Intuit Manager Fraud Risk 

United States, California, Mountain View 

477330304

This individual will be continually evaluating and onboarding new products and tools for underwriting and exposure management and will actively participate with leaders across lending products to develop a coordinated strategy for external vendor usage. They will be an active proponent of leveraging AI and automation in developing analytical insights, updating models and deploying strategy changes.

• Develop, implement, and continuously refine fraud risk management strategies across credit card underwriting, transaction monitoring, and exposure management
• Leverage data driven and machine learning-enabled approaches to devise fraud defenses and rapidly identify emerging fraud trends in the program
• Partner with the Data Science team to evaluate and enhance fraud model performance, identify modeling needs and lay out model development agenda
• Build a comprehensive fraud monitoring framework, including early warning indicators, trend analysis, and performance dashboards
• Establish clear performance metrics and maintain accountability for fraud loss rates, detection efficiency, and customer impact
• Drive cross-functional collaboration with Product, Engineering, Compliance, Marketing, and Operations to balance fraud prevention with seamless customer experience
• Evaluate, onboard, and manage external fraud tools, vendors, and partnerships to ensure state-of-the-art capabilities
• Advocate for the use of AI, automation, and advanced analytics to improve fraud detection speed, scalability, and precision
• Develop and maintain fraud policies, governance frameworks, and escalation protocols for the credit card program
• Hire, mentor, and develop a high-performing fraud analytics team, fostering innovation, accountability, and career growth
• Represent the Fraud Risk function with senior stakeholders, clearly communicating risks, trade-offs, and strategic recommendations

• At least 6 years of experience in fraud risk analytics and risk management, including demonstrated people leadership and team development
• Familiarity with risk scoring and modeling techniques including regression modeling, multivariate analyses and machine learning, and their application in fraud prevention
• Proven expertise with industry-standard tools, fraud platforms, and best-in-class risk models
• Excellent analytical and problem-solving skills with a proven track record of converting analysis insights into business recommendations
• Exceptional verbal and written communication skills to effectively communicate complex ideas and influence audiences in decision making
• Proficiency with analytical packages and tools such as SQL, SAS, Python, R and Tableau
• Bachelor’s degree in Business, Finance, Economics, Mathematics, Engineering or other quantitative disciplines