arXiv:2607. 17586v1 Announce Type: cross Abstract: Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data.
By Yuge Zhang, Yuanxing Zhang, Yichao Jin, Khairul Amsyar Mohd Razis, Nicholas Qi An Choo, Kai Yin Anders Wong, Xinyan Tang, Kenneth Zhu Ke, Wee Keong Dennis Lee, Jingyuan Zhao
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
By Rahil Sharma
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.
By Joseph Walusimbi, Joshua Benjamin Ssentongo
The paper explores a hybrid approach that combines traditional Journal Entry Tests (JETs) with machine learning techniques to enhance anomaly detection in general ledger data. It presents specialized models designed to improve the accuracy and validity of detected anomalies, thereby aiming to reduce false positives and increase audit efficiency. Experiments are conducted using synthetic data that includes both normal and anomalous journal entries.
By Jan Gronewald, Alexander Michael Rombach, Sebastian Stephan, Peter Fettke
arXiv:2608. 07471v1 Announce Type: cross Abstract: This study considers the task of applying artificial intelligence to recognize bank fraud.
By Bohdan Mytnyk, Oleksandr Tkachyk, Nataliya Shakhovska, Solomiia Fedushko, Yuriy Syerov
arXiv:2608. 00566v1 Announce Type: new Abstract: Post-hoc model explainers such as LIME, SHAP, and Integrated Gradients are widely deployed to audit models in high-stakes sensitive domains, including finance, healthcare, and social welfare.
By Niraj Kumar, Harsh Kasyap