Part 2 - Fraud Detection in Auto Insurance Claims: Strategic Business Rules and Data-Driven Insights

Introduction

Auto insurance fraud continues to be a major concern, causing substantial financial losses for insurers. Detecting fraudulent activities effectively depends on strong business rules and thorough analysis of historical data. This article outlines fraud detection business rules and techniques for extracting fraud data from existing historical datasets.

This article explores a range of fraud triggers and business rules, drawing on insights from historical data. Key focus areas include 'Unusual Claimant Behavior,' 'High-Risk Locations,' 'Inconsistent Vehicle Damage,' 'Excessive Medical Claims for Minor Accidents,' and 'Claims Involving Newly Added Drivers.'

Fraud Detection: Strategic Business Rules and In-sights from Historical Data

💥 Unusual Claimant Behavior

Changes in claimant behavior or inconsistencies in their story may suggest fraud.

⏺ Industry Example: A policyholder changes their injury report significantly.

📊 Sample Data: The Initial Injury Report shows 'minor bruising', while the Final Injury Report indicates a 'severe neck injury' under Claim ID # CLS 9876543.

📈 Data Derivation: Track changes in claim details and communication records.

💥 High-Risk Locations

Claims from areas with high fraud rates should be scrutinized.

⏺ Industry Example: A surge in claims from an area known for staged accidents.

📊 Sample Data: Identify the risk locations (Urban Area X) and classify the corresponding fraud risk score 'high' under the Claim ID # CLS9876543.

📈 Data Derivation: Use geospatial analysis to identify and monitor high-risk areas.

💥 Inconsistent Vehicle Damage

Damage reported may not match the accident description.

⏺ Industry Example: Minor rear-end collision with severe front-end damage reported.

📊 Sample Data: The Accident Type reported as 'Rear-End Collision,' but the actual damage is to the 'Front-End' of the vehicle under Claim ID # CLS9876543.

📈 Data Derivation: Verify damage reports using image recognition and accident reconstruction tools.

💥 Excessive Medical Claims for Minor Accidents

Inflated medical claims following minor accidents are often fraudulent.

⏺ Industry Example: Medical claims of $50,000 for a minor fender bender.

📊 Sample Data: The accident severity is 'Minor,' yet the claim amount is $50,000 under Claim ID # CLS9876543.

📈 Data Derivation: Analyze medical claim amounts relative to accident severity.

💥 Claims with Newly Added Drivers

Adding drivers to a policy just before a claim can be a red flag.

⏺ Industry Example: A new driver is added right before a major accident claim.

📊 Sample Data: The new driver was added to policy # PLY123456789 on 01-Jun-2024, just two days before a major accident on 03-Jun-2024, with a claim amount of $15,525.85.

📈 Data Derivation: Monitor new driver additions and their timing relative to claims.

Conclusion

To implement fraud detection effectively, a robust monitoring system must be developed using the outlined rules. Each rule should be customized to the insurance company's specific needs and risk factors. Regular updates and validations of the fraud detection system are crucial to maintaining its effectiveness.

Important Note

This newsletter article is designed to educate a broad audience, including working professionals, faculty members, and students from both engineering and non-engineering disciplines, regardless of their level of computer proficiency.