Ideas on fraud and fraud prevention
What we learned running fraud operations in the real world — detection, rules, metrics, and the total cost of fraud.
For Every $1 of Fraud, You Lose Almost $4 (and You Only Measure the First)
The number you report as the cost of fraud is 25%. The real bill is 3 to 4 times larger, and almost nobody adds it up.
Read → The Real Cost of Fraud · 7/8Rules, Lists, Velocities, Models, Graphs: The Problem Isn't Choosing, It's Combining Them
There's no Swiss army knife in fraud prevention. Combining the techniques well produces the quality data the engine needs to decide.
Read → The Real Cost of Fraud · 6/8Chargebacks Are the Thermometer, Not the Disease
Chargeback rate always reaches you late. What changes when you start thinking about fraud from the attacker's side.
Read → The Real Cost of Fraud · 5/8False Positives: The Damage Your Fraud System Does to Your Best Customers
False positives are the invisible cost of poorly calibrated fraud prevention. How to measure them without sacrificing real fraud detection.
Read → The Real Cost of Fraud · 4/8Scaling the Analyst Team Doesn't Scale
75% of the fraud analyst's day happens outside the fraud product. What has to change for every team hour to land where it matters.
Read → The Real Cost of Fraud · 3/8Your Fraud Stack Has 5 Vendors and None of Them Talk to Each Other
Five fraud vendors that don't talk to each other cost you more than the fraud itself. What a truly integrated stack actually looks like.
Read → The Real Cost of Fraud · 2/8If the Attacker Uses AI, Your Static Engine Has Already Lost
Having a trained model isn't the same as having an engine that learns from the adversary at the pace the adversary learns from you.
Read → The Real Cost of Fraud · 1/8The Invisible Cost of Slow Response
The lag between detecting a fraud pattern and blocking it in production is the metric that defines how much fraud actually costs you.
Read → Open Source · DataspotBuilding Dataspot: Lessons from Real-World Fraud Detection
How obsessing over fraud patterns led to an open-source tool that finds data concentrations — and why we released it to the community.
Read → Fraud detection · GraphsGraph Contamination in Fraud Detection: The Problem Nobody Talks About
Why graph-based fraud detection ends up hurting legitimate customers, and what the industry is missing about contamination by association.
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