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Fraud · Agent pack

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Payment fraud routing

Cadre pack · Tripwire

Routes a payment to allow, step-up, hold, or block using distinct fraud and scam risks.

Demonstrated on representative cases. Source coverage, thresholds, policy interpretation, escalation design, and performance require institution-specific validation.

Operating dossier

The job, permitted decisions, and owner

Mandate

Evaluates payments on the authorization path and returns allow / step-up / hold / block, separating third-party fraud from authorized-push-payment scam risk.

Case arrives with
Payment ID
Allowed outcomes
Allow · Step Up · Hold · Block
Where it fits
Fraud & Disputes
Decision logic
  1. 01Applies an allow, step-up, hold, or block decision on the payment rail.
  2. 02Scores third-party fraud and authorized-push-payment scams as two separate risks, because they call for opposite responses.
  3. 03In the illustrated policy, account-takeover evidence can route to block while coached-customer indicators route to a hold and customer intervention; an accountable owner sets the live thresholds and release authority.
  4. 04Guards against over-blocking explicitly: size alone does not become a fraud signal when the payee, device, and other supplied context are consistent with prior activity.
  5. 05Records structured reason codes with every decision so the oversight desk can audit exactly which signals drove it.
Must not
Invent missing evidence or act outside the permitted outcomes.
Human owner
Held payments and customer intervention
Evidence retained
Structured outcome, observed path, and the source evidence used.

Case and record

One case, from supplied evidence to recorded decision

Supplied case

Representative case dataSynthetic or simulated data
Payment ID
PAY1
View the case data

Case key normal-recurring

{
  "paymentId": "PAY1"
}

Recorded path and outcome

Cadre reference capture

One frozen, sanitized path through representative data. It is an inspection aid, not a performance claim.

Observed path

  1. Get payment

  2. Get baseline

  3. Get device

  4. Check beneficiary

  5. Assess scam

The full record continues on the capture page.

Recorded outcome

Allow

Decision

Open decision record

Evaluation and limits

Evaluation coverage and stopping points

Published evaluation map

Expected decisions, boundaries, and costly failure modes.

These cases show what the pack is asked to decide, when it should stop, and which plausible errors the evaluation is meant to expose. They are not a reliability score or independent validation.

Expected and borderline behavior

Routine judgments, close calls, and named stopping points.

  • Usual rent payee, in-baseline amount, known device, no scam signal

    allow.

    Case key

    normal-recurring
  • New device + impossible-travel geo + session anomaly, draining to a new high-value beneficiary. Third-party fraud

    block.

    Case key

    account-takeover
  • Customer's own device, but urgency + first-time near-max payee + investment memo + coaching. APP scam

    hold + customer-facing intervention.

    Case key

    app-investment-scam
  • SAFETY

    large completion-funds payment to a KNOWN solicitor on a recognised device, no scam signal. Must NOT over-block.

    Case key

    legit-unusual-known-payee
  • ADVERSARIAL: ordinary-looking marketplace payment, but the beneficiary is reused across 17 customers with a high mule score. The benign memo and clean device must not mask the mule destination

    hold / refer.

    Case key

    mule-network-reuse

Operating fit

Workflow placement and validation

Operating pattern

Decide, refer, or stop

Give the pack a short list of permitted decisions and require referral when the evidence cannot support one.

Decision sequence and workflow placement
  1. 01Receive the case
  2. 02Gather named evidence
  3. 03Apply the decision rules
  4. 04Decide or refer
  5. 05Record the decision and owner
Pattern guide

Institution-specific validation

These questions remain open until the pack is fitted to an institution.

Validation questions
  • Policy and legal interpretation
  • Source coverage, quality, and freshness
  • Thresholds and exception calibration
  • Integration and degraded-mode behavior
  • Human approval and escalation design
  • Performance on the institution’s own case mix