Fraud Strategy Manager / Senior Manager (Onboarding)

Cardless; San Francisco (CA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Meaningful start-up equity

100% health, vision & dental coverage

Flexible PTO with a minimum of 15 days off

Job summary

A fintech firm in San Francisco is looking for a Fraud Strategy Manager to oversee the fraud evaluation process for new applicants. This role requires 5–10 years of experience in fraud strategy and strong expertise in managing identity risks, including first-party fraud vectors. The selected candidate will own the decision-making framework and report on the effectiveness of strategies. Competitive compensation includes an annual salary of $150,000–$210,000, equity, and comprehensive benefits.

Qualifications

• 5–10 years of experience in fraud strategy, identity risk, or credit risk.
• Deep expertise in onboarding fraud including first-party fraud vectors.
• Hands-on experience with bank data providers in fraud or credit risk.

Responsibilities

• Own the end-to-end fraud strategy for evaluating new applicants.
• Define logic for routing applicants into manual review to balance fraud risk.
• Document all policy decisions in the fraud rules framework for onboarding.

Skills

Fraud strategy

Identity risk

Credit risk

Strong SQL skills

Excellent communication

Proactive mindset

Job description

The Job

We’re looking for a Fraud Strategy Manager or Senior Manager – Onboarding to own the end‑to‑end fraud strategy for evaluating new applicants. This highly analytical, strategy‑forward role focuses on building the decisioning framework that determines which applicants are approved, stepped up for review, or declined, and ensuring the tools, data, and processes behind those decisions are best‑in‑class.

What You’ll Own

Manual Review Strategy & Operations

• Define 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 rule 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 trade‑offs 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‑decided 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—able to translate complex fraud trade‑offs into clear recommendations for executives, partners, and compliance teams.
• Proactive, bias‑to‑action mindset: asks the right questions, aligns stakeholders, and drives decisions forward.

Compensation

This role has an annual starting salary range of $150,000–$210,000 plus equity and benefits. Actual compensation is influenced by a wide array of factors, including but not limited to skills, experience, and specific work location.

Benefits

• Meaningful start‑up equity
• 100% health, vision & dental primary coverage
• 75% health, vision & dental dependent coverage
• Catered lunches
• $250/month commuter benefit
• Parental leave
• Team building events & happy hours
• Flexible PTO with a minimum of 15 days off per year
• Apple equipment
• 401(k) plan

Location

We’re headquartered in San Francisco, CA. We welcome employees who want to work from the office; we offer additional benefits to those who do, and relocation assistance to those who’d like to.