Car Insurance Claims

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Car Insurance Claims Analysis Dashboard

Overview

• Overview

• Claimant Demographic Analysis

• Auto Attribute Analysis

• Incident Analysis

• Fraud Analysis

Business Understanding

• Monitor Claim Trends: Track the volume and value of claims over time (e.g., comparing March MTD with full months of January and February) to understand current performance and resource allocation needs.

• Identify High-Risk Segments: Pinpoint demographic groups (age, occupation, education) or vehicle types (make, model, year) that are associated with higher claim frequencies or amounts. This information can inform underwriting rules, premium pricing strategies, and targeted risk mitigation efforts.

• Understand Incident Drivers: Analyze common incident types, severities, collision types, and geographical hotspots. This knowledge can be used for developing safety campaigns, improving claims processing for frequent scenarios, and potentially adjusting regional pricing.

• Enhance Fraud Detection: By examining the characteristics of claims flagged as fraudulent (e.g., claimant demographics, incident details, hobbies), the company can refine its fraud detection models, train investigators more effectively, and reduce losses due to fraudulent activities.

• Optimize Resource Allocation: Insights into claim distribution by city and time of day can help in strategically allocating adjuster resources and emergency response services.

• Improve Customer Profiling: Understanding the demographic makeup of claimants can assist in tailoring products and communication strategies.

Conclusion

• Performance Monitoring: The "Overview" tab shows 1,000 claims totaling approximately $52.76 million, with an average claim amount of $52,762. Notably, 247 claims were flagged as fraudulent, amounting to $14.89 million. The monthly claims data indicates a significantly lower claim count for March, reflecting its MTD status compared to full months of January and February.

• Demographic Impact: The "Claimant Demographic Analysis" reveals that while claim amounts are relatively balanced by gender, certain age groups (e.g., 25-34 and 35-44) and education levels (e.g., High School, JD, Masters, MD) contribute significantly to total claim amounts. Specific occupations also show higher claim totals.

• Vehicle Risk Profiling: The "Auto Attribute Analysis" highlights that vehicles from specific years (e.g., 1995, 1999) and certain makes (e.g., Dodge, Saab, Ford, Subaru) are associated with higher total claim amounts.

• Incident Patterns: "Incident Analysis" shows that Multi-vehicle Collisions and Single Vehicle Collisions represent the bulk of claim amounts. Major Damage, Minor Damage, and Total Loss incidents constitute the majority of claim costs, with Rear Collisions, Side Collisions, and Front Collisions being the most common collision types.

• Fraudulent Claim Characteristics: The "Fraud Analysis" tab indicates that younger age groups (18-24, 25-34) have a higher number of fraudulent claims. Major Damage incidents see the highest number of fraud cases. Occupations like "exec-managerial" and "tech-support" show a higher incidence of fraud, and certain hobbies also correlate with higher fraud counts.