Comparisons· Last updated 2026-09-21

Jev versus fraud scores: a practical comparison

A fraud score (device graph, velocity, card BIN models) is trained on events. Jev does not see your device graph unless you put features into state. It can judge a dispute letter or chat that accompanies the event.

Independent. Not a fraud-vendor teardown. docs.typesafe.ai. No keys.

Comparison scope

Chargeback reason codes, BIN, IP, and velocity stay in the fraud stack. “Does this chat sound like social-engineering the agent?” is Jev.

Criteria that decide the architecture

Decision quality and control

Do not add Jev probabilities into the fraud score without a measured ensemble. Official AutoResearch pattern is: Jev as features for a downstream model you own.

Integration trade-offs

Fraud score first (fail closed). Jev on attached text. Combine with explicit weights. Review queue on disagreement.

TypeSafe’s public models page lists jev-1.13 at $0.042 per million input tokens with output tokens free — a vendor claim, not a jev.pro measurement. Confirm on the models page before you bid.

When each approach fits

Prefer Jev when

• Text accompanies the payment event

Prefer Fraud scores when

• The decision is device, BIN, or velocity

Fraud platform + language Jev + rules on amount. Humans on high $.

What this page does not claim

• No fraud-catch-rate claims.

• Not a payments product.

• Schema-safe output is not the same as factual correctness.

• No independent bake-off numbers live here.

FAQ

Can Jev replace Sift/Forter? No.

Should I send full PAN in state? Never. PCI stays in the vault. Minimize.

Disclaimer

This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai. Never treat jev.pro as TypeSafe official documentation. We do not sell, issue, or proxy API keys.

Hub: Comparisons. Siblings: vs risk rules, vs kyc vendors, glossary risk band. Canonical: https://docs.typesafe.ai.