Use cases· Last updated 2026-09-21

Fraud evidence collection 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 evidence collection slice of the fraud language overlay pack. Intent: apply the Jev (TypeSafe System One) decision model to fraud language overlay evidence collection. Primary search language: Fraud Jev evidence collection. 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. Fan-out extra atoms on one request; open a second HTTP call only for a new artifact, not the same state.

Fraud use-case context

Evidence collection for fraud language overlay happens before POST /v1/systemone. Jev does not browse your warehouse, retriever, or ESP. You gather the dispute or chat text + fraud-score summary facts, filter them, then ask snap questions. This slice is where fan-out cost math belongs: batch questions, do not re-send state.

Hub: Use cases. Compare, when the other tool is the real job: fraud scores.

Evidence Collection inputs

Collect:

• Vendor score band (not a request that Jev recompute it)
• The chat or dispute letter
• Reason code + coarse amount bucket (you bucketed)

Never send:

• Asking Jev to score the device graph
• Full raw IP/ASN tables as distractors
• Clear card data

Shape the payload like this once the gather step finishes:

{
  "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

Each evidence field should change a named answer:

social_eng + letter_fit + overlay in one call. Do not HTTP twice to “also” ask if the letter mentions a merchant name — add a Noul on the same request.

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 the gather step fails (empty dispute or chat text + fraud-score summary, redaction stripped everything, retriever empty), fail closed on holding a payout or unblocking a withdrawal. Do not invent evidence so Jev has something to say. 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

Your eval set should include thin-evidence cases, not only happy dispute or chat text + fraud-score summarys. Label hold / follow_score / review gold from fraud analysts, plus social-eng gold. 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

FAQ

Should evidence live in the question text? Put facts in state and point instructions at text.chat, event.reason_code, policy.social. Criteria stay stable so you can replay.

When do I split calls? social_eng + letter_fit + overlay in one call. Do not HTTP twice to “also” ask if the letter mentions a merchant name — add a Noul on the same request.

Where is the rest of the Fraud pack? Start with Fraud input contracts and Fraud decision workflow. 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.