Use cases· Last updated 2026-09-21

Fraud confidence thresholds 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 confidence thresholds slice of the fraud language overlay pack. Intent: apply the Jev (TypeSafe System One) decision model to fraud language overlay confidence thresholds. Primary search language: Fraud Jev confidence thresholds. 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. Treat floors as production gates and price the false-reject cost — official 0.5/0.9 sketches are illustrations.

Fraud use-case context

Thresholds turn fraud language overlay answers into act / review / abstain. They are product policy, not a hyperparameter TypeSafe ships. Official 0.5 / 0.9 sketches are illustrations. This slice also carries the false-reject discussion: over-gating fraud language overlay hides calibration.

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

Confidence Thresholds inputs

You need (1) pinned answers on a frozen contract and (2) labels for hold / follow_score / review gold from fraud analysts, plus social-eng gold. State 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

FLOORS = {
    "log_only": 0.50,      # illustrations — replace
    "hold_payout": 0.88,
}
NOUL_TAU = 0.75  # for social_eng

def allow(ans, action):
    return ans.confidence >= FLOORS[action]

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

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.

Band around 0.5 on social_eng always reviews. Do not copy 0.75 onto Choice confidence.

Evaluation and rollout notes

• Missed planted social-eng chats
• False holds (customer pain) at your τ
• Disagreement rate vs vendor band — investigate, do not auto-average

Fit loop: pin jev-1.13.0 → replay → plot error vs confidence → pick floors where auto-act error ≤ your SLA. 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 hold_payout use 0.9 everywhere? No. Over-gating hides calibration and dumps the queue on humans. Fit per action.

Can I reuse a Noul τ as Choice confidence? No. Jaggedness: they are not interchangeable. See confidence.

Where is the rest of the Fraud pack? Start with Fraud decision workflow and Fraud human handoff. 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.