Fraud Analyst - Personal Loans

About the Role We are looking for a Fraud Analyst to join our Risk Team, supporting the Personal Loans business by identifying, investigating, and mitigating fraud risks across the customer lifecycle. The role focuses on Early Warning Signals (EWS), trigger-based MIS, and fraud investigations, and works closely with Risk, Compliance, Operations, Product, and Technology teams. This is an analytical and investigative role, not manual risk grading. Key Responsibilities Monitor Early Warning Signals (EWS) to identify emerging fraud risks, adverse trends, and abnormal portfolio behaviour Review and investigate cases generated through trigger-based MIS, including rule-based and event-driven fraud alerts Conduct detailed fraud investigations, identify root cause, and document findings with clear rationale and evidence Review applications flagged for AML, PEP, and CTF risks and perform enhanced due diligence (EDD) where required Analyze credit bureau disputes to identify potential fraud, identity misuse, or misrepresentation Identify and track fraud typologies such as identity fraud, mule activity, collusion, and data manipulation Support decision-making on fraud tagging, account treatment, and preventive actions in collaboration with the Risk team Assist in investigations and responses for lender queries and regulatory escalations, including preparation of case summaries Analyze early delinquency accounts from a fraud-risk perspective and perform hind-sight analysis Perform root cause analysis on confirmed fraud cases and recommend risk control or process improvements Prepare and maintain fraud MIS, dashboards, and periodic reporting for Risk and senior management review Collaborate with Risk, Operations, Product, Compliance, and Technology teams to strengthen fraud detection and prevention frameworks Support internal audits, external audits, and regulatory inspections with data, analysis, and documentation What We’re Looking For Mandatory 1–3 years of experience in fraud risk, investigations, or risk analytics Strong working knowledge of Excel Knowledge of fraud typologies, AML concepts, and regulatory expectations Strong analytical, documentation, and communication skills Good to Have Working knowledge of SQL for data extraction and analysis Experience in NBFC / fintech / digital lending Exposure to personal loans or unsecured lending