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 current 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 current 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 transparent 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.

📌 Senior Fraud Analyst Gurugram
🏢 Hero FinCorp
📍 Gurugram