🕵️‍♂️ Power BI Dashboard for Vehicle Insurance Fraud Analysis

Uncovering Patterns Through Data Visualization

Fraud detection is one of the most critical challenges facing the auto insurance industry. The cost of fraudulent claims can be massive, and the cost not just financially, but also in terms of eroded trust and increased premiums for honest customers. Data visualization tools like Power BI can help us uncover hidden patterns and build smarter and efficient prevention strategies.

Using the Vehicle Claim Fraud Detection dataset from Kaggle, I’ve developed an simple interactive Power BI dashboard that offers deep insight into fraudulent behavior across thousands of claims. I have used simple; Exploratory Data Analysis (EDA) techniques for this dashboard,  I have not used any complex DAX or Data Modeling.

Fraud Overview

I began with a high level summary to provide users with an immediate sense of the problem’s scale.

Key KPIs:

  • Total Claims: 15,000

  • Total Fraudulent Claims: 923

  • Fraud Rate: 5.99%

These metrics are visualized using KPI cards and a donut chart to compare the proportion of fraudulent to non-fraudulent claims. This helps establish a quick baseline for further analysis.

Demographics of Fraud

Next, I examined the relationship between fraud and policyholder demographics.

Key Insights:

  • Marital Status: Divorced individuals showed the highest fraud rate (96.20%), followed by singles and married.

  • Age Groups: The 31-40 age group had the highest fraud volume, with 597 combined fraudulent cases in the 31-35 and 36-40 brackets.

I used stacked bar charts and slicers to explore fraud by age, gender, and marital status. These insights can help insurers adjust risk models and build targeted interventions.

Vehicle & Policy Analysis

Fraud isn’t just about who, it’s also about what and how.

Key Findings:

  • Top 5 Fraud-Prone Makes: Pontiac, Toyota, Honda, Mazda, Chevrolet.

  • Policy Types with Highest Fraud %: Sport - Collision (14%), Utility - All Perils (12%), Sedan - All Perils (10%).

These were visualized through clustered bar charts, heatmaps to highlight patterns between vehicle make, type, and coverage. This is especially useful for product teams and fraud investigators who want to prioritize high-risk segments.

Monthly Fraud Trends

Using a line chart, I analyzed how fraud rates varied throughout the year. I found spikes in May, July, and October, with a noticeable dip in December.

This suggests potential seasonal or behavioral triggers that could help insurers predict and monitor periods of increased fraud risk.

Final Thoughts: Data Driven Fraud Detection

Building this Power BI dashboard revealed the power of interactive visual analytics in fraud detection. Not only does it make trends easier to spot, but it empowers claims analysts, investigators, and actuaries to:

  • Detect risky patterns early

  • Prioritize claims for investigation

  • Improve fraud prediction models

  • Optimize fraud prevention efforts

With data as our guide, we can move from reactive detection to proactive prevention saving money, time, and reputations in the process.