Fraud Risk Management Lead

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.

Our customers are affluent business owners ($3–$200M in revenue)—the backbone of the economy and one of the most underserved segments in finance. They’re stuck with outdated banks and fragmented tools. We’re replacing all of it. The opportunity is massive: a ~$1T+ revenue market hiding in plain sight. Our ambition is to build a product that is fundamentally better—not incrementally improved.

Flex Fuels Ambition.

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.

Core 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 a