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.
Category Fraud Analytics & Risk Controls
Fraud analytics, real-time detection, graph analytics, KPIs, risk controls, model monitoring, and fraud operations measurement.
Fraud Analytics KPIs for Banking Teams: Measuring Risk Before Losses Become Reports
A practical guide to fraud analytics KPIs for banks, covering loss, false positives, APP scams, mule risk, instant payments, and AI model governance.
Graph Analytics ATO Fraud: From ATLAS Research to Systems
Graph analytics ATO fraud detection models account takeover as a network problem rather than a single risky login. Traditionally, fraud systems evaluate sessions independently. However, modern attackers operate in coordinated campaigns. They reuse credentials, devices, IP addresses, and automation frameworks…
Event-Driven Fraud Detection: How Kafka and Real-Time Streaming Are Transforming Alerts
1) From Batch Reports to Real-Time Defense For years, banks relied on overnight jobs, static dashboards, and manual reviews to detect fraud. However, in an instant-payment world, by the time a batch job finishes, the fraud has often already completed.…
