Senior Fraud and Risk Analyst - Fintech

Overview

We are seeking a Senior Staff Analyst to join the Quick Books 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 Quick Books 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.

Quick Books 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.

• Serve as the primary risk analytics partner across Quick Books 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 Quick Books 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 Quick Books 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.

• 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 Quick Books behavioral data (revenue trends, payment history, payroll consistency).