The paper presents a graph‑based method for detecting money‑mule accounts by treating a bank’s relational schema as a heterogeneous graph and scoring each account using multi‑hop neighbourhoods, per‑column encoders, relative‑time encoding, and HeteroGraphSAGE message passing. On a five‑year RBIH retail‑banking dataset of 400 million transactions, the model achieves 83 % precision and a 0.5 % false‑alarm rate, recovering nine out of ten held‑out mules and outperforming tree‑based baselines in recall while maintaining precision deep into the alert queue. The approach can be deployed directly on a bank’s existing database without constructing a custom feature graph.
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 a...
arXiv:2608.30364v1 Announce Type: new Abstract: Retail banking attrition is usually represented as a terminal binary event, even though client relationships often weaken earlier through partial movem...
arXiv:2608. 15177v1 Announce Type: cross Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities.
arXiv:2608. 15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions.
arXiv:2608.22389v1 Announce Type: cross Abstract: Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This remo...
Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This removes both the overnight batch window in which anti-m...
The study evaluates how the choice of test boundary affects feature‑based hardware Trojan detection across Trust‑Hub families. Using a corpus of 49,124 gates from 16 netlists, the authors compare three test settings—pooled gates, a single netlist held out, and an entire host family held out—showing that performance drops markedly when a host family is excluded. The results demonstrate that sibling benchmark variants can inflate detection metrics, and the authors recommend reporting family‑aware holdouts alongside pooled scores.
arXiv:2608. 15559v1 Announce Type: cross Abstract: Anomaly detection in dynamic graphs underpins financial fraud analysis, intrusion detection, and platform integrity, where automated decisions require human-interpretable justifications.
arXiv:2606. 17555v1 Announce Type: cross Abstract: Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements.
arXiv:2609.14234v1 Announce Type: cross Abstract: Financial fraud in corporate transaction networks has grown more coordinated and harder to detect with rule-based engines and with classical learning...
arXiv:2609.08247v1 Announce Type: new Abstract: Wallet reputation scores decide who receives an airdrop, who can borrow, and who enters an allowlist across decentralised finance. They almost always b...
arXiv:2610.27107v1 Announce Type: new Abstract: Credit card fraud is a significant problem for financial institutions, resulting in substantial monetary loss each year. Fraud patterns and methods are...