Fraud audit trail 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 audit trail slice of the fraud language overlay pack. Intent: apply the Jev (TypeSafe System One) decision model to fraud language overlay audit trail. Primary search language: Fraud Jev audit trail. 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
An audit trail for fraud language overlay is a decision trace: replayable inputs, typed answers, floors, and the action the fraud overlay took. It is not a chat log and not a clone of a SIEM product page.
Hub: Use cases. Compare, when the other tool is the real job: fraud scores.
Audit Trail inputs
Persist the filtered payload (the contract), not whatever arrived at the edge:
{
"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." }
}Redact secrets before the object hits cold storage.
Decision signals and actions
Minimum fields:
- event.id
- vendor score id / band
- Jev answers + pin
- blend / rule version
- payout hold flag
Also store usage.input_tokens (vendor meter) and the full probabilities map — argmax-only logs cannot explain a close social_eng.
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
If you cannot explain holding a payout or unblocking a withdrawal from the trace, you are not ready to auto-act. 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
Traces are the eval warehouse. Replay against hold / follow_score / review gold from fraud analysts, plus social-eng gold after criteria or alias changes. 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 | you are here |
| How it breaks | failure modes |
| Labeled replay | evaluation |
| Shadow → canary | production rollout |
FAQ
Is the HTTP log enough? No. Persist the filtered state, full probabilities, floors, and downstream action as a decision trace.
May I log raw secrets? Redact in code. Jev will not be your DLP layer.
Where is the rest of the Fraud pack? Start with Fraud human handoff and Fraud evaluation. 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.