AI-Based Risk Scoring Models Using Large Language Models (LLMs) in Auto Insurance

Introduction

Auto insurance risk scoring is a critical component in underwriting, pricing, and claims management. Traditional actuarial models rely on structured historical data, but recent advancements in AI, particularly Large Language Models (LLMs), enable more dynamic and accurate risk assessments. LLMs enhance predictive modeling by integrating structured and unstructured data sources, such as telematics, customer interactions, and social determinants. This article explores AI-based risk scoring models, categorizing the top key base influential variables, defining derived variables, and identifying a target variable with a calculation approach and formulas for robust risk prediction.

Objectives of the 'AI-Based Risk Scoring Models'

?? Enhanced Risk Segmentation: Improve customer risk profiling by analyzing historical claims, driving behavior, and external factors.

?? Automated Underwriting & Pricing: Enable real-time policy pricing adjustments based on AI-driven risk assessments.

?? Fraud Detection & Prevention: Detect fraudulent claims by analyzing linguistic patterns in claims narratives and past claim behaviors.

?? Claims Processing Optimization: Reduce processing time by identifying risk-associated claims through AI-generated risk scores.

?? Regulatory Compliance & Reporting: Ensure adherence to legal frameworks through AI-driven audit and compliance checks.

Benefits of the 'AI-Based Risk Scoring Models'

?? Increased Pricing Accuracy: AI-driven insights allow for granular premium adjustments based on individual risk factors.

?? Improved Claims Efficiency: Faster claims validation and settlement with AI-assisted decision-making.

?? Reduced Fraud Losses: Identifies anomalies in claim descriptions and behaviors to detect fraudulent activities.

?? Enhanced Customer Experience: Personalized policies and faster resolutions improve customer satisfaction.

?? Regulatory & Risk Compliance: Ensures compliance with industry regulations using AI-driven documentation analysis.

Base Influential Variables by Category-Wise

We systematically categorized key base variables and aligned them with AI-powered Large Language Models (LLMs) for "AI-Based Risk Scoring Models," ensuring seamless associations for efficient analysis and implementation.

?? Policyholder Information

?? Age – Younger drivers pose higher risks.

?? Gender – Historical claim data varies by gender.

?? Marital Status – Married individuals have lower accident risks.

?? Credit Score – Lower scores indicate higher risk-taking behavior.

?? Occupation – Certain jobs are linked to different risk levels.

?? Annual Income – Higher incomes suggest potential for safer vehicles.

?? Driving Experience – More years of driving reduce accident likelihood.

?? Policy Tenure – Long-term policyholders are generally lower risk.

?? Claims History – Frequent past claims indicate a higher probability of future claims.

?? Multi-Vehicle Policy – Multi-policyholders exhibit diverse risk profiles.

?? Loyalty to Insurer – Longer loyalty may indicate lower risk.

?? Previous Insurer Details – Helps assess past underwriting risks.

?? Vehicle Information

?? Vehicle Age – Older cars may have higher repair costs.

?? Vehicle Make & Model – High-performance models tend to have higher risks.

?? Vehicle Safety Ratings – Higher ratings indicate safer vehicles.

?? Annual Mileage – More miles driven increase accident risk.

?? Usage Type (Personal/Commercial) – Commercial use often leads to more claims.

?? Anti-Theft Features – Reduces probability of theft-related claims.

?? Fuel Type (Gasoline/Diesel/Electric) – Certain fuel types correlate with accident rates.

?? Previous Owners – More owners can indicate higher wear and tear.

?? Vehicle Parking Location – Parking in secured areas reduces risk.

??Modification History – Aftermarket modifications impact vehicle safety.

?? Driving Behavior

?? Speeding Violations – More violations suggest reckless driving.

?? Traffic Violations – Increased violations lead to higher claims.

?? Accident History – Past accidents predict future risk.

?? Daily Distance Driven – Long distances increase exposure.

?? Braking Patterns – Hard braking correlates with aggressive driving.

?? Lane Change Frequency – Higher frequency implies risky behavior.

?? Nighttime Driving – Increased accident probability at night.

?? Highway vs. City Driving – Different risks based on road type.

?? Fatigue Indicators – AI detects drowsy driving risk.

?? Distraction Indicators – Mobile usage while driving increases accident likelihood.

?? Claims Data

?? Past Claim Severity – Higher severity predicts future claims.

?? Number of Claims in 3 Years – Frequent claims suggest higher risk.

?? Claim Amount vs. Premium Ratio – High ratios indicate poor risk quality.

?? Nature of Past Claims – Helps identify fraudulent behavior.

?? Claim Filing Frequency – Frequent filings increase risk scores.

?? At-Fault Claims – Past fault claims predict future behavior.

?? Litigation Involvement – Legal claims raise insurer costs.

?? Claim Settlement Time – Delays may indicate fraudulent behavior.

?? Third-Party Involvement – More third-party claims increase liability.

?? Claim Denial History – Past denials suggest risk-prone individuals.

?? External & Environmental Data

?? Weather Conditions – Adverse weather increases accident risks.

?? Road Quality – Poor infrastructure raises claim likelihood.

?? Crime Rate in Area – High crime areas correlate with theft claims.

?? Traffic Density – Congested areas see more accidents.

?? Fuel Prices – High prices impact driving patterns.

?? Insurance Regulation Changes – Policy shifts affect claims behavior.

?? Economic Indicators – Recessions increase fraudulent claims.

?? Technology Adoption – AI-assisted driving reduces accident rates.

?? Medical Cost Index – Impacts bodily injury claim amounts.

?? Auto Repair Costs in Area – High repair costs increase claim payouts.

?? Industry Claim Trends – Macro patterns influence risk assessment.

Derived (Feature Engineering) Variables by Category-Wise

We systematically derived variables through feature engineering and aligned them with AI-powered Large Language Models (LLMs) for "AI-Based Risk Scoring Models," ensuring streamlined associations for efficient analysis and seamless implementation.

?? Risk Behavior Index

?? Aggregated Violation Score = Weighted sum of all traffic violations.

?? Aggressive Driving Index = (Braking Intensity + Speeding Score) / Total Trips.

?? Driver Stability Score = 1 - (At-Fault Claims / Total Claims).

?? Fraud Likelihood Score = AI-derived anomaly detection.

?? Policyholder Reliability Index = (Loyalty Years / Claim Frequency).

?? Vehicle Safety Index

?? Accident Severity Probability = Historical vehicle damage patterns.

?? Vehicle Wear Index = (Vehicle Age + Mileage) / Safety Rating.

?? Modification Risk Score = AI-detected risk on modifications.

?? Repair Cost Prediction = Region-based auto repair cost estimation.

?? Theft Susceptibility Score = (Crime Rate + Parking Location Factor).

??Claim Processing & Financial Impact

?? Claim Settlement Efficiency = 1 - (Settlement Time / Industry Avg.).

?? Litigation Probability Score = Legal history & fraud likelihood.

?? Premium-to-Claim Ratio = Premium Paid / Historical Claims.

?? Underwriting Consistency Index = Changes in insurer history.

?? Claim Payment Anomaly Score = AI-based fraud detection.

?? Environmental Influence

?? Road Safety Score = (Weather + Traffic Density + Road Quality).

?? Economic Hardship Impact = (Fuel Prices + Unemployment Index).

?? Regulatory Risk Score = AI-based legal compliance rating.

?? Geographical Accident Frequency = Region-based claims data.

?? Technological Adaptation Factor = AI-adoption levels in vehicles.

Target Variable Definition & Calculation Approach

The primary target variable for AI-based risk scoring is "Policyholder Risk Score" (PRS), which combines base and derived variables using:

?? Policyholder Risk Score (PRS) = A x (Risk Behaviour Index) + B x (Vehicle Safety Index) + C x (Claim Impact) + D x (Environmental Risks)

where A, B, C, D are optimized weights derived from AI-based training models.

Industry Data Sources for AI Risk Scoring

Data serves as the foundation, making it crucial to collect key influential base variables from various data sources.

?? National Highway Traffic Safety Administration (NHTSA) Reports

?? Insurance Information Institute (III) Data

?? Telematics & IoT Data from Connected Vehicles

?? Historical Claims Data from Insurance Companies

?? Government Accident & Fatality Reports

Model Development and Monitoring in Production

Our team evaluated over 35 statistical techniques and algorithms, including hybrid approaches, to develop optimal solutions for our clients. While we have not detailed every key variable used in "Auto Insurance: AI-Based Risk Scoring Models Using Large Language Models (LLMs)," this article provides a concise, high-level overview of the problem and essential data requirements.

We continuously monitor model performance in production to identify any degradation, which may result from shifts in customer behavior or evolving market conditions. If the predicted outcomes deviate from the client’s SLA by more than ±2.5% (model drift), we conduct a comprehensive model review. Additionally, we regularly update and retrain the model with fresh data, incorporating user feedback to improve accuracy and effectiveness.

Conclusion

AI-powered risk scoring using Large Language Models (LLMs) is transforming the auto insurance landscape by enabling deeper insights from structured and unstructured data sources. By leveraging key base and derived influential variables, insurers can optimize policy pricing, enhance claims automation, and detect fraud with greater accuracy. LLMs significantly improve underwriting precision, reducing operational inefficiencies while ensuring compliance with regulatory standards. The integration of diverse industry data sources strengthens predictive capabilities, leading to more precise risk segmentation and better customer engagement. As AI continues to evolve, insurers must adopt sophisticated risk-scoring methodologies to maintain competitive advantage. This article highlights the transformative role of LLMs in auto insurance, paving the way for a more intelligent, data-driven approach to risk assessment.

Important Note

This newsletter article is intended to educate a wide audience, including professionals considering a career shift, faculty members, and students from both engineering and non-engineering fields, regardless of their computer proficiency level.