Quarterly Report

Q2 2026 Fraud Infrastructure Report

A tighter operator summary of what serious fintech teams should pressure-test this quarter: vendor evaluation discipline, payout and setup risk, latency tails, explainability for ops, and the metrics that still fool buyers in fraud.

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Vendor evaluation

Latency tails

Explainability for ops

Payout + setup risk

Inside this report

What serious buyers are still getting wrong when they evaluate fraud vendors
Why payout and first-time-payee exposure deserves separate thinking from purchase fraud
Why average latency claims are still misleading without tail visibility
Which essays on the site go deeper on each issue

1. Fraud vendor buying is still too demo-driven

The most common evaluation failure is still trusting a polished product demo and a checklist. What actually matters is what a model does on your traffic, how stable the tails are, how usable the reasons are for analysts, and whether the vendor can survive a real shadow test without hiding behind average-case metrics.

Read the shadow-test guide Read the API buying guide

2. Latency claims still hide the part that hurts ops

A vendor can quote a good average latency number and still blow up your approval path in the tail. The useful question is not “what is the average?” It is “what happens at P95 and P99 when the dependencies and scoring path are under pressure?”

Read the latency article

3. Explainability still gets sold as a dashboard, not an ops tool

Analysts do not need a portfolio-level AI story. They need decision-level reasons that help them move faster on real cases. If the reasons are vague or disconnected from review work, the model may still be impressive and the system will still be operationally heavy.

Read the explainability article

4. Event-centric scoring still misses payout and setup exposure

Some of the most expensive fraud does not look obviously bad at the transaction level. The risk sits in the setup around it: first-time payees, payout timing, release flows, account changes, and the context around money movement.

Read the payout-risk article

5. Strong-looking offline metrics still hide rare-event failure

Fraud teams still get sold on offline metrics that look strong but collapse under real operating thresholds. Rare-event modeling, false-positive load, and queue behavior matter far more than a polished AUC slide.

Read the rare-event modeling article

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