AI-Powered Risk Scoring and Premium Optimization in Auto and Property Insurance
DOI:
https://doi.org/10.5281/zenodo.22111574Keywords:
AI-Driven Risk Stratification, Premium Optimization, Risk-Based Pricing, Machine Learning in Insurance, Claims Frequency–Severity Modeling, Individual Risk Scoring, Closed-Group Risk Reallocation, Dynamic Pricing Strategies, Underwriting Analytics, Customer Lifetime Value, Churn Mitigation, Behavioral Telematics, Spatial Risk Modeling, Data Quality in Insurance Analytics, Algorithmic Pricing Frameworks, Fairness in Automated Pricing, Auto and Property Insurance Analytics, Portfolio Risk Management, Capital Allocation Optimization, Intelligent Underwriting Systems.Abstract
A unified perspective on risk stratification in insurance through the lenses of AI and premiums facilitates optimal risk-based pricing. Risk stratification improves upon traditional segmentation, notably via machine learning, in three areas: identifying and quantifying drivers of claims frequency or severity; reallocating risk among closed groups such that all members share risk characteristics; and estimating risk scores on individual customers based solely on their attribute values. Empirical studies show the added value for stratification of (1) modelling claims as a two-dimensional distribution, (2) incorporating policyholders’ driving behaviour, and (3) considering spatial factors, although data quality may determine the feasibility of particular enhancements. Whereas risk stratification is central to allocating premiums properly, premium optimisation considers the entire pricing and underwriting framework: rate-setting, discount/surcharge application, customer lifetime value, and churn mitigation. Joint consideration also allows dynamic pricing—adjusting premiums in line with changing risk over time—while taking precautions against unfair pricing. All formulation dimensions are amenable to algorithmic solutions. Unifying the two perspectives reveals a clear schematic of one’s research contribution, underlining that empirical applications in auto and property insurance harness AI-driven risk stratification within broader premium-optimisation frameworks. Successful projects yield novel insights and valuable lessons for future efforts.
AI has opened exciting avenues for stratifying risk and optimally calibrating premiums in auto and property insurance. Risk stratification assesses underwriting risk for different groups and the entire portfolio, steering loss reserves and capital allocation. AI enhances traditional segmentation by identifying the main drivers of frequency and severity, reallocating risk among closed groups for equitable sharing of risk characteristics, and estimating risk scores for individual consumers—each with wider applicability. Empirical studies highlight the potential for two-dimensional frequency-severity modelling, incorporation of policyholders’ driving behaviour, and consideration of spatial factors, while underscoring the paramount importance of data quality. Promoting stratification typically directs research attention towards the technology employed, whether neural networks, external data enrichment, or other areas.
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