Manager, Fraud Strategy & Operations

Payactiv•Milpitas, CA

4d•Onsite

About The Position

Payactiv is seeking a Manager, Fraud Strategy & Operations to serve as a key operational partner to the VP of Consumer Banking, Payments and Fraud. This role supports the day-to-day execution of fraud programs and initiatives across Payactiv’s consumer banking and payments ecosystem, including various card types. The individual will be the technical lead for the Payactiv fraud rules engine, responsible for designing, implementing, and optimizing real-time decisioning logic for the card portfolio. This involves engineering fraud prevention through advanced data analytics, writing rules to intercept fraudulent transactions in milliseconds while minimizing impact on legitimate users, and acting as a bridge between data and security.

Requirements

4-7+ years of experience in fraud strategy and operations.
Deep experience in authoring rules on real-time decisioning platforms.
Experience measuring relevant KPIs with appropriate trade-offs.
Hands-on experience with SQL and Python.
Strong technical knowledge of the ISO 8583 messaging standard and the card authorization lifecycle.
Good organizational and execution skills with the ability to manage multiple priorities.
Experience working cross-functionally in demanding environments.
Analytical mindset with attention to detail.

Responsibilities

Author complex, multi-factor fraud rules using platforms like FICO Falcon, Actimize, or Sardine, evaluating numerous data points per transaction.
Manage the lifecycle of fraud rules, proactively auditing and retiring stale logic, and resolving rule overlaps to maintain a lean and fast decisioning engine.
Conduct champion-challenger testing and simulation for new rules against historical data to predict impact on legitimate users.
Justify new rules with data-backed cost-benefit analyses, considering fraud prevention versus potential blocking of legitimate users.
Write dynamic and adaptive rules that adjust thresholds based on individual user behavior.
Utilize SQL and Python for data forensics and emerging pattern detection, including cluster analysis.
Identify and address potential carding attacks by analyzing transaction patterns.
Link data from various sources (EWA transfers, direct deposits, card swipes) to identify account takeovers (ATO).
Incorporate external merchant risk data to create differentiated rule sets based on merchant risk levels.
Optimize the false positive ratio to remain below mandated thresholds.
Pivot fraud strategy within hours to address fraud loss spikes in specific categories.
Ensure rules do not inadvertently decline high-value transactions that generate significant interchange revenue, unless the risk score is undeniably high.