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.
CardlessSan Francisco, CA
