Fraud Risk Management Lead
New
USFull-TimeLead
Salary150,000 - 250,000 USD per year
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Job Details
- Experience
- 7–15 years
- Required Skills
- PythonSQLData analyticsR
Requirements
- 7–15 years of experience in fraud risk management, with ownership of detection, monitoring, and loss prevention in consumer or small business financial products.
- Experience across both regulated financial institutions and fintech environments is strongly preferred.
- Deep expertise in first-party, third-party, and synthetic identity fraud, with strong analytical differentiation across fraud types.
- Strong understanding of fraud detection technologies including device fingerprinting, behavioral analytics, identity graphing, and anomaly detection systems.
- Experience with deposit account (DDA) fraud vectors such as ACH abuse, check fraud, Reg E disputes, and account takeover patterns.
- Advanced analytical skills with proficiency in SQL and either Python or R for data exploration, modeling, and detection logic development.
- Strong understanding of regulatory frameworks including BSA/AML, FCRA, and Reg E as they relate to fraud and disputes.
- Ability to translate complex fraud patterns into actionable insights and executive-level narratives.
- High ownership mindset with the ability to operate independently in a fast-evolving, high-growth environment.
- Strong communication skills with experience presenting to senior leadership and cross-functional stakeholders.
Responsibilities
- Own end-to-end fraud risk management strategy across credit card and deposit account products, covering the full lifecycle from onboarding to post-transaction monitoring.
- Analyze acquisition fraud patterns, including identity validation, synthetic identity detection, and channel-based risk anomalies.
- Monitor first-party, third-party, and synthetic fraud behaviors across credit and deposit ecosystems, identifying emerging attack patterns and loss trends.
- Develop and enhance fraud detection frameworks using multi-source data including behavioral, transactional, device, and identity signals.
- Oversee real-time and near-real-time transaction monitoring to detect anomalies such as velocity spikes, account takeovers, and payment abuse.
- Lead dispute, chargeback, and recovery analytics to identify fraud leakage, friendly fraud, and recovery optimization opportunities.
- Build segmentation models across fraud types, products, and channels to improve detection precision and policy effectiveness.
- Partner cross-functionally with credit, compliance, legal, product, and operations teams to embed fraud prevention into product design and decisioning.
- Present fraud risk insights, exposure analysis, and strategic recommendations to senior leadership and risk committees.
- Support scenario modeling and stress testing to assess fraud exposure under adverse or high-attack conditions.
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