Director of Fraud Risk Management
New
F
Flexbase Technologies Inc.Financial services
Remote / USAFull-TimeDirector
Salary$150,000 - $250,000 a year
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Job Details
- Experience
- We can work with folks who have 7–15 years of hands-on fraud risk management experience
- Required Skills
- PythonSQLR
Requirements
- Have hands-on fraud risk management experience with direct ownership of detection and loss management across consumer and small business products.
- Bring 7–15 years of hands-on fraud risk management experience; experience across both a bank or regulated card program and a fintech is strongly preferred.
- Demonstrate expertise in first-party, third-party, and synthetic identity fraud and distinguish these typologies analytically.
- Understand how fraud typologies manifest differently in credit card and DDA products.
- Have knowledge of device fingerprinting, IP intelligence, identity graph analysis, behavioral biometrics, velocity rules, and ML-based anomaly detection.
- Understand DDA fraud vectors, including ACH origination and return abuse, check fraud, Reg E dispute dynamics, and account takeover.
- Be proficient in SQL and Python or R, and able to build detection logic, cohort analysis, and loss attribution from raw data.
- Understand fraud-related regulatory and compliance requirements, including SAR filing thresholds, Reg E obligations, FCRA considerations for adverse action, and BSA/AML red flags.
Responsibilities
- Own end-to-end fraud risk management across credit card and DDA products, from pre-acquisition through post-disbursement.
- Monitor acquisition, identity verification, first-party, third-party, synthetic identity, and DDA-specific fraud patterns.
- Analyze authorization, transaction, dispute, chargeback, and recovery data to identify emerging attack patterns and losses.
- Build detection frameworks using device, IP, identity, bureau, transaction, and behavioral signals.
- Lead periodic fraud risk reviews and present exposure findings to risk committees and senior leadership.
- Develop fraud segmentation by fraud type, acquisition channel, product, obligor type, and attack vector.
- Partner with credit, legal and compliance, financial crimes, operations, and product teams on fraud risk visibility and decisioning.
- Model fraud loss exposure under elevated attack conditions and estimate related losses and operational costs.
- Establish fraud analytics detection standards, taxonomy, and measurement frameworks.
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