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Effect of Algorithmic Risk Scores on Insurance Premium Differentiation

Abstract

Algorithmic risk scores are increasingly used in insurance to assess the level of risk associated with individual policyholders and insurance contracts. These scores are generated from policy, claims, demographic, behavioural, and exposure information and can be used to support automated risk classification and premium determination. The use of algorithmic risk scores may therefore influence the extent to which insurance premiums differ across policyholders with varying risk characteristics. This study will examine the effect of algorithmic risk scores on insurance premium differentiation. It will assess how changes in risk scores influence the differences in premiums assigned to policyholders and determine the relationship between algorithmically estimated risk levels and premium values. The study will also examine how different risk score ranges correspond to variations in insurance premiums across selected risk categories. The study will focus on algorithmic risk scores, insurance premium differentiation, risk classification, insurance pricing, actuarial modelling, predictive variables, premium rating, claims experience, risk assessment, and underwriting. Statistical and actuarial techniques will be used to evaluate the relationship between algorithmic risk scores and premium levels. The study will consider how risk score thresholds, score distributions, and rating structures influence the differentiation of premiums among policyholders. A quantitative research approach will be adopted for the study. Historical insurance policy, claims, premium, and risk-factor data will be analysed using descriptive statistics, correlation analysis, regression analysis, comparative analysis, risk score modelling, and sensitivity analysis. Policyholders will be classified according to relevant algorithmic risk scores, and the resulting premium estimates will be compared across different risk categories to determine the effect of risk scores on premium differentiation. The study is expected to reveal that algorithmic risk scores may have a significant effect on insurance premium differentiation. Higher or lower risk scores may correspond with differences in estimated premiums as insurers adjust prices according to predicted levels of risk. The magnitude of the effect may depend on the predictive variables included in the scoring model, the distribution of risk scores, claims experience, rating structures, and the pricing methodology applied. The study will be useful to actuaries, insurance companies, underwriters, pricing analysts, data scientists, regulators, financial analysts, and researchers. It may provide useful information for evaluating algorithmic approaches to insurance pricing, improving risk classification, strengthening premium rating practices, and understanding the financial effects of automated risk assessment. The findings may also assist insurers in assessing the consistency of premium differences generated through algorithmic risk scoring. The study concludes that algorithmic risk scores are important considerations in insurance premium differentiation because differences in assessed risk levels can influence the premiums assigned to policyholders. It is therefore recommended that insurers regularly validate algorithmic risk scoring models, evaluate the relationship between risk scores and observed claims experience, and review premium differentials to support accurate and consistent actuarial pricing.

Keywords: Algorithmic risk scores, insurance premium differentiation, risk classification, insurance pricing, actuarial modelling, predictive variables, premium rating, claims experience, risk assessment, underwriting, automated risk scoring, premium estimation, insurance analytics, actuarial pricing, risk-based premiums.