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Fraud Strategy Manager / Senior Manager (Onboarding)

Owns end-to-end fraud strategy for applicant onboarding, including manual review, alternative data signals, rules development, and performance monitoring. Requires 5-10 years in fraud/credit risk, strong SQL, and expertise in onboarding fraud vectors.

About the job

What You'll Own

Manual Review Strategy & Operations

  • Define the logic and thresholds for routing applicants into manual review, balancing fraud risk against approval rates and customer experience.
  • Own the manual review program end-to-end: queue prioritization, SLA design, analyst tooling, and case escalation paths.
  • Evaluate the effectiveness of tools used in manual review; identify gaps and advocate for new capabilities that help analysts make faster, higher-quality decisions.
  • Track manual review outcomes rigorously: analyst decisions, approval/decline rates, reversal rates, downstream fraud on reviewed accounts, and false positive costs.
  • Build structured feedback loops between review outcomes and upstream rules triggers to drive continuous policy refinement.

Alternative Data & Signal Development

  • Lead the strategy for incorporating bank account data into onboarding decisions, leveraging signals such as account tenure, balance history, income patterns, and return/NSF activity.
  • Operationalize device intelligence and behavioral signals to strengthen identity and fraud detection at the top of the funnel.
  • Develop a framework for using partner data: loyalty engagement, transaction history, tenure signals as supplementary fraud indicators in co-brand card programs.
  • Evaluate new data sources and vendors on an ongoing basis; build a rigorous test-and-learn methodology to validate signal lift before production deployment.

Fraud Detection & Rules Ownership

  • Own the fraud rules framework for onboarding: design, test, implement, and continuously tune rules across identity, velocity, device, funding, and behavioral dimensions.
  • Partner with data science to define feature requirements, evaluate model performance, and translate model outputs into operational policy.
  • Document all policy decisions clearly, including the tradeoffs made at each threshold.

Performance Measurement & Portfolio Monitoring

  • Define and own the KPI framework for onboarding fraud: fraud rate by vintage and partner, manual review rate, auto-decisioned bad rate, tool efficacy, and cost-per-review.
  • Conduct regular portfolio reviews to surface emerging fraud patterns, track loss trends, and assess detection performance.
  • Build reporting to enable real-time monitoring, trend identification, and rapid policy response.

Cross-Functional Partnership

  • Partner with Product and Engineering to translate fraud strategy into system requirements and influence roadmap prioritization.
  • Work with Compliance and Legal to ensure onboarding controls meet BSA/AML, Red Flags Rule, ECOA, and UDAAP requirements.
  • Collaborate with Customer Operations to manage edge cases, decision appeals, and applicant escalations.

What We're Looking For

  • 5–10 years of experience in fraud strategy, identity risk, or credit risk at a financial institution, fintech, or payments company.
  • Deep expertise in onboarding fraud — synthetic identities, identity manipulation, first-party fraud vectors, and deposit/funding fraud.
  • Hands-on experience with bank data providers in a fraud or credit risk context.
  • Familiarity with device intelligence and behavioral fraud platforms.
  • Strong SQL skills and experience using data to build and evaluate fraud rules, track performance, and identify emerging patterns.
  • Excellent communication skills — you can translate complex fraud tradeoffs into clear recommendations for executives, partners, and compliance teams.
  • Proactive, bias-to-action mindset: you ask the right questions, align stakeholders, and drive decisions forward.

Compensation

Annual starting salary range of $150,000 - $210,000 + equity + benefits.

Skills

SQL, Fraud Rules, Device Intelligence, Behavioral Analytics, Bank Data, Identity Risk, Credit Risk, Bsa/Aml, Data Analysis, Rules Engine

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