Stefan Institute, Slovenia · University of Amsterdam/University of Twente, The Netherlands
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
Jun 4, 2025Bruno Deprez, Bart Baesens, Tim Verdonck +1Anti-Money Laundering Anti-Money Laundering Detection
KU Leuven · University of Antwerp - imec · University of Southampton

Oct 15, 2026J/K move · Enter open · S save

Money-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset

Owais Ahmad, Anirudh Deodhar

Applied Research, Quantiphi, Marlborough, MA 01752, USA

Financial Fraud Detection Anti-Money Laundering Detection Anti-Money Laundering Node Classification Relational Deep Learning Heterogeneous GNNs

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Money-mule networks move criminal funds through many small transfers, each of which looks ordinary on its own. What marks an account as a mule lies outside any single transfer: in the counterparties and addresses its network shares, and in the shape of its activity over time. Production systems remain largely rule-based and see neither, while most research classifies transactions rather than accounts, recovering account-level risk only by aggregating afterwards. We instead score the account directly, reading the bank's relational schema as a heterogeneous graph in which rows are nodes and foreign keys are edges. Each account is scored from a multi-hop neighbourhood using per-column encoders, a relative-time encoding and HeteroGraphSAGE message passing, with that neighbourhood restricted to rows predating the account's own pre-flag cutoff so that no model sees evidence recorded only after the account was flagged. On a five-year Reserve Bank Innovation Hub (RBIH) retail-banking dataset of 400 million transactions and 94{,}063 labelled accounts, 2.6% of them confirmed mules, we compare against RelGNN, XGBoost, LightGBM and CatBoost trained on identical labels, splits and cutoffs. Our detector recovers nine in ten held-out mules at 83% precision and a 0.5% false-alarm rate, finding 115 more mules than the strongest tree ensemble given the same evidence. At the tuned thresholds the gap is mostly in recall: XGBoost and LightGBM lead across the first hundred alerts, but only the graph models hold their precision deep into the queue. Since the graph is the schema itself and every score is dated per account, the approach deploys on a bank's existing database without a custom feature graph build.