arXiv AI June 17, 2026 By Joseph Walusimbi, Joshua Benjamin Ssentongo

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.

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

agents

arXiv Machine Learning

Jun 25

Multi-Stream Temporal Fusion for Financial Fraud Detection

arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.

By Mohammadamin Dashti Moghaddam, Nick Sciarrilli

llms benchmarks safety

arXiv AI

Jul 23

Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience

arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.

By Mariam Zakaria Moussa Ali

benchmarks safety

arXiv AI

Jul 16

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.

By Anupa Lodhi

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 Machine Learning

1d ago

Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework

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.

By Emmanuel Nahimana, Ya\'e Ulrich Gaba

benchmarks

arXiv AI

Jun 26

TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

arXiv:2604. 17420v2 Announce Type: replace-cross Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring.

By Keyang Chen, Mingxuan Jiang, Yongsheng Zhao, Zeping Li, Zaiyuan Chen, Weiqi Luo, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Xihong Wu, Hongfeng Chai

benchmarks

Related stories

Multi-Stream Temporal Fusion for Financial Fraud Detection

Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience

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

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

Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework

TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering