Director of Fraud Risk Management (copy)

Flex

8d•$150,000 - $250,000

About The Position

Flex is building an AI-native private bank for business owners, combining banking, credit, payments, personal finance, and financial operations into a single system. Since launching publicly in September 2023, the company has scaled to nine-figure annualized revenue and aims to reach profitability by late 2026. Flex serves affluent business owners, generally with $3–$200 million in revenue. The Director of Fraud Risk Management is a member of the Flex Risk Management Leadership Team and reports to the Chief Risk Officer. This leader will own end-to-end fraud risk management for Flex’s credit card and DDA portfolio across consumer and small-business segments, covering the full lifecycle from pre-acquisition through post-disbursement. The role will build and maintain fraud detection frameworks, develop analytical views of fraud losses and disputes, identify emerging attack patterns, and help embed fraud risk visibility in product design and upstream decision-making. The position involves synthesizing device, IP, identity, bureau, transaction, and behavioral data to identify coordinated fraud rings and correlated anomalies. The Director will lead periodic fraud risk reviews, develop fraud segmentation, contribute to fraud-loss scenario analysis and stress testing, and serve as the internal subject-matter expert on fraud analytics. The role partners with credit, legal and compliance, financial crimes, operations, and product teams, and presents findings to risk committees and senior leadership. Flex describes this as a foundational role with significant upward potential for the right candidate. The company emphasizes ownership and provides equity in addition to base compensation.

Requirements

The posting identifies 7–15 years of hands-on fraud risk management experience as a range the company can work with, with direct ownership of detection and loss management across consumer and small-business products.
Deep expertise in first-party, third-party, and synthetic identity fraud, including the ability to distinguish these typologies analytically and understand how they differ across credit card and DDA products.
Fluency with fraud signals and tools, including device fingerprinting, IP intelligence, identity graph analysis, behavioral biometrics, velocity rules, and machine-learning-based anomaly detection; understands when rules-based logic is preferable to models.
Product-level understanding of DDA fraud vectors, including ACH origination and return abuse, check fraud, Reg E dispute dynamics, and the intersection of payment fraud and account takeover.
Proficiency in SQL and Python or R, with the ability to build detection logic, cohort analyses, and loss attribution from raw data.
Familiarity with regulatory and compliance considerations relevant to fraud, including SAR filing thresholds, Reg E obligations, FCRA considerations for adverse action, and BSA/AML red flags that overlap with fraud patterns.
Ability to communicate complex fraud dynamics clearly to risk committees, product teams, and senior leadership.
Strong quantitative aptitude and the ability to identify unusual loss or dispute trends and escalate findings with evidence.
Ability to take end-to-end ownership in an environment where detection infrastructure is being built and the threat landscape is evolving.

Nice To Haves

Experience spanning both a bank or regulated card program and a fintech is strongly preferred.

Responsibilities

Own end-to-end fraud risk management for Flex’s credit card and DDA products across consumer and small-business segments, from pre-acquisition through post-disbursement.
Analyze acquisition and onboarding fraud, including application patterns, identity signals, document authenticity, synthetic identity indicators, and anomalous approval-rate changes.
Maintain analytical visibility into identity and entity verification performance, including match rates, challenge rates, and step-up conversion, in partnership with compliance.
Monitor first-party fraud signals such as spend acceleration, balance build without payment intent, cash advance abuse, and bust-out patterns, and distinguish fraud risk analytically from credit deterioration.
Track third-party fraud, including unauthorized transactions, account takeover, card-not-present abuse, compromised credentials, and dispute and chargeback rates by fraud type.
Build and maintain synthetic identity detection frameworks, including for SMB applicants with sparse bureau data or more difficult entity verification.
Monitor DDA fraud vectors, including ACH manipulation, payee substitution, unauthorized external transfers, check fraud, funds-flow anomalies, and potential account misuse or laundering behavior.
Analyze real-time and near-real-time authorization and transaction patterns for velocity anomalies, geographic inconsistencies, merchant-category abuse, and card-testing signals.
Own the analytical view of dispute resolution, identify chargeback abuse and friendly fraud, and track recovery rates by fraud type and loss-emergence timing.
Build and maintain fraud detection frameworks that surface emerging attack patterns and distinguish signal from noise across transaction and behavioral data.
Synthesize device, IP, identity, bureau, transaction, and behavioral data to identify layered risk, coordinated fraud rings, and correlated anomalies.
Lead periodic fraud risk reviews, develop the analytical narrative, own loss and dispute data, and present exposure implications to risk committees and senior leadership.
Develop fraud segmentation by fraud type, acquisition channel, product, obligor type, and attack vector to support detection tuning, policy intervention, and loss-reserve calibration.
Partner with credit, legal and compliance, financial crimes, operations, and product to embed fraud risk visibility in product design and upstream decision-making.
Contribute to fraud-loss scenario analysis and stress testing, modeling exposure under elevated attack conditions and estimating losses and operational costs.
Serve as the internal subject-matter expert on fraud analytics and establish detection standards, taxonomy, and measurement frameworks.