ACH Fraud Disputes: Regulation E vs. Nacha Rules
Regulation E and Nacha rules can apply to the same ACH dispute, but they answer different questions. This guide explains timelines, WSUDs, returns, reversals, KPIs, KRIs, and dashboard design.
Tag banking fraud
Ghost Students, Real Money: How Banks Can Detect Federal Student Aid Fraud
FinCEN’s 2026 alert shows why federal student aid fraud is a banking problem. This guide explains the ACH, account, device, mule, and graph signals financial institutions can use.
Section 314(b) Fraud Information Sharing: A Practical Guide for Banks
FinCEN's 2026 guidance clarifies how banks can use Section 314(b) information sharing for suspected fraud. This guide explains the workflow, controls, analytics, governance, and privacy considerations.
Receiver Risk Scoring in Banking: Signals, Models, and Pre-Payment Decisions
Receiver risk scoring helps banks assess whether a destination account, its transaction behavior, and its surrounding network create enough risk to change a payment decision before funds move.
Fraud KRI Governance in Banking: Risk Appetite, Escalation, and Executive Reporting
Fraud KRI governance turns fraud metrics into decisions by connecting risk appetite, ownership, escalation, remediation, data confidence, and executive reporting.
Fraud Model KRIs in Banking: Drift, False Positives, and Alert Quality
Fraud model KRIs help banks monitor drift, false positives, alert quality, rules, labels, warnings, and AI-assisted controls before losses rise.
Fraud Risk KRIs in Banking: Early Warning Indicators Before Losses Become KPIs
Fraud risk KRIs help banks detect rising scam, mule, account takeover, synthetic identity, ACH, and instant-payment exposure before losses become KPIs.
Operational Fraud KRIs in Banking: Early Warnings Before Control Breakdowns
Operational fraud KRIs help banks detect alert backlogs, SLA breaches, queue aging, escalation delays, QA defects, and control stress before losses spike.
Human-in-the-Loop AI Fraud Detection in Banking: Why Analysts Still Matter
AI fraud detection in banking works best when models, rules, graph signals, case evidence, and human analysts operate inside a governed feedback loop.
Fraud Data Quality: Why Bad Labels Break AI Models
Bad fraud labels can weaken AI models. Learn how banks can improve dispositions, feedback loops, model governance, and fraud analytics.
