Flex

Interest-bearing digital banking with expense tools

Director of Fraud Risk Management

Risk ManagementConfirmed live in the last 24 hours

Full-timeRemote in USA$150k - $250k /yrRemote

About the Job

Requirements

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 is strongly preferred.
Deep subject matter expertise across first-party, third-party, and synthetic identity fraud, including analytical distinctions and differences between credit card and DDA contexts.
Knowledge of device fingerprinting, IP intelligence, identity graph analysis, behavioral biometrics, velocity rules, and machine-learning-based anomaly detection, including when rules-based logic outperforms 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 with account takeover.
Proficiency in SQL and Python or R, including the ability to build detection logic, cohort analysis, and loss attribution from raw data.
Familiarity with fraud-related regulatory and compliance requirements, including SAR filing thresholds, Reg E obligations, FCRA considerations for adverse action, and BSA/AML red flags overlapping with fraud patterns.
Ability to assess emerging attack vectors, challenge detection assumptions, model how bad actors exploit products or verification weaknesses, and build detection logic accordingly.
Strong quantitative aptitude, including detecting anomalous loss or dispute trends and escalating pattern shifts with evidence.
Ability to communicate complex fraud dynamics as clear narratives for risk committees, product teams, and senior leadership.
Ability to work effectively in an environment where detection infrastructure is being built and the threat landscape is evolving.

Responsibilities

Own end-to-end fraud risk management across Flex’s credit card and DDA portfolio for consumer and small business segments, covering the lifecycle from pre-acquisition through post-disbursement.
Analyze acquisition and onboarding fraud patterns by channel and source; assess identity signal quality, document authenticity rates, and synthetic identity indicators at the population level; monitor approval flows for anomalous shifts that may indicate policy exploitation.
Maintain analytical visibility into identity and entity verification match rates, challenge rates, and step-up conversion across KYC and KYB layers in partnership with compliance; identify friction for good applicants and gaps for bad actors, and surface findings for joint policy discussions.
Monitor behavioral signals associated with first-party fraud, including spend acceleration, balance build without payment intent, cash advance abuse, and bust-out patterns; analytically distinguish first-party risk from credit deterioration.
Track third-party fraud patterns, including unauthorized transactions, account takeover indicators, card-not-present abuse, and compromised credentials; maintain segment-level dispute and chargeback views by fraud type.
Build and maintain synthetic identity detection frameworks, including for thin-file manipulation, credit piggybacking, and fabricated entity structures, with attention to SMB applicants with sparse bureau data and harder entity verification.
Monitor DDA-specific fraud vectors, including ACH manipulation, payee substitution, unauthorized external transfer attempts, and check fraud; maintain visibility into funds-flow anomalies that may indicate account misuse or laundering behavior.
Analyze real-time and near-real-time authorization patterns for velocity anomalies, geographic inconsistencies, merchant-category abuse, and card-testing signals.
Own the analytical view of dispute resolution patterns; identify chargeback abuse and friendly fraud at segment and merchant levels; 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 device, IP, identity, bureau, transaction, and behavioral data into a layered fraud risk view; identify coordinated fraud rings and correlated anomalies that single-signal models may miss.
Lead periodic fraud risk reviews by designing the analytical narrative, owning underlying loss and dispute data, and presenting 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 support more precise detection tuning, policy intervention, and loss reserve calibration.
Partner with credit, legal and compliance, financial crimes, operations, and product teams to embed fraud risk visibility in product design and upstream decisioning.
Contribute to fraud-loss scenario analysis and stress testing by modeling exposure under elevated attack conditions and translating it into loss and operational cost estimates.
Serve as the internal subject matter expert on fraud analytics by establishing detection standards, taxonomy, and measurement frameworks as the product portfolio scales.

Desired Qualifications

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

Salary Range: 150000 to 250000 (Currency: USD) (Pay period: per-year-salary)

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 Director of Fraud Risk Management 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 analytics — establishing detection standards, taxonomy, and measurement frameworks as the product portfolio scales

Qualifications

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

Mindset

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

About the Company

Flex

Interest-bearing digital banking with expense tools

Flex.one runs a digital business banking platform that lets companies earn interest on idle cash (up to 4%), manage expenses in real time, issue team cards, and send free domestic ACH and wire payments, with 1% cashback on transactions. Funds on the platform generate interest while users access real-time reporting, expense management tools, and card issuance, with payments processed within the platform; revenue comes from service fees and interest on client cash. It differentiates itself by offering a high interest rate on idle cash together with a full suite of banking tools in one place, supported by regular security audits and fraud protections. Its goal is to help businesses maximize cash utility and streamline financial operations with secure, cost-effective payment processing and added rewards.

Company size: 201-500
Company stage: Series B
Total funding: $493.2M
Headquarters: Miami, FL
Founded: 2020

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