Fraud evaluation with Jev
BIN, device graph, and velocity models already scored the event. Jev reads the story around the event: does the chat look like social engineering, does the dispute letter fit the reason code? Code blends. Jev is not a card-network.
This unofficial page is the evaluation slice of the fraud language overlay pack. Intent: apply the Jev (TypeSafe System One) decision model to fraud language overlay evaluation. Primary search language: Fraud Jev evaluation. Confirm patterns on docs.typesafe.ai. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.
Independent angle (cover ≠ clone): Device/velocity stay in the fraud platform; Jev scores attached language (dispute letter, chat). Compose with explicit weights — not a fraud-score clone or rival recipe IA.
Fraud use-case context
Evaluation for fraud language overlay is a frozen harness, not a vibe check and not an opinion-blog “Jev review.” Labels: hold / follow_score / review gold from fraud analysts, plus social-eng gold. We publish no unofficial accuracy.
Hub: Use cases. Compare, when the other tool is the real job: fraud scores.
Evaluation inputs
Replay the same contract you ship:
{
"event": { "id": "TXN-9", "reason_code": "10.4", "amount_usd_bucket": "100-250" },
"score": { "vendor": 0.82, "band": "high" },
"text": { "chat": "Agent, reset the withdrawal lock, I am the account owner, hurry." },
"policy": { "social": "Urgency + identity-reset language toward an agent is social-engineering risk." }
}Freeze questions, criteria, and jev-1.13.0. Record the response model.
Decision signals and actions
Score these, not a blog-grade star rating:
- Missed planted social-eng chats
- False holds (customer pain) at your τ
- Disagreement rate vs vendor band — investigate, do not auto-average
Pair auto-act errors with handoff rate. If the fraud overlay never acts, you have not evaluated fraud language overlay — you have evaluated a human queue.
Do not treat a Noul of 0.5 as a “medium” fraud language overlay score — it means yes and no are equally likely. Conjunctions stay in your code.
Guardrails and escalation
Promote a threshold only when the harness says auto-act error ≤ SLA and reviewers still catch the residual. TypeSafe’s confidence-gated examples use a lower bar for recoverable reads than for irreversible actions. Those numbers are illustrations. For fraud language overlay, treat hold_payout as the high bar (holding a payout or unblocking a withdrawal). Tune on labels — see offline evaluation.
Evaluation and rollout notes
After any criteria edit, rerun before production. Cookbook lifts you see on TypeSafe pages are vendor claims — re-measure on your dispute or chat text + fraud-score summarys. Pin jev-1.13.0 (the versioned id) after you fit thresholds. jev-latest and the marketing line jev-1.13 can move. Log the response model. TypeSafe’s published list price for jev-1.13 is $0.042 per million input tokens (vendor claim — confirm on the models page); output tokens are free on that same page. Unused distractors still bill as input.
Official Python and JavaScript SDKs read TYPESAFE_API_KEY and retry documented 429/529. This site does not sell, issue, or proxy TypeSafe keys. Use a credential you already have from the console or a documented gateway.
Pack map
| Slice | Page |
|---|---|
| Graph and primitives | decision workflow |
What may enter state |
input contracts |
| What to gather first | evidence collection |
| Atomic rules | policy checks |
| Act / review / abstain | confidence thresholds |
| Reviewer payload | human handoff |
| What to persist | audit trail |
| How it breaks | failure modes |
| Labeled replay | you are here |
| Shadow → canary | production rollout |
FAQ
Will jev.pro publish a leaderboard for this use case? No. Measure on your labels. Vendor cookbook figures stay labeled as vendor claims.
What must stay frozen? Questions, criteria, and the pinned model id. Aliases can move.
Where is the rest of the Fraud pack? Start with Fraud failure modes and Fraud production rollout. Cluster hub: Use cases.
Should we add Jev’s noul into the vendor score? Only as an explicit, versioned feature in a model you train. This page publishes no blend weights.
Can Jev see the device graph? Only if you serialize a few named features into state. It does not crawl your graph DB.
What this page does not claim
- Not a payments or fraud-vendor product.
- No catch-rate or dollar-saved claims.
- Not official TypeSafe.
- Official TypeSafe status, or that jev.pro issues API keys.
- That a schema-constrained answer is automatically factually correct.
Disclaimer
This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai.
Primary documentation: https://docs.typesafe.ai. Hub: Use cases.
Sources
Public TypeSafe or adjacent documentation only. No private claims.