Fraud Prevention Analyst job description

Job brief

We are seeking a detail-oriented Fraud Prevention Analyst to join our Risk Management team and protect our global transaction platform. You will be responsible for investigating complex fraud cases, refining our automated risk policies, and serving as a key subject matter expert during incident response. This is an exciting opportunity to influence our security roadmap while directly impacting the bottom line of a fast-growing financial services organization. If you possess a keen investigative mindset and a passion for data-driven security, we want to hear from you.

What is a Fraud Prevention Analyst?

A Fraud Prevention Analyst serves as a critical line of defense, monitoring transaction data and behavioral patterns to detect illicit activity. By utilizing advanced fraud detection software and predictive analytics, a Fraud Prevention Analyst identifies anomalies in real-time to safeguard company assets and customer trust. This role bridges the gap between technical data analysis and loss prevention, ensuring that security measures align with evolving financial regulations and industry best practices.

What does a Fraud Prevention Analyst do?

A Fraud Prevention Analyst investigates flagged transactions using platforms like Sift, Kount, or Feedzai, verifying customer data to distinguish legitimate activity from sophisticated financial attacks. They collaborate with payment processors, compliance departments, and legal teams to resolve security incidents, submit suspicious activity reports (SARs), and update risk rules. By analyzing historical breach data, a Fraud Prevention Analyst continuously optimizes detection algorithms to balance high-security standards with a frictionless user experience.

Key responsibilities

• Analyze real-time transaction data and user activity logs to detect patterns indicative of account takeover, identity theft, or payment processing fraud.
• Investigate high-risk alerts escalated by automated systems, conducting manual reviews of customer accounts and digital footprints to determine legitimacy.
• Draft and maintain internal risk rule sets within tools like Signifyd or LexisNexis to proactively block malicious actors before financial losses occur.
• Compile detailed investigation reports for management and law enforcement, documenting evidence of fraudulent activity and regulatory compliance violations.
• Coordinate with customer support and payment operations teams to reconcile disputed charges, chargebacks, and potentially compromised financial data.
• Perform deep-dive root cause analysis on recurring fraud incidents to identify vulnerabilities in our checkout process or user authentication workflows.
• Stay current on emerging financial crime trends, such as synthetic identity fraud and card-not-present (CNP) schemes, to adapt security strategies.
• Audit existing internal controls and access management policies to ensure the organization remains compliant with PCI-DSS and related security standards.

Requirements and skills

• Bachelor’s degree in Criminal Justice, Finance, Data Science, or a related field, or equivalent experience in financial investigations.
• 3+ years of experience working in an e-commerce, banking, or fintech environment with a focus on fraud detection or risk mitigation.
• Proficiency in SQL or similar query languages for extracting and manipulating large datasets for investigative and reporting purposes.
• Hands-on experience using industry-standard fraud management platforms like Sift, Kount, Feedzai, or IPQualityScore for real-time monitoring.
• Solid understanding of PCI-DSS compliance requirements and the lifecycle of financial chargebacks and dispute resolution processes.
• Professional certification such as Certified Fraud Examiner (CFE) or Certified Anti-Money Laundering Specialist (CAMS) is highly preferred.
• Proven ability to articulate complex technical findings to non-technical stakeholders, including legal teams, executives, and customer success managers.
• Demonstrated expertise in analyzing device fingerprinting, geolocation data, and behavioral biometrics to verify user intent and identity.

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