Flex - Fraud Risk Management Lead
Upload My Resume
Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT
Requirements
• This role sits in the foundational build path of core risk management disciplines, and we expect significant upward potential for the right candidate. The emphasis is on finding colleagues with a strong foundation more than a ‘minimum number of years’ constraint. We can work with folks who have 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; understands how each manifests differently in a credit card versus DDA context • Fluent in the fraud signal stack: device fingerprinting, IP intelligence, identity graph analysis, behavioral biometrics, velocity rules, and ML-based anomaly detection — knows which tools to reach for and when rules-based logic outperforms models • 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 rather than consuming pre-built dashboards • 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 • 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
Responsibilities
• 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 • Flex is building the AI-native private bank for business owners. • We’re re-architecting the entire financial system for entrepreneurs—from the first dollar a business earns to how that value compounds, moves, and is ultimately spent in real life. Banking, credit, payments, personal finance, and financial operations—rebuilt from the ground up as a single, intelligent system. Flex is the full financial home for ambitious owners. • Since launching publicly in September 2023, Flex has scaled from zero to nine-figure annualized revenue, with a clear path to profitability by late 2026. We move fast, ship relentlessly, and operate with extreme ownership.
No credit card. Takes 10 seconds.