← Back to all news
arXiv AI By Joseph Walusimbi, Joshua Benjamin Ssentongo

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

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

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
More like this →
arXiv AI
Aug 20

FraudBench: Stress-Testing Policy-Grounded Banking Agents Against Adaptive Fraud

FraudBench is a new benchmark that tests policy‑grounded banking conversational agents against adaptive fraud scenarios. It uses a dual‑control framework and a 698‑document internal policy corpus, presenting 150 adversarial scenarios (107 public, 43 held‑out) that require agents to manage mutable account state and tool access while preventing identity, authorization, and trust manipulation. Preliminary results on four agents show attack‑security rates between 49% and 65%, highlighting weaknesses in money‑mule and first‑party fraud detection.

By Dheeraj Mohandas Pai, Lu Xian
agentsbenchmarkssafety
More like this →
arXiv AI
Sep 30

Agentic Commerce Bench: Measuring Fraud Detection for Agents That Spend Money

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
agentsbenchmarkssafety
More like this →