Senior Fraud Analyst

• Analyze end-to-end lending lifecycle data (application, onboarding, bureau, repayment, device) to identify fraud patterns and high-risk segments
• Track key fraud indicators such as First Payment Default (FPD), Early Payment Default (EPD), and abnormal delinquency trends
• Perform deep-dive analyses and root-cause investigations on fraud spikes, portfolio deterioration, and channel-level risks
• Support development, testing, and optimization of fraud rules, score cut-offs, and risk triggers to balance fraud capture and customer experience
• Build and maintain analytical datasets (feature marts) by combining internal and external data sources for fraud detection and monitoring
• Collaborate with Fraud Control Unit (FCU), Risk, Credit, and Business teams to provide data-backed insights and investigation inputs
• Develop and maintain fraud monitoring reports and dashboards using tools such as SQL, Python, and Power BI
• Assist in exploring new data sources (bureau, alternate data, device, telecom, etc.) and contribute to their evaluation for fraud use cases
• Support development of machine learning models and analytical frameworks for anomaly detection, behavioural segmentation, and fraud risk prediction
• Leverage basic Generative AI tools (LLMs, prompt-based workflows) for exploratory analysis, summarisation of fraud cases, and signal identification
• Participate in POCs and pilot programs to evaluate new fraud detection techniques, models, and data capabilities
• Translate identified fraud patterns (e.g., synthetic identities, mule accounts, sourcing fraud) into actionable analytical features and rules
• Present insights, findings, and recommendations to stakeholders in a clear and structured manner
• Strong analytical experience in fraud/risk analytics for digital or retail lending portfolios.
• Ability to work with large datasets and derive actionable insights for fraud detection and

risk mitigation.

Skills Required
Machine Learning, Power Bi, Sql, Python