Senior Staff Fraud and Risk Analyst - Fintech

We are seeking a Senior Staff Analyst to join the QuickBooks Risk Management team as a strategic analytics partner supporting our Payments, Payroll, Bill Pay, and Capital product lines. This role leverages advanced analytical capabilities and modern AI tools to identify, quantify, and communicate risk trends across the QuickBooks money movement ecosystem — driving data-informed decisions that protect small businesses and enable product growth. The ideal candidate combines deep technical proficiency with strong business acumen, translating complex findings into actionable insights for product, operations, and risk stakeholders. You will build scalable frameworks that improve decision quality across the full customer lifecycle — from onboarding and underwriting through transaction monitoring, loss mitigation, and recovery. QuickBooks serves millions of small businesses who depend on our Payments, Payroll, Bill Pay, and Capital products to manage their financial lives. Each product carries distinct risk profiles — real-time payment fraud, payroll disbursement exposure, bill payment authorization risk, and credit loss on lending — yet many of the underlying patterns, customer signals, and mitigation strategies are shared. With the emergence of AI/ML tools (LLMs, copilots, automated pipelines), a senior analyst who can harness these tools while applying cross-product business judgment becomes a force multiplier. This role accelerates the speed, depth, and quality of insights reaching decision-makers across all four product lines simultaneously — ensuring we grow responsibly without creating friction for good customers. Responsibilities Serve as the primary risk analytics partner across QuickBooks Payments, Payroll, Bill Pay, and Capital — delivering cross-product insights that no single product team could surface alone. Build and maintain automated risk dashboards covering transaction approval rates, fraud losses, payroll disbursement anomalies, bill pay failure rates, and Capital portfolio health — with AI-generated narrative summaries for leadership. Develop reusable frameworks for policy impact sizing so that rule changes in Payments fraud, Payroll verification, Bill Pay limits, or Capital underwriting come to committee pre-quantified. Design and execute champion/challenger experiments across products (e.g., testing new authentication flows in Payments, alternative verification steps in Payroll, adjusted credit limits in Capital). Build loss forecasting and early warning systems tailored to each product's risk dynamics — real-time signals for Payments/Bill Pay, vintage-based models for Capital, disbursement pattern detection for Payroll. Optimize approval and intervention strategies through simulation and segmentation — reducing false declines in Payments, minimizing payroll holds for legitimate businesses, and right-sizing Capital offers. Enable self-serve analytics across product and ops teams by deploying AI-assisted query tools, so Tier-1 questions about QuickBooks risk metrics don't require analyst time. Ensure responsible AI usage by building internal playbooks for how AI-generated insights are applied in risk decisions across the QuickBooks ecosystem. Deliver executive strategy briefs that connect risk performance across all four products, positioning the Risk team as a growth enabler — not just a control function. Decision Optimization Across the Lifecycle Payments: Approval Strategy Optimization —ML-driven simulation of fraud rule thresholds; swap-set analysis quantifying revenue lift from reducing false declines while holding loss rates flat. Payroll: Disbursement Risk Scoring —Develop risk-tiered verification flows — streamlining onboarding for trusted businesses while adding friction only where exposure warrants it. Bill Pay: Authorization & Limit Optimization —Analyze payment failure patterns and unauthorized attempt clusters to refine per-customer limits and authentication triggers. Capital: Underwriting & Portfolio Strategy — Segmentation-driven offer optimization — right-sizing loan amounts and pricing based on QuickBooks behavioral data (revenue trends, payment history, payroll consistency). Scalable Frameworks & Organizational Leverage Cross-Product Champion/Challenger Platform — Standardized test-and-learn methodology that Payments, Payroll, Bill Pay, and Capital teams all use — with AI-powered monitoring and automated executive readouts of experiment results. Forecasting & Early Warning —Product-specific leading indicators (real-time velocity for Payments, payroll run anomalies for Payroll, vintage curves for Capital) unified into a single early warning view. Self-Serve Analytics for QuickBooks Risk — AI chatbot layer on curated data so product managers and ops leads can answer 'what's my approval rate for segment X this week' without filing a ticket. Strategic Influence & Enterprise Scale New Product/Feature Risk Assessment —LLM-assisted scenario generation for pre-launch risk sizing — e.g., launching instant payouts in Payments, expanding Capital to new segments, adding recurring Bill Pay features. Model Monitoring Automation —Continuous compliance pipelines with AI-flagged drift detection across all production models spanning the four product lines. AI Governance & Responsible Use Playbook —Auditable, explainable AI usage standards for risk decisions across the QuickBooks ecosystem. Executive Strategy Briefs — Quarterly 'state of QuickBooks risk' narratives connecting performance across Payments, Payroll, Bill Pay, and Capital to inform enterprise strategy. Qualifications 10+ years of experience in analytics, data science, or quantitative risk roles within financial services or fintech. Strong business acumen with demonstrated ability to translate analysis into strategic recommendations for senior leadership across multiple product lines. Expert SQL and Python/R skills; experience building automated reporting and analytical pipelines at scale. Experience supporting multiple business lines or products simultaneously — comfortable context-switching between payments, lending, and operational risk domains. Familiarity with AI/ML tools (LLMs, copilots, predictive modeling) and a track record of applying them to accelerate analytical output. Excellent communication skills — ability to present complex findings clearly to product leaders, engineers, and executives. Understanding of the small business financial ecosystem and how products like Payments, Payroll, Bill Pay, and Capital interconnect. Preferred Experience in payment fraud, ACH/RTP risk, payroll disbursement controls, or SMB lending/credit risk. Background in experimentation design (A/B testing, champion/challenger frameworks) in a product-driven environment. Experience with BI tools (Tableau, Looker, Power BI) and cloud data platforms (Databricks, Snowflake, AWS/GCP). Familiarity with regulatory requirements relevant to payments, lending, and payroll (BSA/AML, fair lending, NACHA rules). Prior experience at Intuit, QuickBooks, or a comparable SMB fintech platform. Graduate degree in a quantitative field (Statistics, Economics, Data Science, Engineering, or related). This role isn't just an analyst hire — it's a capability investment for the entire QuickBooks money movement ecosystem. The right person turns AI from a buzzword into a daily accelerant, ensures every major risk decision across Payments, Payroll, Bill Pay, and Capital is backed by evidence, and builds frameworks that scale as our products grow. Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: Mountain View $199,500 - $270,000 New York City $187,000 - $253,000 #J-18808-Ljbffr