[Hiring] Fraud Risk Management Lead @Flex

Role Description

The Fraud Risk Management Lead is a key member of the Flex Risk Management Leadership Team (reports to the Chief Risk Officer) who will have the opportunity to take the Fraud Risk Management function at Flex to a level that rivals the best in class.

• Own end-to-end fraud risk management for Flex's credit card and DDA product portfolio across consumer and small business segments; end-to-end meaning full lifecycle coverage, from pre-acquisition through post-disbursement:
• Acquisition & onboarding fraud:
  • Analyze application fraud patterns by channel and source.
  • Assess identity signal quality, document authenticity rates, and synthetic identity indicators at the population level.
  • Monitor approval flow for anomalous approval rate shifts that may signal policy exploitation.
• Identity & entity verification performance:
  • Maintain analytical visibility into match rates, challenge rates, and step-up conversion across KYC and KYB verification layers in partnership with compliance.
  • Identify where the verification stack may be generating friction for good applicants or gaps for bad ones — and surface those findings as inputs to joint policy discussions.
• First-party fraud:
  • Monitor behavioral signals associated with intentional default — spend acceleration, balance build without payment intent, cash advance abuse, and bust-out patterns.
  • Distinguish first-party risk from credit deterioration analytically.
• Third-party fraud:
  • Track unauthorized transaction patterns, account takeover indicators, card-not-present abuse, and compromised credential signals.
  • Maintain segment-level views of dispute and chargeback rates by fraud type.
• Synthetic identity fraud:
  • Build and maintain detection frameworks for synthetic identities — thin-file manipulation, credit piggybacking, fabricated entity structures — with particular attention to SMB applicants where bureau data is sparse and entity verification is harder.
• DDA-specific fraud vectors:
  • Monitor ACH manipulation, payee substitution, unauthorized external transfer attempts, and check fraud patterns within the DDA product.
  • Maintain visibility into funds flow anomalies that may indicate account misuse or laundering behavior.
• Authorization & transaction monitoring:
  • Analyze real-time and near-real-time authorization patterns for velocity anomalies, geographic inconsistencies, merchant category abuse, and card testing signals.
• Dispute, chargeback & recovery:
  • Own the analytical view of dispute resolution patterns.
  • Identify chargeback abuse and friendly fraud at the segment and merchant level.
  • Track recovery rates by fraud type and loss emergence timing.
• Build and maintain fraud detection frameworks that surface emerging attack patterns before they scale — distinguishing signal from noise across high-volume transaction and behavioral data.
• Synthesize data across sources — device, IP, identity, bureau, transaction, and behavioral — to construct a layered fraud risk view; capable of identifying coordinated fraud rings and correlated anomalies that don't surface in single-signal models.
• Lead periodic fraud risk reviews: design the analytical narrative, own the underlying loss and dispute data, and present findings with clear exposure implications to risk committees and senior leadership.
• Develop fraud segmentation — by fraud type, acquisition channel, product, obligor type, and attack vector — to enable more precise detection tuning, policy intervention, and loss reserve calibration.
• Partner cross-functionally with credit, legal & compliance, financial crimes, operations, and product to ensure fraud risk visibility is embedded in product design and upstream decisioning, not bolted on reactively.
• Contribute to scenario analysis and stress testing for fraud loss: model exposure under elevated attack conditions and translate into concrete loss and operational cost estimates.
• Serve as the internal SME on fraud analytics — establishing detection standards, taxonomy, and measurement frameworks as the product portfolio scales.

Qualifications

• 7–15 years of hands-on fraud risk management experience with direct ownership of detection and loss management across consumer and small business products; experience spanning both a bank or regulated card program and a fintech strongly preferred.
• Deep subject matter expertise across all three fraud typologies — first-party, third-party, and synthetic identity — with the ability to distinguish them analytically, not just definitionally.
• Fluent in the fraud signal stack: device fingerprinting, IP intelligence, identity graph analysis, behavioral biometrics, velocity rules, and ML-based anomaly detection.
• Understands DDA fraud vectors at a product level: ACH origination and return abuse, check fraud, Reg E dispute dynamics, and the intersection of payment fraud with account takeover.
• Analytically self-sufficient: proficient in SQL and Python or R; capable of building detection logic, cohort analysis, and loss attribution from raw data.
• Familiar with the regulatory and compliance overlay on fraud: SAR filing thresholds, Reg E obligations, FCRA considerations for adverse action, and BSA/AML red flags that overlap with fraud patterns.

Requirements

• Operates at a senior thinking level relative to peer cohort — brings a point of view on emerging attack vectors, challenges detection assumptions, and drives the fraud agenda without waiting to be directed.
• Instinctively thinks from the other side of the table: models how a bad actor would exploit a product, policy gap, or verification weakness — and builds detection logic accordingly.
• High quantitative aptitude with strong intuition for when loss or dispute trends don't pass the smell test; catches pattern shifts early and escalates with evidence, not just instinct.
• High-energy, end-to-end owner who thrives in environments where detection infrastructure is still being built and the threat landscape is actively evolving.
• Effective communicator who can translate complex fraud dynamics — ring structures, synthetic identity clusters, bust-out cohorts — into crisp narratives for risk committees, product teams, and senior leadership.

Benefits

• $150,000 - $250,000 a year.
• All employees are given Equity in the company in addition to the Base Compensation.