Fraud Analyst preventing, detecting, and responding to fraud while supporting customer security at Fidelity Investments. Lead efforts in fraud risk assessments, analytics, and cross-functional collaboration.
Responsibilities
Analyze large datasets to detect suspicious activity, fraud patterns, and emerging threats
Develop or refine fraud detection rules, models, and strategies using analytics, AI/ML, or anomaly detection tools
Build dashboards, KPIs, and reporting frameworks to monitor fraud trends, losses, customer experience, and control performance
Partner with data science, product, and technology teams to design and implement scalable fraud controls
Triage fraud events, conduct root cause analysis, and manage incident response efforts
Investigate suspicious financial activity, reviewing transactions, behaviors, and digital interactions
Document cases, gather supporting evidence, and collaborate with law enforcement or regulatory agencies when appropriate
Conduct fraud risk assessments across products, processes, and business units
Identify key risks, evaluate controls, and document risk/control statements
Recommend enhancements to policies, processes, and controls to reduce fraud exposure
Provide fraud subject matter expertise to partners across product, engineering, legal, compliance, cybersecurity, analytics, and operations
Influence strategic initiatives by advising on fraud risk considerations, control design, and implementation requirements
Communicate findings, insights, and recommendations to stakeholders and senior leadership
Requirements
Bachelor’s degree in finance, business, economics, data science, cybersecurity, or related field
2–12+ years of fraud, risk management, compliance, audit, cybersecurity, or financial services experience (level dependent)
Experience with fraud analytics, risk assessments, investigations, or fraud control design
Familiarity with fraud risks across digital channels, payments, account onboarding, authentication, or card programs
Hands on experience with analytics tools (e.g., Python, SQL) is a plus and may be required for technical roles
Understanding of fraud detection technologies, behavioral analytics, anomaly detection, or machine learning based solutions
Knowledge of fraud trends, regulatory expectations, and industry best practices
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