Fraud human handoff 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 human handoff slice of the fraud language overlay pack. Intent: apply the Jev (TypeSafe System One) decision model to fraud language overlay human handoff. Primary search language: Fraud Jev human handoff. 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
Handoff is a first-class outcome for fraud language overlay, not a failure of Jev. When the fraud overlay cannot auto-act, a human sees a packed dispute or chat text + fraud-score summary — not a chat transcript. We do not ask Jev to write the reviewer essay.
Hub: Use cases. Compare, when the other tool is the real job: fraud scores.
Human Handoff inputs
Humans should see what the model saw (filtered), not the warehouse dump you correctly refused to POST:
{
"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." }
}Decision signals and actions
| Signal | Typical reason enum (you name it) |
|---|---|
| High $ rule in code | payments_hold |
| social_eng high | ato_queue |
| overlay other / low confidence | analyst |
| letter_fit Score very low + high vendor score | mismatch_review |
These are application outcomes next to HTTP 200, not invented TypeSafe status codes.
Send the reviewer:
- TXN id + chat as judged
- vendor band + amount bucket
- answers + overlay
- Action already applied (held or not)
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
Do not page humans on a single Score unless your conjunction says so. Pair with confidence thresholds. 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
Write the gold label back into the offline set. That is how floors move. 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 | you are here |
| What to persist | audit trail |
| How it breaks | failure modes |
| Labeled replay | evaluation |
| Shadow → canary | production rollout |
FAQ
Is handoff a Jev failure? No. It is a first-class outcome. Abstention is “no auto action”; handoff is the queue you send that case to (glossary).
Should Jev draft the reviewer note? No. Send structured answers. Generation is the wrong job.
Where is the rest of the Fraud pack? Start with Fraud confidence thresholds and Fraud audit trail. 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.