Director, Fraud Strategy & Operations

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Position:Director, Fraud Strategy & Operations Location: Milpitas, CA
Job :237 # of Openings:1
Who we are
We are Payactiv, a Fintech company in the heart of Silicon Valley, devoted to giving workers access to their earned wages when they need them. Payactiv is the pioneer and industry leader in Earned Wage Access. We are the only Certified B Corporation and Public Benefit Corporation in our industry. Our app provides millions with financial services that help them avoid debt, manage their finances, and regain flexibility to pay for things on their own schedule.
About the Role The Director, Fraud Strategy & Operations will serve as a key operational partner to the VP of Consumer Banking, Payments and Fraud, supporting day-to-day execution of fraud programs and initiatives across Payactiv’s consumer banking and payments ecosystem, including cards such as credit, debit, prepaid, etc. The person in this role will be the technical mastermind behind the Pay Activ fraud rules engine.

The primary function will be to design, implement, and optimize the real-time decisioning logic for our card portfolio. The person does not just monitor fraud, the person engineers its prevention. Using advanced data analytics, the person will write the rules that intercept fraudulent transactions in milliseconds while ensuring that legitimate users never face embarrassment at the register. The person will be the bridge between raw data and actionable security.
What you will do
Rules engine strategy and ownership

• Author complex, multi-factor fraud rules (e.g., using FICO Falcon, Actimize, Sardine, etc.) that evaluate 200+ data points per transaction, including merchant category, velocity, IP reputation, and behavioral biometrics.
• Manage the constant lifecycle of a rule. Proactively audit the legacy ruleset to retire stale logic that no longer catches fraud but still adds latency or causes false positives.
• Identify and resolve rule overlap where multiple rules trigger on the same pattern, ensuring the decisioning engine remains lean and fast.

Champion-challenger testing and simulation

• Before any new rule goes live, it must be tested in shadow mode, testing it against 30 days of real historical traffic to predict exactly how many good users it would have blocked.
• Justify every new rule with a data-backed cost-benefit analysis. For example, “This rule will stop $50k in monthly fraud but will block 12 legitimate users, the trade-off is acceptable.”
• Rather than static rules, write dynamic and adaptive rules that adjust thresholds based on the user’s individual behavior.

Data forensics and emerging pattern detection

• Use SQL and Python to perform cluster analysis. For example, if three cards are used at the same digital merchant within 5 minutes, one must identify if this is a carding attack and write a temporary circuit breaker rule immediately.
• Link data from EWA transfers, direct deposits, and card swipes to identify account takeovers (ATO).
• Incorporate external merchant risk data to write rules that are stricter at high-risk digital goods stores but leaner at local grocery stores

Performance tuning and KPI accountability

• Optimize false positive ratio and keep it below mandated threshold.
• If fraud…