In today’s data-driven world, insurance companies are under pressure to extract more value from their data. Whether it's to identify high-risk customers, streamline claims processing, or enhance profitability, a well-designed dashboard can provide a single source of truth for decision-makers
With this Auto Insurance Claims and Policyholder Risk Analysis Dashboard, built using Power BI and a real-world dataset from Kaggle. The objective is to uncover insights about claim patterns, risk segments, and premium profitability.
Dashboard Overview
The dashboard is split into two core analytical themes:
Claims & Risk Analysis
This section focuses on identifying risk factors among policyholders and analyzing the nature of insurance claims.
Premium vs. Claims Profitability
This section uncovers how different customer segments perform from a revenue and cost perspective.
Together, these dashboards tell a comprehensive story: Who’s claiming the most? Where is risk concentrated? And how profitable are the clients?
Key Metrics at a Glance
At the top of the dashboard, you’ll see several KPI cards that summarize essential metrics:
-
10,000 Total Clients
-
4,972 Total Claims
-
22M Total Premium Collected
-
368K Total Adjustments Paid
-
74 Average Claim Amount
Claims & Risk Analysis
This section helps answer the question: Where is our exposure?
Claims Severity Breakdown
A bar chart shows most claims are of low severity, with only a small number being high-risk. This is reassuring for underwriters but also highlights opportunities to focus on medium-risk cases, where fraud or policy misuse may hide.
Regional Trends
An area chart breaks down claims severity by region. Urban areas dominate the claim volume, and not surprisingly, they also show higher severity, possibly due to traffic density or higher repair costs.
Marital Status and Claims
A pie chart shows married individuals account for almost half of all claims. Single and divorced policyholders also make up a significant portion, which may influence pricing strategies.
Claims by Age
Age groups between 20-60 are most active in claims, especially ages 30-50. This might correlate with peak driving years and higher vehicle ownership.
Claims Frequency by Region
Urban and suburban clients submit the most claims, together accounting for nearly 80% of the total. This suggests both risk and opportunity are highest in densely populated areas.
Premium vs. Claims Profitability
While the risk side is important, insurers also need to know: Are we making money?
Premiums by Region
-
Urban: $11M (50%)
-
Suburban: $7M (30%)
-
Rural: $4M (20%)
The urban market brings in the most revenue, but as we saw earlier, it's also where risk is concentrated.
Frequency vs. Premium
A scatter plot compares claim frequency to premium amount. There's a visible trend: higher premiums generally correlate with higher claim frequencies. This insight can help pricing analysts fine-tune models for more accurate risk-based pricing.
Power BI & DAX
The dashboard was built using Power BI Desktop, with data transformations handled via Power Query and calculations powered by DAX. A few of the measures used:
Avg Claim Amount = AVERAGE('Claims'[Claim_Amount])
Claim Frequency = COUNT('Claims'[Claim_ID]) / DISTINCTCOUNT('Policyholders'[Policy_ID])
Total Adjustment = SUM('Claims'[Adjustment_Amount])
Calculated columns were used to classify claims into severity categories (Low, Medium, High).
Filters & Interactivity
To make the dashboard dynamic and customizable, I included slicers for:
-
Region (Urban, Suburban, Rural)
-
Policy Type (Full Coverage / Liability-Only)
-
Age Group
-
Marital Status
These allow users to drill into specific segments of the customer base and make more targeted decisions.
Final Thoughts
This Power BI dashboard demonstrates how a combination of visual storytelling, data modeling, and domain-specific metrics can uncover the meaningful insights of a insurance company. Whether you're an actuary, underwriter, or data analyst, having a real-time view of claims, risk, and profitability is a powerful asset for smarter decision making.