Bengaluru, Karnataka, India
I am a Risk & data analyst with experience in fraud detection, transaction monitoring, customer due diligence (CDD), and financial crime investigations across fintech and payments environments. Currently working as a Fraud Detection Analyst at Bazaarvoice with prior experience at PayPal handling complex financial risk cases. Strong understanding of AML regulations, suspicious activity identification, sanctions screening, and risk mitigation. Skilled in data analysis using Excel and investigative tools to detect and prevent financial crime. I have hands-on experience working with SQL, Python, and Power BI to analyze large datasets, identify trends, and generate actionable insights that support fraud prevention strategies. My work involves deep-dive analysis, fraud pattern identification, and supporting fraud rule evaluation to reduce false positives while maintaining strong risk controls. Having worked with global payment ecosystems like PayPal and currently in fraud detection, I bring strong analytical thinking, attention to detail, and a problem-solving mindset focused on balancing fraud prevention with seamless customer experience. 📍 Based in Bangalore, open to remote/hybrid roles across India or globally. Let’s connect if you’re hiring, working on exciting data challenges, or just want to share ideas!
Analyze high-volume transactional and behavioral data to identify fraud patterns, anomalies, and emerging risks across digital platforms. Conduct detailed investigations into account takeovers (ATO), suspicious user activity, and payment fraud signals using multiple data points and behavioral indicators. Support fraud strategy initiatives by identifying gaps in existing fraud detection rules and recommending enhancements to improve detection accuracy. Monitor fraud trends and contribute to reporting frameworks using SQL and Power BI, enabling data-driven decision-making. Collaborate with cross-functional teams (risk, operations, product) to balance fraud prevention with seamless customer experience and reduced false positives.
Analyzed high-volume payment transaction data to uncover trends in payment failures, refund patterns, and checkout abandonment. Partnered with risk and compliance teams to monitor suspicious activity and define alerting criteria for potential fraud. Built and maintained dashboards tracking transaction success rates, settlement timelines, and user payment behavior. Identified anomalies in payment routing, duplicate transactions, and unclaimed funds, contributing to improved process reliability. Automated financial reporting and reconciliation tasks using SQL and Excel, reducing manual workload and increasing accuracy. Delivered strategic insights to finance, product, and engineering teams on payment flows across different geographies and platforms.
Performed detailed analysis of high-volume transactions across C2C (peer-to-peer) and C2B (merchant) payment flows to detect fraudulent activity. Investigated unauthorized transactions, disputes, and chargebacks to identify root causes, fraud patterns, and potential system vulnerabilities. Conducted account-level investigations to detect ATO, suspicious login behavior, and payment anomalies using transactional and behavioral data. Supported fraud rule evaluation by identifying false positives and missed fraud scenarios, contributing to improved detection logic. Utilized SQL, Python, and Excel to extract, analyze, and interpret large transactional datasets for actionable insights. Contributed to improving authorization decisions by identifying legitimate transaction behaviors and reducing unnecessary declines. Ensured compliance with risk policies while maintaining a balance between fraud prevention and customer experience.
Reviewed and assessed transactions across digital payment channels and e-commerce platforms to identify potential fraud risks. Identified suspicious activity patterns and escalated high-risk cases based on predefined fraud indicators and risk thresholds. Supported fraud prevention workflows and ensured consistency in decision-making aligned with operational risk guidelines and SLAs. Assisted in identifying fraud trends, operational gaps, and opportunities for process improvements.