Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering
Money laundering is the process where criminals use financial services to move massive amounts of illegal money to untraceable destinations and integrate them into legitimate financial systems. It is very crucial to identify such activities accurately and reliably in order to enforce an anti-money laundering (AML). Despite tremendous efforts to AML only a tiny fraction of illicit activities are prevented. From a given graph of money transfers between accounts of a bank, existing approaches attempted to detect money laundering. In particular, some approaches employ structural and behavioural dynamics of dense subgraph detection thereby not taking into consideration that money laundering involves high-volume flows of funds through chains of bank accounts. Some approaches model the transactions in the form of multipartite graphs to detect the complete flow of money from source to destination. However, existing approaches yield lower detection accuracy, making them less reliable. In this paper, we employ semi-supervised graph learning techniques on graphs of financial transactions in order to identify nodes involved in potential money laundering. Experimental results suggest that our approach can sport money laundering from real and synthetic transaction graphs.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph LearningSimilar Papers 제목 키워드 기반
SemiETS: Integrating Spatial and Content Consistencies for Semi-Supervised End-to-end Text Spotting
Most previous scene text spotting methods rely on high-quality manual annotations to achieve promising performance. To reduce their expensive costs, we study semi-supervised text spotting (SSTS) to exploit useful informa…
Domain AdaptationText DetectionText SpottingCATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
Federated learning is a promising paradigm that utilizes distributed client resources while preserving data privacy. Most existing FL approaches assume clients possess labeled data, however, in real-world scenarios, clie…
Federated LearningSemi-Supervised Federated Learning for Keyword Spotting
Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly expanded the ability to train machine le…
Federated LearningKeyword SpottingFighting Money Laundering with Statistics and Machine Learning
Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in…
BIG-bench Machine LearningFairnessSynthetic Data GenerationInspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin
Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars of di…
Graph Neural Network