Anti Fraud Manager

Overview

Why This Matters

Why Deriv

• We’re not theorising about AI in fraud detection.
• We’re running it in production.
• Dozens of fraud detection models already running continuously across payment channels — monitoring real transactions, not demo data.
• Real-time controls across peer-to-peer, card, account-based, and crypto-related activity, already in production.
• Finance and compliance automation eliminating manual processing across reconciliation, reporting, and monitoring at scale.
• 400+ internal users on our workflow orchestration platform.
• You’ll inherit a detection function that’s already operationally mature — and have the engineering and data teams to push it further.
• When you identify a control gap, you’ll have the cross-functional access to close it.

Fraud Systems & Automation

• Own the detection stack. Design, implement, and continuously tune fraud prevention controls across payment channels. Build, test, and sharpen fraud rules, thresholds, velocity limits, and alerting frameworks to improve accuracy and reduce false positives.
• Drive end-to-end automation. Ensure the vast majority of fraud checks run automatically. Reduce manual intervention through effective decisioning logic and real-time controls — and know where human review is still the right call.
• Partner with Engineering and Data. Work cross-functionally to advance fraud tooling, case management, data enrichment, and predictive detection capabilities. Translate what you see in investigations into system requirements.

Analytics & Investigation

• Lead with data. Analyse transactional, behavioural, device, and network data to identify fraud patterns, attack vectors, and control weaknesses. Convert insights into measurable prevention outcomes, not just reports.
• Conduct complex investigations. Lead deep-dive investigations into account takeover, payment abuse, onboarding fraud, card testing, mule activity, collusion, and organised fraud networks. Use root-cause analysis to close control gaps, not just close cases.
• Own fraud rules performance. Monitor detection accuracy, false positive rates, and control effectiveness continuously. Tune and retune based on what the data shows.

Framl Integration & Financial Crime Escalation

• Bridge fraud and AML. Ensure key fraud indicators — account takeover signals, card testing patterns, synthetic identity, mule behaviour — are reflected in AML risk assessments and customer risk ratings. Drive FRAML integration as an operating practice, not a project.
• Own the escalation process. Maintain clear, documented processes for escalating confirmed or highly suspicious fraud cases to Compliance and the MLRO as potential predicate offenses. Provide Compliance with the technical evidence and case detail required for SAR/STR filings.
• Support regulatory obligations. Align fraud controls and escalation practices with AML obligations and the regulatory expectations of Malta-licensed entities.

Governance & Audit Readiness

• Document to regulatory standard. Ensure all investigations are fully documented with clear rationale, structured evidence, and reliable audit trails suitable for governance review, regulatory examination, and law enforcement engagement.
• Manage fraud loss mitigation. Oversee chargebacks, fraud disputes, and recovery processes in partnership with Payments and Finance. Use fraud intelligence to minimise losses and improve control effectiveness.

Who You Are

• You’ve owned fraud prevention at depth. 8+ years in fraud prevention, financial crime, compliance, risk, or investigations — ideally in fintech, payments, forex, crypto, online trading, or financial services. You’ve run the full lifecycle: detection design, complex investigation, escalation, and regulatory documentation.
• You understand the fraud-AML connection. You know fraud doesn’t stop at a chargeback. You understand how account takeover, synthetic identity, and mule networks create money laundering exposure — and how to document that in a way Compliance and regulators can act on.
• You’re technical enough to build, not just direct. You’re not a hands-on engineer, but you understand how fraud models are trained and deployed, how rules engines work, and how to collaborate with engineers on detection logic without losing precision in translation.
• You think in systems, not individual cases. When you close an investigation, your first question is: what control does this change? You use root-cause analysis to drive better automated defences.
• You’ve led teams and held them to a high standard. Proven people management experience. Your team documents thoroughly, escalates decisively, and doesn’t cut corners under volume pressure.
• You communicate at both ends of the technical spectrum. You can brief the MLRO on SAR rationale and brief Engineering on detection logic requirements — without losing either audience. Excellent written and verbal English is essential.
• Good to Have
• Experience in a regulated brokerage, CFD, forex, crypto, or payments environment.
• Familiarity with FRAML models, unified risk scoring, or integrated fraud and AML monitoring frameworks.
• Hands-on experience with fraud detection tools, case management platforms, rules engines, or device and behavioural intelligence solutions.
• CFE, CAMS, or equivalent certification.

The Honest Reality