Insurance fraud remains one of the major challenges affecting loss ratios and operational efficiency across the auto insurance industry. To better understand fraud patterns and highlight high-risk customer segments, I built an interactive Auto Insurance Fraud Analysis Dashboard using Power BI. This dashboard combines demographic data, claim history, vehicle information, and policy details to uncover meaningful insights for underwriting, pricing, and fraud investigation teams.
📊 Key Insights From the Dashboard
The analysis revealed several important patterns across customer demographics and vehicle characteristics. One of the most notable findings was the variation in fraud percentage by marital status. Divorced policyholders exhibited the highest fraud rate at approximately 96%, slightly above single, married, and widowed individuals, all of whom still maintained unusually high rates above 92%. This suggests that personal or financial transitions may influence fraudulent behaviour more than previously assumed.
Age group analysis showed that fraud is most common among policyholders aged 31–35, followed by those 36–40 and 41–50. These age segments tend to have active financial responsibilities—mortgages, car payments, childcare—which may explain a higher tendency to inflate or falsify claims. Younger groups (below 25) and older groups (above 65) had significantly lower fraudulent claim activity.
When examining vehicle characteristics, certain makes stood out. The top five vehicle brands associated with the highest number of fraudulent claims included Pontiac, Toyota, Honda, Mazda, and Chevrolet. This insight helps insurers adjust risk scoring models and apply additional screening for claims involving these vehicle types, especially in high-risk geographic regions or policy categories.
Fraud behaviour also demonstrated seasonal trends. The monthly fraud percentage showed noticeable spikes in March, June, and August, periods commonly associated with tax refunds, summer travel, and higher claim activity. Understanding these seasonal peaks allows fraud teams to allocate investigation resources more efficiently.
Finally, policy type analysis revealed that fraud was most prevalent within Sport – Collision, Utility – All Perils, and Sedan – All Perils coverages. Collision-heavy policies and comprehensive protection plans tend to present more opportunities for misrepresentation, highlighting the need for tighter controls and verification procedures in these product categories.
🛠️ Methodology, Data Sources & Modeling Approach
This dashboard was created using a structured dataset consisting of policy records, customer demographics, vehicle attributes, claim outcomes, and fraud flags. The data model followed a star schema, including a central fact table (FactClaims) connected to dimensions such as DimCustomer, DimVehicle, DimPolicy, and DimDate. These relationships allowed seamless filtering and drill-down capabilities across different segments.
Power BI’s modelling capability was leveraged to create key DAX measures that calculated fraud counts, fraud percentages, and ranking metrics. Custom tooltips were added for deeper insight during visual hover interactions, while conditional formatting highlighted red-flag segments. Bookmarks and slicers were included to allow analysts to explore fraud patterns by gender, marital status, vehicle type, or policy category.
🚀 Conclusion
Power BI enables insurance companies to transform raw claim data into actionable intelligence. By visualizing fraud patterns across demographics, vehicles, policies, and time, insurers can strengthen their fraud detection frameworks, enhance underwriting accuracy, and reduce unnecessary claim payouts. The dashboard developed here can be further extended with machine learning models, anomaly detection, or automated fraud scoring systems to create an even more powerful fraud-prevention ecosystem.
