Fraud & AML Monitoring · Use case

Score transactions in real time

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Short answer

Every payment is scored before it completes, using rules and models on transaction, customer and device data. The decision — approve, step up, hold or decline — has to arrive inside your payment flow's time limit, so latency and timeout behaviour matter as much as detection.

What this use case is

Every payment is scored before it completes, using rules and models on transaction, customer and device data. The decision — approve, step up, hold or decline — has to arrive inside your payment flow's time limit, so latency and timeout behaviour matter as much as detection.

Who builds this

Payment institutions
Neobanks
Card issuers
PSPs
Marketplaces

What your customer sees

  1. 1Most payments go straight through
  2. 2A risky one asks for confirmation or is held briefly
  3. 3A warning appears for a likely scam payee

How Fraud & AML Monitoring solves it

1

Payment event sent to the scoring engine

Payment event sent to the scoring engine

2

Enriched with customer, device and history

Enriched with customer, device and history

3

Rules and models return a score and reasons

Rules and models return a score and reasons

4

Decision applied: approve, step up, hold, decline

Decision applied: approve, step up, hold, decline

5

Held payments reviewed

Held payments reviewed

6

Outcomes fed back into models

Outcomes fed back into models

Want to have

  1. Low-latency API with defined timeout behaviour
  2. Rules plus models
  3. Reason codes
  4. Step-up integration
  5. Review queue
  6. Back-testing

Optional

  1. Scam and payee-risk signals
  2. Consortium data
  3. Champion–challenger testing of rules

Design decisions

Timeout behaviour

Fail open
Fail closed

customer experience versus loss exposure.

Ownership

Vendor-managed models
Your team tunes

less work versus control.

What to put in your RFP

Latency and availability commitments

  • Latency and availability commitments

Scoring

  • rules
  • models
  • reasons

Actions

  • step-up
  • holds
  • declines

Tooling

  • review
  • testing
  • back-tests

Commercials

  • per transaction pricing
Add these to an RFP

How to evaluate providers for this use case

  1. 1Back-test results
  2. 2Latency under load
  3. 3Analyst control
  4. 4Explainability

Pitfalls

  • No defined behaviour on timeout
  • Rules nobody can explain six months later
  • Ignoring scams where the customer authorises the payment

What providers will ask you

  1. Transaction volumes and types
  2. Loss data
  3. Current rules
  4. Payment flow timing limits

Other Finlane categories that cover parts of this

This use case is solved mainly with Fraud & AML Monitoring. These categories cover specific parts of it and can be tendered alongside it.

Payment Processing & Acquiring

Fraud tools built into your PSP

Chargebacks & Disputes

Disputes after fraud

Card Issuing

Authorisation-time controls on cards

Frequently asked questions

Other Fraud & AML Monitoring use cases

Prevent account takeover at login

Combine device, behavioural and network signals to challenge or block suspicious sign-ins.

Run transaction monitoring for AML

Detect structuring, mule networks and typology-based patterns with tunable rules and clear audit trails.

Manage cases and SAR reporting

Investigate alerts in a shared case manager and export SAR/STR reports to regulators.

Scope score transactions in real time with Fraud & AML Monitoring providers

Scope this use case