Fraud failure modes 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 failure modes slice of the fraud language overlay pack. Intent: apply the Jev (TypeSafe System One) decision model to fraud language overlay failure modes. Primary search language: Fraud Jev failure modes. 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
Fraud language overlay breaks in product-specific ways. This page lists those modes so you can write tests — not a generic “AI can be wrong” essay, and not a rival limitations-page clone.
Hub: Use cases. Compare, when the other tool is the real job: fraud scores.
Failure Modes inputs
Many failures start as contract violations (distractors, missing dispute or chat text + fraud-score summary text). Canonical shape:
{
"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
- Replacing the fraud platform with a Noul (compare).
- Silent ensembles — official-style AutoResearch pattern is Jev as features you own, not a hidden add.
jev-1.13will not do reliable velocity math — keep counts in code.- Schema-safe
hold_payout≠ a law-enforcement finding. - Invented catch-rate claims.
HTTP vs application:
| You see | Class | Fraud move |
|---|---|---|
| 401 / 422 / 429 / 529 | Documented HTTP | Fix key/body or back off — errors |
| 200 + flat confidence or Noul ≈ 0.5 | Low confidence | Hold; do not unblock a payout on a Jev guess |
| Empty gather | Missing evidence | Skip Jev or ask “is enough information present?” |
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
Fail closed: do not unblock a payout on a Jev guess. Schema-safe answers are not factual correctness. 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 canary set should include each bullet above.
- Missed planted social-eng chats
- False holds (customer pain) at your τ
- Disagreement rate vs vendor band — investigate, do not auto-average
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 | you are here |
| Labeled replay | evaluation |
| Shadow → canary | production rollout |
FAQ
If the API returns 200, is the decision good? 200 only means the call parsed. Low confidence, Noul ≈ 0.5, or a policy miss are application failures.
Where do official weaknesses live? TypeSafe’s jev-1.13 jaggedness note — distractors, arithmetic, adversarial content. We do not invent more.
Where is the rest of the Fraud pack? Start with Fraud evaluation 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.
Sources
Public TypeSafe or adjacent documentation only. No private claims.