Fraud & AML Monitoring · Use case
Prevent account takeover at login
Short answer
Account takeover starts with a login that looks legitimate. Monitoring providers combine device, behavioural and network signals to score each login and sensitive action, so you can allow, challenge or block without adding friction for genuine customers.
What this use case is
Account takeover starts with a login that looks legitimate. Monitoring providers combine device, behavioural and network signals to score each login and sensitive action, so you can allow, challenge or block without adding friction for genuine customers.
Who builds this
What your customer sees
- 1Logs in normally
- 2Occasionally gets an extra check on a new device
- 3Gets alerted if someone else tries
How Fraud & AML Monitoring solves it
Login or sensitive action captured with device and behaviour data
Login or sensitive action captured with device and behaviour data
Risk scored in real time
Risk scored in real time
Low risk allowed
Low risk allowed
Medium risk challenged with step-up authentication
Medium risk challenged with step-up authentication
High risk blocked and alerted
High risk blocked and alerted
Outcomes fed back to improve models
Outcomes fed back to improve models
Want to have
- Device fingerprinting
- Behavioural signals
- Real-time scoring API
- Configurable responses
- Step-up integration
- Alerting
- Feedback loop
Optional
- Consortium device intelligence
- Session monitoring after login
- Bot detection
Design decisions
Response
loss prevention versus friction.
Coverage
simplicity versus protection against session hijacking.
What to put in your RFP
Signals
- device
- behaviour
- network
Decisions
- latency
- responses
- step-up
Operations
- alerts
- investigation
Data
- privacy
- retention
Commercials
- per event pricing
How to evaluate providers for this use case
- 1Detection on your historical incidents
- 2Friction for genuine users
- 3Latency
- 4Privacy approach
Pitfalls
- Protecting login but not payee changes
- Over-challenging loyal customers
- Behavioural data collected without the right notices
What providers will ask you
- Monthly active users and logins
- Known attack patterns
- Authentication setup
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.
Frequently asked questions
Other Fraud & AML Monitoring use cases
Run every payment through a scoring engine with rules and ML, and step-up risky flows to SCA or review.
Run transaction monitoring for AMLDetect structuring, mule networks and typology-based patterns with tunable rules and clear audit trails.
Manage cases and SAR reportingInvestigate alerts in a shared case manager and export SAR/STR reports to regulators.