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The Senior Fraud Analytics Analyst is responsible for overseeing day-to-day fraud analytics activities, ensuring data-driven insights are accurately generated and applied. The role serves as a key point of control within the team, providing guidance, validation, and support across analytical workflows while maintaining strong governance standards.
Job Responsibility:
Review and validate analytical outputs to ensure quality, consistency, and accuracy
Support identification of fraud trends, patterns, and anomalies through data analysis
Provide guidance on analytical approaches and methodologies
Actively contribute to analytical activities where required, ensuring continuity and operational resilience
Support team members in handling complex or high-risk scenarios
Provide subject matter expertise across fraud analytics processes
Ensure adherence to established processes, policies, and control frameworks
Monitor outputs for potential risks or inconsistencies and initiate corrective actions
Support audit and compliance activities through accurate documentation and validation
Support shift-based coverage ensuring continuity of operations across defined business hours
Coordinate with team members to manage workload effectively
Escalate key issues or risks as appropriate
Requirements:
Bachelor's degree in Data Science, Statistics, Finance, Computer Science, or a related field
2-4 years of experience in fraud analytics, fraud risk operations, or MIS reporting or similar within banking, fintech, or payment services
Willing to work in shifting schedule, 24/7 with possible weekends and holidays
Strong experience in fraud analytics or fraud risk management
Ability to review, validate, and interpret complex analytical outputs
Strong hands-on skills in SQL, Excel, and at least one analytics or scripting tool (e.g., Python, R, SAS)
Familiarity with BI tools such as Tableau, Power BI, or similar is preferred
Experience with fraud detection platforms or rule engines
Nice to have:
Familiarity with BI tools such as Tableau, Power BI, or similar is preferred