Hub Overview
Banking fraud is now an instant-payment, AI, data, and customer-experience problem.
Banking fraud is changing quickly as instant payments, AI-generated scams, mule networks, synthetic identities, and account takeover tactics collide. EdEconomy tracks those changes for readers who need practical analysis rather than hype.
This hub collects EdEconomy’s best explainers, field guides, and practical resources on banking fraud, financial crime analytics, payment scams, and AI-driven fraud detection.
Newest Guide
Human-in-the-Loop AI Fraud Detection in Banking
How banks can use AI for evidence assembly, triage, summaries, graph context, and feedback loops while keeping analysts accountable for sensitive fraud decisions.
Start Here
Core banking fraud guides.
Human-in-the-Loop AI Fraud Detection
AI-supported fraud investigation with analyst accountability, evidence provenance, model feedback, override governance, and case quality controls.
Agentic AI Fraud in Banking
Autonomous scam agents, journey-level fraud signals, warning overrides, customer coaching, AI governance, and payment-risk KPIs.
AI-Generated Identity Fraud
Track deepfake IDs, synthetic documents, onboarding risk, KYC lifecycle controls, and mule account links.
Money Mule Detection
Detect mule accounts through receiver-side signals, rapid funds-out behavior, graph links, AML handoffs, and practical fraud KPIs.
Fraud Analytics KPIs
Measure risk before losses become reports: exposure, detection quality, false positives, friction, workflow, and AI model governance.
Authorized Push Payment Fraud
Why APP scams are hard for banks: the customer sends the payment, but the decision may have been engineered.
Payee Verification and Recipient Intelligence
Use recipient-side signals, name matching, mule indicators, and warning outcomes to evaluate APP fraud risk before payment release.
Bank Scam Prevention
A field guide for fraud analysts reviewing scam patterns, warnings, escalation cues, and prevention controls.
AI Fraud Detection Hub
A guided path through AI fraud detection, scam analytics, behavioral signals, graph analytics, and instant-payment risk.
Related Topic Hub
Fraud KRI Series
Fraud KRIs for banking teams.
Use this EdEconomy series to connect fraud operations, fraud exposure, model-control health, and governance reporting into one early-warning framework.
Fraud KRIs in Banking
The full hub for operations, exposure, models, controls, risk appetite, escalation, and executive reporting.
Operational Fraud KRIs
Backlogs, SLAs, queues, staffing pressure, QA, handoffs, and control stress.
Fraud Risk KRIs
Scams, mule risk, account takeover, synthetic identity, ACH, instant payments, and customer harm.
Fraud Model KRIs
Model drift, false positives, alert quality, rule effectiveness, labels, warnings, and AI-assisted controls.
Fraud KRI Governance
Risk appetite, ownership, thresholds, escalation, remediation, taxonomy, data lineage, and board reporting.
AI Fraud Detection
Explore how banks use AI, behavioral analytics, graph signals, and real-time decisioning to detect fraud while criminals use AI to scale scams.
Instant Payments and APP Fraud
Faster payment rails shrink the time banks have to detect scams. Authorized push payment fraud is especially difficult because the real customer may approve the transfer after being manipulated by a scammer.
Identity, Mule, and Account Risk
- AI-Generated Identity Fraud in Banking: Why KYC Must Become a Lifecycle Control
- Human-in-the-Loop AI Fraud Detection in Banking: Why Analysts Still Matter
- Money Mule Detection in Banking: Signals, Controls, and Analytics
- First-Party Fraud in Banking: The Hidden Threat in Plain Sight
- Synthetic Identity Fraud: A Threat to Financial Institutions
- Account Takeover Fraud: Prevention Strategies and Top Tools
- Graph Analytics ATO Fraud: From ATLAS Research to Systems
- Event-Driven Fraud Detection: Kafka and Real-Time Analytics
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