The paper introduces an autonomous AI security agent designed to detect multi‑vector fraud and anti‑money‑laundering (AML) threats in both retail and corporate banking. It operates across two parallel event streams—transactions and sessions—using a fusion of LSTM behaviour models, statistical velocity monitors, and graph‑based account‑counterparty analysis. Experiments on a synthetic dataset show the agent outperforms rule‑based and LSTM‑only baselines, achieving F1 scores of 0.787 for transactions and 0.867 for sessions, while also providing rapid, low‑latency responses and supporting customer verification and analyst assistance.
By Joseph Walusimbi, Joshua Benjamin Ssentongo
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.
By Anupa Lodhi
The paper introduces the Agentic Commerce Bench (ACB), a benchmark for measuring fraud in AI agents that autonomously spend money. It presents a taxonomy of agentic commerce fraud, a dataset of twenty fraud classes derived from real production data, and an open‑source detector stack called gordonguard for auditing and replaying hostile counterparties. The study shows that current reasoning layers and security scanners perform poorly on many classes, highlighting the need for better detection mechanisms.
By Ankit Srivastava, Debjyoti Paul
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:2606. 08146v1 Announce Type: new Abstract: Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers.
By Yichen Chen, Siying Li, Yuhang Liang, Lijun Wang, Renyang Liu
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