This project analyzes insurance claim costs across regions, claim types, severity levels, and time periods to help insurance leadership identify high-cost drivers, improve underwriting decisions, and prioritize pricing actions.
The dashboard provides a centralized view of insurance claim performance through financial, operational, and risk-focused KPIs.
Key results include:
- Total Claim Cost: $3,871,652
- Average Claim Amount: $3,872
- Total Claims: 1,000
- Year-over-Year Cost Change: 94.53% Increase
The analysis highlights how claim severity, regional concentration, and claim type distribution influence insurance loss exposure and financial risk.
Built using SQL, Tableau, Python, Streamlit, Excel, and Power Query.
Insurance organizations face increasing claim costs driven by:
- High-severity claims
- Regional loss concentration
- Rising claim frequency
- High-cost claim categories
- Inefficient pricing and underwriting decisions
Without centralized reporting, leadership teams may struggle to identify:
- High-risk claim categories
- Cost concentration trends
- Regional loss exposure
- Underwriting improvement opportunities
- Pricing strategy adjustments
This project provides an executive-level decision-support solution that transforms insurance claims data into actionable business insights.
This dashboard supports insurance leadership, underwriting teams, risk managers, and claims operations teams by helping them:
- Monitor claim cost performance
- Identify high-risk claim categories
- Evaluate severity trends
- Detect regional loss concentration
- Prioritize underwriting interventions
- Support pricing strategy decisions
- Improve financial risk management
The dashboard answers key business questions such as:
- Which claim types create the largest financial exposure?
- Which regions require closer monitoring?
- Which severity levels contribute most to losses?
- Where should pricing reviews be prioritized?
- How should risk management resources be allocated?
The dashboard monitors:
| KPI | Business Purpose |
|---|---|
| Total Claim Cost | Measures overall financial exposure |
| Average Claim Amount | Measures average claim severity |
| Total Claims | Measures claim volume |
| YoY Cost Change | Measures cost growth trends |
| Claim Cost by Region | Identifies regional exposure |
| Claim Cost by Claim Type | Identifies category exposure |
| Severity Distribution | Measures risk concentration |
| Monthly Cost Trend | Tracks claim cost performance |
| High-Cost Claim Exposure | Identifies major financial risk |
The dashboard provides a comprehensive view of insurance claim performance through:
- Executive KPI Monitoring
- Regional Claim Cost Analysis
- Claim Type Performance Evaluation
- Severity Distribution Monitoring
- Monthly Cost Trend Reporting
- High-Cost Claim Exposure Tracking
The objective is to provide insurance leaders with a centralized reporting solution for monitoring financial risk and supporting data-driven decision-making.
Property claims and high-severity incidents account for the largest share of claim costs, making them the primary drivers of insurance loss exposure and financial risk.
This project demonstrates how insurance organizations can use analytics to:
- Improve pricing decisions
- Strengthen underwriting strategies
- Monitor claim cost growth
- Reduce loss exposure
- Improve executive reporting
- Enhance financial risk visibility
- Support data-driven insurance operations
The dashboard provides leadership teams with actionable visibility into claim performance and loss concentration.
Prioritize pricing reviews, underwriting controls, and risk monitoring efforts for high-cost claim categories and high-severity segments to reduce future loss exposure and improve financial performance.
| Column | Description |
|---|---|
| claim_id | Unique claim identifier |
| claim_date | Date claim was filed |
| region | Geographic claim region |
| claim_type | Insurance claim category |
| severity | Claim severity classification |
| claim_amount | Monetary value of the claim |
| policy_id | Policy identifier |
| customer_id | Customer identifier |
| broker_id | Broker identifier |
| claim_status | Current claim status |
The repository includes SQL queries supporting:
- KPI Analysis
- Regional Claim Cost Analysis
- Severity Evaluation
- Claim Type Performance Analysis
- High-Cost Claim Identification
SQL File:
sql/insurance_claim_queries.sql
The exploratory data analysis and feature engineering process includes:
- Dataset structure review
- Data type validation
- Date formatting validation
- Missing value assessment
- Duplicate record validation
- Claim amount range validation
- Categorical value standardization
Created analytical fields including:
- Month
- Quarter
- Year
- High-Cost Claim Flag
- Cost Band Categories
- Claim Severity Segments
- Claim distribution by region
- Claim cost by claim type
- Severity analysis
- Monthly trend analysis
- Outlier identification
- Cost concentration analysis
Business Problem
↓
EDA + Cleaning
↓
Feature Engineering
↓
SQL Transformations
↓
Risk Analysis
↓
Dashboard Development
↓
Business Insights
↓
Decision Support
↓
Business Impact
Property claims and high-severity incidents are the primary drivers of insurance loss exposure and claim cost growth.
Increase monitoring of high-cost claim categories and regions with elevated loss activity.
Review pricing models, underwriting criteria, and risk controls for the highest-cost claim segments.
Prioritize risk management resources toward regions and claim categories generating the greatest financial impact.
- SQL
- Tableau
- Python
- Pandas
- Streamlit
- Excel
- Power Query
- GitHub
Claims-Cost-Risk-Analytics/
├── README.md
├── requirements.txt
├── streamlit_app.py
├── data/
│ ├── raw/
│ └── cleaned/
├── notebooks/
│ └── insurance_claims_eda.ipynb
├── screenshots/
│ ├── dashboard_overview.png
│ ├── kpi_summary.png
│ ├── claim_distribution.png
│ ├── claim_type_comparison.png
│ └── cost_trend.png
├── sql/
│ └── insurance_claim_queries.sql
git clone https://github.com/yourusername/Claims-Cost-Risk-Analytics.gitpip install -r requirements.txtstreamlit run streamlit_app.py- Dataset is synthetic and created for portfolio purposes.
- No real customer, broker, policyholder, or insurance company information is included.
- Project developed for educational and demonstration purposes.
- Business impact examples are illustrative and intended to demonstrate analytical decision-making.