Category
Fraud & AML Monitoring
Fraud and AML monitoring vendors combine device fingerprinting, behavioural signals, network intelligence and rules/ML to detect account takeover, payment fraud, mule activity and money laundering. Compare on model quality, rules engine, case management and integration into your payment stack.
In one minute
What it is
Systems that detect fraud and financial crime — account takeover, payment fraud, scams, mule activity, money laundering — using device, behavioural and transaction data.
What you get
Real-time scoring, rules and machine-learning models, alerts, case management and reporting support.
What you still own
Your risk appetite, rule and threshold decisions, investigations, customer contact and regulatory filings.
When you need it
As soon as money moves through your product — and before your partner or regulator asks.
How it works
Fraud prevention usually decides in real time; AML monitoring often works across time and networks. Some providers do both, others specialise.
Who does what
| Task | You | Provider | Licence holder |
|---|---|---|---|
| Risk appetite and thresholds | Owns | Advises | Sets minimums |
| Event and transaction data | Sends | Ingests | — |
| Real-time scoring | Acts on result | Scores | — |
| Rules and models | Tunes | Supplies and maintains | Reviews |
| Alert investigation | Performs | Supplies tooling | Oversees |
| Customer contact | Owns | — | — |
| Suspicious activity reports | Files or prepares | Supports export | Files (if licence holder) |
| Model governance | Documents | Explains models | Audits |
Risk appetite and thresholds
- You
- Owns
- Provider
- Advises
- Licence holder
- Sets minimums
Event and transaction data
- You
- Sends
- Provider
- Ingests
- Licence holder
- —
Real-time scoring
- You
- Acts on result
- Provider
- Scores
- Licence holder
- —
Rules and models
- You
- Tunes
- Provider
- Supplies and maintains
- Licence holder
- Reviews
Alert investigation
- You
- Performs
- Provider
- Supplies tooling
- Licence holder
- Oversees
Customer contact
- You
- Owns
- Provider
- —
- Licence holder
- —
Suspicious activity reports
- You
- Files or prepares
- Provider
- Supports export
- Licence holder
- Files (if licence holder)
Model governance
- You
- Documents
- Provider
- Explains models
- Licence holder
- Audits
Use cases
Prevent account takeover at login
Combine device, behavioural and network signals to challenge or block suspicious sign-ins.
Read the use caseScore transactions in real time
Run every payment through a scoring engine with rules and ML, and step-up risky flows to SCA or review.
Read the use caseRun transaction monitoring for AML
Detect structuring, mule networks and typology-based patterns with tunable rules and clear audit trails.
Read the use caseManage cases and SAR reporting
Investigate alerts in a shared case manager and export SAR/STR reports to regulators.
Read the use caseWhat to compare
Detection quality
Missed fraud and false positives both cost money
Ask: Can we run a back-test on our historical data?
Latency
Real-time decisions sit in the payment flow
Ask: What decision time do you commit to, and what happens on timeout?
Signals
Coverage of the attacks you face
Ask: Device, behavioural, network, consortium data — which are included?
Rules engine
Your team needs to act fast
Ask: Can analysts change rules without engineering, with testing before release?
Explainability
Needed for customers and regulators
Ask: Why did a model score this event high?
Case management
Daily working tool
Ask: Queues, linking, audit trail, four-eyes approval?
AML typologies
Laundering patterns differ by product
Ask: Which typologies are covered out of the box?
Reporting
Regulatory filings
Ask: Export formats for suspicious activity reports?
Data protection
Behavioural data is personal data
Ask: Processing locations, retention, consent requirements?
How providers charge
Per event or transaction scored
Tiered by volume
Per monitored customer
Common for AML monitoring
Platform subscription
Includes rules engine and case tools
Seats
For analysts using case management
Data add-ons
Consortium or device intelligence
Implementation
Integration and model tuning
Ask each provider for the all-in cost at your expected volume, for year one and year three.
Regulatory context
EU
- PSD2 requires transaction monitoring for strong customer authentication exemptions based on risk analysis
UK
- Mandatory reimbursement of authorised push payment fraud has made scam prevention a direct cost for payment firms
- Suspicious activity reports go to the UK Financial Intelligence Unit.
US
- Suspicious activity reporting and AML programme requirements under the Bank Secrecy Act.
Orientation only, not legal advice.
What providers will ask you
Have these ready before you publish.
- Products and payment types
- Event and transaction volumes
- Current fraud and loss levels
- Existing rules and tools
- Team size
- Licence holder requirements
- Data you can share
Red flags
- No back-test on your own data before signing
- Black-box scores with no explanation
- Rule changes that require the vendor
- Latency commitments without timeout behaviour
- Case management bolted on
How Finlane fits
Your RFP covers
- Products and events to monitor
- Fraud and AML scope
- Real-time requirements
- Signals and data
- Rules and models
- Case management
- Reporting
- Data protection
- Volumes and pricing
- 1Scope your monitoring requirements with the AI advisor
- 2Matching providers receive your brief
- 3Compare structured responses side by side
- 4Shortlist and run a back-test
Frequently asked questions
Related reading
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