When fraud losses hit £1.28 billion in a single year, and criminals operate across interconnected networks, having separate fraud and AML teams can create exploitable gaps. This checklist guides you through the operational steps needed to integrate fraud and financial crime prevention into a unified detection framework.
Use this to audit your current state and identify specific integration gaps. Each item defines what "done" looks like so you can assess whether your controls are truly connected or just running in parallel.
Prerequisites
Before starting this checklist, ensure you have:
- Executive support for collaboration between fraud and AML teams
- Access to transaction monitoring, fraud detection, and case management systems
- Authority to review and modify data-sharing protocols between departments
- A working understanding of your institution's current fraud typology landscape, particularly Authorised Push Payment (APP) fraud patterns
Checklist Items
1. Establish a unified case escalation protocol
Your fraud team identifies a suspicious payment pattern. Does that information reach your AML investigators, or does it stop at a closed fraud case?
☐ Document a formal escalation path that routes fraud cases with potential money laundering indicators to AML review
☐ Define specific triggers: mule account indicators, structuring patterns, cross-border movement following fraud losses
☐ Set response time requirements (recommend 24-hour acknowledgment for high-risk referrals)
Good looks like: A fraud analyst reviewing an APP scam victim can flag the receiving account for AML investigation without manual email chains or case re-entry.
2. Create shared access to transaction history
Criminals don't respect departmental boundaries. Your fraud team sees the outbound scam payment. Your AML team sees the inbound deposit that preceded it. Neither sees the full picture.
☐ Grant fraud analysts read access to AML case management systems
☐ Grant AML investigators read access to fraud alert queues and case notes
☐ Implement a unified customer view that displays fraud alerts, AML risk ratings, and transaction monitoring hits in a single interface
Good looks like: An investigator reviewing a Suspicious Activity Report can see that the same customer filed a fraud claim three months ago without switching systems.
3. Align your customer risk rating methodology
If your fraud team rates a customer high-risk based on behavioral signals but your AML team maintains a low-risk rating based on static KYC data, you're creating blind spots.
☐ Incorporate fraud indicators into Customer Risk Ratings (account takeover attempts, reported scam contact, unusual payment patterns)
☐ Trigger automatic risk rating reviews when fraud events occur
☐ Document how fraud events influence ongoing due diligence requirements
Good looks like: A customer who falls victim to a romance scam automatically moves to enhanced monitoring for potential money mule activity, even if their initial KYC profile was low-risk.
4. Build cross-trained investigation capacity
Fraud analysts understand social engineering tactics. AML investigators understand layering and integration. Few understand both.
☐ Deliver quarterly cross-training sessions where fraud teams present typologies to AML staff and vice versa
☐ Rotate investigators between fraud and AML desks for 30-day shadowing assignments
☐ Create joint investigation teams for high-value cases involving both fraud and money laundering indicators
Good looks like: Your fraud team can identify when an APP scam victim's account is being used as a mule account without waiting for AML to discover it independently.
5. Implement consortium intelligence sharing
Your institution sees one piece of a criminal network. Other institutions see different pieces. Without information sharing, the network remains invisible.
☐ Join a financial crime consortium that enables cross-institution intelligence sharing
☐ Configure your systems to query consortium data during fraud and AML investigations
☐ Establish protocols for contributing your institution's fraud and mule account intelligence back to the consortium
Good looks like: When you identify a mule account, you can immediately see if other institutions have flagged related accounts, revealing the full network structure.
6. Harmonize your technology stack
If your fraud detection platform can't communicate with your transaction monitoring system, integration is theoretical, not operational.
☐ Audit your current technology architecture for data silos and integration gaps
☐ Implement API connections or data feeds between fraud and AML platforms
☐ Where full integration isn't possible, create automated alert feeds that push critical fraud events into AML systems and vice versa
Good looks like: A high-risk transaction monitoring alert automatically enriches with fraud case history, device intelligence, and behavioral analytics without manual lookup.
7. Define joint performance metrics
What gets measured gets managed. If fraud teams are measured on fraud loss reduction and AML teams are measured on SAR quality, neither is incentivized to collaborate.
☐ Create shared KPIs: time from fraud detection to mule account identification, percentage of SARs that incorporate fraud intelligence, cross-referral conversion rates
☐ Report these metrics to executive leadership monthly
☐ Tie compensation or performance reviews to collaboration outcomes, not just departmental metrics
Good looks like: Your quarterly compliance report includes a metric tracking how many fraud investigations led to AML escalations and subsequent Suspicious Activity Reports.
8. Document AI-assisted fraud patterns
Criminals are using artificial intelligence to scale operations and personalize attacks. Your detection methods need to account for this evolution.
☐ Catalog fraud typologies that show signs of AI assistance (mass personalization, rapid iteration, sophisticated impersonation)
☐ Update transaction monitoring rules to detect AI-scaled fraud patterns (high-volume low-value scams, coordinated multi-account activity)
☐ Brief your investigation teams on AI-driven social engineering tactics quarterly
Good looks like: Your analysts can distinguish between traditional phishing and AI-generated deepfake voice scams, and your systems flag the behavioral patterns unique to each.
Common Mistakes
Treating integration as a technology project alone. You can implement the most sophisticated unified platform available, but if your fraud and AML teams don't talk to each other, don't share case context, and don't understand each other's workflows, the technology won't deliver value. Integration is organizational first, technological second.
Waiting for perfect data before sharing intelligence. Fraud investigations often begin with incomplete information. If you require definitive proof before escalating to AML, you'll miss the window where intervention is possible. Build protocols that allow for provisional escalations based on reasonable suspicion.
Ignoring the psychological impact on customers. Fraud victims experience anxiety, embarrassment, and loss of trust that extends beyond financial recovery. Your integrated approach should include customer communication protocols that acknowledge this impact and provide support resources, not just reimbursement procedures.
Assuming your current risk ratings capture fraud exposure. Many Customer Risk Profiles focus on KYC data and transaction patterns but ignore fraud victimization as a risk indicator. A customer who falls for one scam is statistically more likely to be targeted again or recruited as a mule.
Next Steps
Start with items 1, 2, and 7. Establishing escalation protocols, creating shared system access, and defining joint metrics will reveal your biggest integration gaps and build the foundation for deeper collaboration.
Within 90 days, you should be able to answer: How many fraud cases did we escalate to AML last quarter? How many resulted in Suspicious Activity Reports? Where did cases fall through the cracks?
Those answers will tell you whether you're genuinely integrated or just running parallel operations with occasional handoffs.
