FraudBench introduces protocol-sensitive evaluation framework for assessing adversarial robustness of ML fraud-detection systems in financial risk assessment, accounting for domain constraints and ...
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Researchers introduce findr, a machine learning framework that combines predictive accuracy with interpretable credit risk decisions and fairness constraints for regulatory compliance.
Report reveals 40% of employees are leveraging AI tools—often company-provided—to create fake expense receipts, highlighting a new fraud vector in workplace finance controls.
Research paper proposes multi-vector AI agent for banks to detect both signature-based fraud (card attacks, account takeover) and behavioral financial crime (structuring, BEC, money laundering) sim...
Capterra survey of 500 US accounting managers finds 89% see ROI from AI tools in chatbots, data entry, and fraud detection, yet 52% experienced recent data breaches and most lack clear AI data-hand...
Academic study reveals personal AI agents with elevated privileges—like OpenClaw—vulnerable to prompt injection attacks that could leak credentials or redirect financial transactions, exposing gaps...