Senior Staff Fraud & Risk Analyst

Intuit QuickBooks is leading the charge in revolutionizing financial services for small and mid-market businesses through its money movement platform. Lending — Term Loan (TL), Line of Credit (LOC), and Revenue Based Financing (RBF) — is one of the fastest-growing pillars of that platform, extending credit to millions of small businesses that traditional lenders overlook. Growing responsibly at that scale requires world-class fraud strategy. The Lending Fraud Policy team combines cutting-edge AI, cross-product signal intelligence, and deep credit-risk expertise to detect and prevent fraud across the full lending lifecycle — from onboarding, through underwriting, disbursement, servicing, and recovery — while enabling frictionless experiences for good customers. As a Senior Staff Fraud & Risk Analyst, you will own end-to-end lending fraud strategy across Term Loan (TL), Line of Credit (LOC), and Revenue Based Financing (RBF) — the policy, detection systems, and operational governance that determine who Intuit lends to, on what terms, and how we defend the book from stolen/synthetic-identity fraud (FRAPP), account takeover (ATO), first-party fraud, and bust-out rings. You will collaborate with Product, Engineering, Data Science, Finance, Legal/LCPO, Risk Operations, and Internal Audit to design and continuously improve the risk experience. If you are passionate about solving real customer problems through decision science and analytics — and building the fraud strategy that lets Intuit safely extend credit to businesses no one else will — we welcome you to join our talented team. Responsibilities • Own end-to-end lending fraud strategy across Term Loan (TL), Line of Credit (LOC), and Revenue Based Financing (RBF) — set policy across the full lifecycle: onboarding eligibility, application underwriting, line/loan issuance, draw-level risk assessment (LOC and RBF), funding speed etc. • Design and continuously tune detection systems for stolen/synthetic identity (FRAPP), account takeover (ATO), first-party fraud, and bust-out rings across all lending products. • Lead complex forensic investigations of sophisticated multi-product lending fraud rings; drive same-day containment and translate ring learnings into permanent policy. • Partner with Data Science on ML detection models, risk-based decisioning, and AI Agent automation for case review and policy execution. • Set risk strategy for new lending initiatives — including NTTF (New-to-the-Franchise) Line of Credit (LOC) — balancing revenue enablement against fraud loss and customer experience; • Partner with Finance on loss forecasting, loss reserves, and unit economics to ensure lending decisions are both risk-informed and revenue-aware. • Partner with Risk Operations on case review workflows, fraud-hold enforcement at money-movement, and portfolio cleanup after ring detection. • Partner with Legal/LCPO and Compliance on regulatory risk — Reg B/Z, ECOA, UDAAP, adverse action, KYB/KYC, sanctions (OFAC). • Champion AI and automation — identify opportunities to apply AI-powered tooling and automation to accelerate risk strategy work, reduce manual toil, and improve decision quality and consistency at scale. • Communicate complex fraud strategy decisions clearly to senior leadership, financial partners, and regulators. Qualifications • MS/PhD in a quantitative field (Statistics, Mathematics, Economics, Operations Research, Finance, or related) with 7+ years in fraud analytics, credit risk, or data science • Fintech / Lending experience strongly preferred — hands-on ownership of fraud strategy for a lending product • Working knowledge of the full risk-control stack — fraud detection, underwriting, loss forecasting, disputes/collections and loss reserves. • Demonstrated experience in ML-based detection and statistical modeling — scorecard development, anomaly detection, clustering, feature engineering,