Fraud Lead - Consumer Lending

Felix Technologies•New York, NY

7d•Remote

About The Position

As our Fraud Lead for Consumer Lending, you will be the core architect and gatekeeper protecting the unit economics of our Send Now Pay Later (SNPL) product. This is a high-visibility, cross-functional role reporting directly to the Head of Credit Risk, with a dotted line into our broader Fraud Organization. You will be responsible for defining, architecting, and mitigating fraudulent behaviors at the intersection of instant conversational interfaces (WhatsApp) and digital cross-border lending. We need an autonomous, highly analytical fraud specialist who can build prevention frameworks from scratch, identify sophisticated attack vectors, deploy dynamic decisioning logic, and ensure that our business growth remains secure and highly profitable.

Requirements

4–5+ years of hands-on experience in Fraud Risk Strategy and Prevention, explicitly within consumer lending, Buy Now Pay Later (BNPL), digital wallets, or credit card ecosystems.
Advanced skills in SQL (BigQuery, Snowflake, etc.) and data manipulation (Python/R or BI visualization tools) to extract, analyze, and model transactional logs independently with total autonomy.
Proven experience working with modern decision engines (such as Taktile, Provenir, Alloy, or similar) and deploying/monitoring machine learning risk models in live environments.
Deep understanding of emerging fraud attack vectors, account takeover (ATO), and synthetic fraud, with a track record of using data and OSINT techniques for threat detection.
Strong ability to translate complex fraud patterns and technical metrics into actionable business risks for C-suite executives, product managers, and engineering teams.
A true builder mentality with an experimental spirit, capable of moving from high-level data insights down to policy execution with speed, precision, and zero hand-holding.
Equivalent competencies in any of the above will also be considered.

Responsibilities

Design, launch, and continuously refine first-party and third-party fraud typologies specifically tailored to our WhatsApp conversational lending ecosystem.
Own repayment fraud, identity spoofing, and chargeback analytics for SNPL payments, proactively identifying exploit loops, synthetic identities, and implementing preemptive transaction blocks.
Drive and hold direct accountability for core fraud metrics (e.g., net fraud rate, false positive ratios, challenge rates, chargeback-to-sales ratios) across the entire lending vertical without hindering conversion.
Own rule design and logic orchestration within modern decision engines (e.g., Taktile, Provenir, Alloy) and partner with Data Science to deploy, monitor, and calibrate Machine Learning risk models in production.
Partner closely with Operations and Engineering teams to design, optimize, and scale automated blocking protocols alongside efficient manual investigation workflows.
Coordinate with Legal & Compliance to monitor, flag, and report Suspicious Activity Reports (SARs), loan abuse patterns, and system exploitation in alignment with regulatory standards.