● Processed 100+ policy cancellations and refunds daily, ensuring full regulatory compliance and improving refund turnaround time by 30%.
● Analysed customer arrears and payment behaviour using Power BI and Looker, contributing to a 20% reduction in payment defaults through early risk identification.
● Supported operational decision-making by extracting and validating customer and transaction data for cross-functional teams.
● Collaborated with Claims, Fraud, and Underwriting teams to streamline workflows and reduce case-handover delays.
● Designed standardised customer communication templates, reducing response time by 20% and improving process consistency.
Key Projects:
Product-Level Loss Driver Analysis (SQL, Power BI).
● Analysed transactional sales data to identify why a product generated losses despite positive overall performance.
● Performed product-, customer-, discount-, and time-based analysis using SQL (SQLite).
● Identified extreme discounting (70–80%) as the primary loss driver; ruled out operational factors.
● Built a Power BI dashboard with an executive overview, product funnel, and loss driver analysis.
Supply Chain Data Analysis (Python).
● Conducted exploratory supply chain analysis using Python (pandas, matplotlib).
● Analysed demand patterns, lead times, and inventory-related metrics.
● Communicated insights through structured notebooks and visual summaries.
