Lifecycle-based analytics of auto-renewal policies, analysing customer awareness, timing risk, escalation drivers, and preventable complaint patterns.
End-to-end Power BI dashboard for Shield Insurance tracking revenue, customers, DRG/DCG growth, trends, and segmentation by city, sales mode, age group, and policy ID.
Synthetic personal-lines insurance portfolio built as a governed digital twin, with dataset freezing, validation gates, and actuarial realism.
Insurance policy and claims analytics report built with SQL Server integration, DAX lifecycle logic, and automated scheduled refresh.
Production-style regression project for predicting insurance claim amounts using advanced modeling techniques, feature analysis, and business-driven insights.
Production-style ML system for automated insurance claims decisions using a SQL gold dataset, rule + model scoring, FastAPI inference, and explainable outputs.
> Shield Insurance Analytics > A data-driven approach to analyzing revenue trends, customer segmentation, and sales mode performance in the insurance sector. This project provides structured insights to optimize engagement strategies, enhance policy targeting, and improve overall business decision-making.
SQL + Power BI case study detecting claim severity & leakage: reserve adequacy, vendor IQR outliers, missed subrogation/late FNOL, duplicate payments. End-to-end T-SQL (staged → core → mart) + executive dashboards.
End-to-end BI project simulating and analyzing a car insurance portfolio with 1M+ records to derive insights on claims trends, loss ratio, and business risk using Python, SQL and Power BI.
3-page Power BI insurance report with executive KPIs, drill-through customer details, and feedback sentiment insights using a word cloud.
End-to-end motor insurance analytics project using a two-stage (hurdle) modeling approach to predict claim occurrence and claim severity. Combines business analysis, machine learning, and risk insights to support underwriting, pricing, and claims optimization in General (P&C) Insurance.
Predicting insurance charges and identifying key risk divers using regression and regularization.
End-to-end analytics project testing whether Australian macroeconomic indicators (ABS, RBA, APRA) can predict general insurance claim severity 2–4 quarters in advance. Includes lead-lag correlation analysis, OLS/Ridge/Lasso regression, stress-test scenarios, and an interactive Streamlit dashboard.
Insurance Company Analytics is a Power BI and Excel project focused on evaluating customer demographics, total policies, and total claims. Using Excel for data preparation and Power BI for visualization, the project highlights overall company performance and key metrics to support better business decisions.
Insurance risk analytics for 10 Academy. Includes EDA, DVC, A/B testing, and XGBoost modeling for claim severity and pricing, with SHAP analysis.
An interactive Business Intelligence (BI) dashboard developed in Tableau to visualize insurance claims frequency, regional risk distribution, and policy profitability through dynamic KPI tracking and geospatial mapping.
A Power BI dashboard analyzing Shield Insurance's customer segmentation, revenue trends, and sales performance across cities, age groups, and sales modes.
Geospatial underwriting lab for climate, terrain, and exposure-informed insurance decisions.
Insurance analytics project comparing flat vs risk-based pricing under catastrophe risk using A/B testing, Monte Carlo simulation, VaR/TVaR, and Streamlit.
An econometrics tutorial using data from the USDA's Federal Crop Insurance Corporation
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