Hub Overview
Financial crime is now a data, payments, identity, and customer-intent problem.
EdEconomy tracks financial crime through the lens of banking fraud, payment scams, AI fraud detection, mule networks, synthetic identity, account takeover, and real-time analytics.
This page is a curated guide for readers who want the strongest EdEconomy financial crime resources without sorting through a default category archive.
Core Financial Crime Paths
Start with the major risk clusters.
Banking Fraud
APP fraud, bank scam prevention, instant payment risk, mule accounts, account takeover, and financial crime analytics.
AI Fraud Detection
AI-driven fraud detection, behavioral signals, graph analytics, scam automation, and real-time model governance.
Resources
Practical checklists, hubs, and reader tools for fraud analysts, payments teams, and financial crime readers.
Payment Scams and Instant Payments
- Authorized Push Payment Fraud: Why Banks Struggle to Stop APP Scams
- Fraud Data Quality: Why Bad Labels Break AI Models
- Fraud Analytics KPIs for Banking Teams
- Bank Scam Prevention: Field Guide for Fraud Analysts
- FedNow Network Intelligence API and Bank Risk
- FedNow Fraud Detection: Real-Time Risk on Instant Payments
AI, Identity, and Detection
- AI-Generated Identity Fraud in Banking
- AI in Fraud Detection for U.S. Banking
- AI vs. AI in Banking Fraud
- AI Voice Cloning Scams
- Synthetic Identity Fraud: Detection Signals for Financial Institutions
- Account Takeover Fraud: Prevention Strategies and Top Tools
- First-Party Fraud in Banking
- Graph Analytics ATO Fraud
- Event-Driven Fraud Detection
Archives and Updates
Need chronological category archives?
The curated hubs above are the best starting points. Use the category archives when you want to browse articles by publication order inside a specific editorial shelf.
