Senior Data Analyst
Mphasis
02.2025 - Current
- Project: Insurance Claims Analysis & Fraud Risk Dashboard
- Role and Responsibilities:
- Designed and delivered 10+ interactive Power BI dashboards covering policy sales, renewals, persistency, claims, and customer analytics, improving leadership visibility by 35%.
- Developed and optimized 100+ complex SQL queries (joins, CTEs, window functions) to extract and transform data from multiple insurance systems, reducing data preparation time by 40%.
- Built enterprise-grade Power BI data models (star schema), supporting datasets of 5–10 million+ records, improving report performance by 30%.
- Created 50+ DAX measures and KPIs, including persistency ratio, renewal rate, lapse ratio, loss ratio, and premium growth, enabling accurate business tracking with 99% data accuracy.
- Automated recurring MIS and operational reports, eliminating manual Excel-based reporting, and reducing reporting effort by 50%.
- Implemented Row-Level Security (RLS) for 5+ user roles, ensuring secure, role-based access, and compliance with internal data governance standards.
- Performed trend, variance, and cohort analysis to identify high-lapse and high-risk segments, supporting initiatives that improved policy renewal rates by 8% to 12%.
- Analyzed insurance claims data to identify abnormal claim patterns and loss-making products, contributing to a 10% reduction in claims leakage.
- Collaborated with 10+ cross-functional stakeholders (sales, underwriting, claims, finance, operations) to translate business requirements into analytics solutions with a faster turnaround time (25%).
- Optimized SQL queries and Power BI refresh processes, reducing dashboard refresh duration by 35%, and improving system reliability.
- Delivered actionable insights and executive presentations that supported data-driven decisions impacting ₹50+ crore in policy premium portfolios.
- Maintained detailed documentation of KPIs, business logic, and data definitions, supporting audit readiness, and long-term analytics scalability.
- Key Achievements & Outcomes:
- Delivered data-driven Power BI dashboards that improved business decision-making speed by 30%–40% for senior leadership and functional heads.
- Enabled identification of high-lapse and low-persistency segments, contributing to an improvement in policy renewal rates by 10%+ through targeted retention initiatives.
- Reduced manual reporting dependency by 50% by automating MIS and performance reports using SQL-driven datasets and Power BI.
- Improved data accuracy and reporting reliability to over 99% by implementing structured data validation, reconciliation checks, and standardized KPI definitions.
- Supported risk and claims teams in identifying loss-making products and abnormal claim patterns, contributing to an estimated 8–12% reduction in claims leakage.
- Enhanced dashboard performance and scalability by optimizing SQL queries and Power BI data models, resulting in 30–35% faster report refresh times.
- Enabled secure and compliant data access by implementing Row-Level Security (RLS) across multiple business roles, ensuring adherence to internal governance standards.
- Provided actionable insights that influenced strategic decisions across ₹50+ crore premium portfolios, supporting revenue growth, and profitability analysis.
- Improved stakeholder satisfaction by 25%+ through intuitive dashboards, drill-down analysis, and self-service BI capabilities.
- Established reusable SQL views and Power BI templates, reducing future development effort by 20–25%, and accelerating new report delivery.
- Challenges Addressed:
- Handled large and complex insurance datasets by optimizing SQL queries and implementing efficient Power BI star-schema data models, improving performance by over 30%.
- Resolved data quality and reconciliation issues across policy, premium, and claims systems through standardized validation checks and KPI definitions.
- Eliminated manual and error-prone MIS reporting by automating reports using SQL views and Power BI dashboards, reducing effort by 50%.
- Addressed slow dashboard performance by optimizing DAX measures, data models, and refresh strategies, reducing report load, and refresh times significantly.
- Ensured secure and role-based data access by implementing Row-Level Security (RLS) and designing intuitive self-service dashboards for business users.