**Overview** Now, we are expanding our focus to Consumer Lending and banking within the Consumer Group. We are seeking a brilliant and passionate Staff Analyst for Fraud Policy to be a key player in safeguarding our ecosystem and building unwavering trust with our customers focusing on consumer banking products. This isn't just a job; it's an opportunity to be at the forefront of the fight against fraud in the dynamic world of fintech. You’ll work closely with fraud strategy, data science, engineering, and customer support teams to ensure we deliver a secure and seamless experience for our customers while minimizing fraud losses and maintaining compliance with applicable regulations. **What you'll bring** ● 5–7 years of experience in fraud operations or fraud analytics, preferably in fintech, banking● Strong understanding of fraud typologies relevant to consumer financial products (e.g., synthetic identity, phishing/ATO, 1st/3rd party fraud, ACH disputes).● Experience with fraud tools and platforms (e.g., Alloy, Sift, Ekata, Kount, etc.)● Proficiency in analyzing fraud data using SQL and/or Excel; familiarity with Looker or other BI tools is a plus.● Familiarity with banking regulations and fraud return codes (e.g., ACH NACHA rules, Reg E, KYC/AML basics).● Strong analytical, investigative, and problem-solving skills.● Excellent communication and documentation skills. **Nice to Have** ● Experience in fraud for digital banking, challenger banks, or fintech products.● Exposure to fraud rules management or machine learning-based detection systems.● Prior work with ACH return reason codes, dispute handling, or chargeback processes. **How you will lead** **Fraud Detection & Investigation** ● Monitor alerts and analyze real-time signals to detect suspicious activity (e.g., account takeover, check kiting, synthetic identity, mule activity, ACH fraud, remote access tools). ● Investigate and resolve fraud cases across onboarding, KYC, account funding (e.g., ACH, Plaid, RTP), and account usage stages. ● Leverage fraud tools and external vendor data to review high-risk activity and recommend actions. **Data-Driven Analysis** ● Analyze fraud trends and patterns using data analytics tools (SQL, Excel, dashboards) to identify emerging threats or gaps in current detection logic. ● Support creation and tuning of fraud detection rules, machine learning models, and velocity checks in collaboration with fraud strategy and data teams. ● Track key fraud KPIs (e.g., fraud loss rates, false positive rates, return reasons like R05/R10) and prepare reports for stakeholders. **Operational Efficiency & Documentation** ● Respond to fraud-related escalations from customer support and other internal teams. ● Assist in refining workflows and automations to improve response time and operational throughput. ● Document fraud scenarios, case investigations, root cause analysis, and preventive actions. **Cross-Functional Collaboration** ● Work closely with compliance, risk policy, customer support, engineering, and external vendors. ● Participate in projects related to new product launches, fraud tooling enhancements, and regulatory reviews (e.g., SAR filings support). ● Provide fraud subject matter expertise in discussions related to account security, identity verification, and payment flows. Intuit provides a competitive compensation package with a strong pay for performance rewards approach. The expected base pay range for this position is: Bay Area California $172,000- $232,500 This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. EOE AA M/F/Vet/Disability. Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.