arXiv AI July 16, 2026 By Anupa Lodhi

Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective

arXiv:2607. 13469v1 Announce Type: cross Abstract: The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 21

Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification

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

llms

arXiv AI

Jul 22

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.

By Rahil Sharma

llms agents

arXiv AI

Jun 17

An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts

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

agents

arXiv AI

Aug 11

Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

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 Machine Learning

Aug 4

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors

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

rag benchmarks safety

arXiv Machine Learning

Jun 16

Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance

arXiv:2606. 16663v1 Announce Type: new Abstract: Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk.

By Dara Goldar, Geir Kjetil Ferkingstad Sandve, Martin Jullum

Related stories

Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts

Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors

Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance