Fraud Lead - Consumer Lending

Felix Technologies, Inc.•Washington, DC

6d•Remote

About The Position

As Fraud Lead for Consumer Lending, you will protect the unit economics of Félix’s Send Now Pay Later (SNPL) product. This high-visibility, cross-functional role reports directly to the Head of Credit Risk, with a dotted line to the broader Fraud Organization. You will define, architect, and mitigate fraudulent behaviors at the intersection of WhatsApp conversational interfaces and digital cross-border lending. The role calls for an autonomous, highly analytical fraud specialist who can build prevention frameworks from scratch, identify sophisticated attack vectors, deploy dynamic decisioning logic, and support secure, profitable business growth.

Requirements

4–5+ years of hands-on experience in fraud risk strategy and prevention within consumer lending, BNPL, digital wallets, or credit card ecosystems.
Advanced SQL skills (e.g., BigQuery or Snowflake) and proficiency in data manipulation using Python/R or BI visualization tools to independently extract, analyze, and model transactional logs.
Proven experience with modern decision engines such as Taktile, Provenir, Alloy, or similar, and deploying or monitoring machine-learning risk models in live environments.
Deep understanding of emerging fraud attack vectors, account takeover (ATO), and synthetic fraud, with experience using data and OSINT techniques for threat detection.
Ability to translate complex fraud patterns and technical metrics into actionable business risks for executives, product managers, and engineering teams.
Builder mentality and experimental spirit, with the ability to move from high-level data insights to policy execution with speed, precision, and minimal guidance.
Equivalent competencies in the listed areas will also be considered.

Responsibilities

Design, launch, and continuously refine first-party and third-party fraud typologies for the WhatsApp conversational lending ecosystem.
Own repayment fraud, identity spoofing, and chargeback analytics for SNPL payments; identify exploit loops and synthetic identities, and implement preemptive transaction blocks.
Drive and be accountable for core fraud metrics, including net fraud rate, false positive ratios, challenge rates, and chargeback-to-sales ratios, across the lending vertical without hindering conversion.
Own rule design and logic orchestration within decision engines such as Taktile, Provenir, and Alloy; partner with Data Science to deploy, monitor, and calibrate machine-learning risk models in production.
Work with Operations and Engineering to design, optimize, and scale automated blocking protocols and efficient manual investigation workflows.
Coordinate with Legal and Compliance to monitor, flag, and report Suspicious Activity Reports (SARs), loan abuse patterns, and system exploitation in alignment with regulatory standards.