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Effect of Claims Data Granularity on Insurance Loss Modelling

Abstract

Claims data granularity refers to the level of detail at which insurance claims information is recorded, organised, and analysed for actuarial purposes. Claims data may be available at highly detailed individual claim levels or in aggregated forms based on time periods, policy classes, claim types, or other characteristics. The level of detail contained in claims data may influence the ability of actuaries to identify loss patterns and develop reliable insurance loss models. This study will examine the effect of claims data granularity on insurance loss modelling. It will assess how different levels of detail in claims data influence the estimation and modelling of insurance losses. The study will also compare loss modelling outcomes obtained from detailed and aggregated claims data to determine how data granularity affects the reliability, consistency, and predictive performance of actuarial loss models. The study will focus on claims data granularity, insurance loss modelling, individual claims, aggregated claims data, claim frequency, claim severity, loss distributions, expected losses, actuarial models, data aggregation, model accuracy, and insurance risk estimation. Different levels of claims data detail will be examined to identify their effects on the representation of loss experience. Statistical and actuarial techniques will be applied to evaluate variations in model estimates and prediction results arising from different data structures. A quantitative research approach will be adopted for the study. Historical insurance claims data will be organised at different levels of granularity and analysed using descriptive statistics, claim frequency and severity analysis, loss distribution modelling, regression analysis, actuarial loss estimation, model validation, and sensitivity analysis. Results generated from detailed and aggregated claims datasets will be compared using appropriate model performance and prediction accuracy measures. The study is expected to reveal that claims data granularity may have a significant effect on insurance loss modelling. More detailed claims data may provide greater information about individual loss characteristics and improve the identification of variations in claims experience, while highly aggregated data may simplify analysis but conceal important loss patterns. The magnitude of the effect may depend on claims variability, data quality, portfolio characteristics, aggregation level, and the modelling technique applied. The study will be useful to actuaries, insurance companies, claims analysts, pricing analysts, underwriters, risk managers, and insurance researchers. It may provide useful information for determining appropriate levels of claims data detail, improving insurance loss models, strengthening loss estimation, and supporting pricing and reserving decisions. The findings may also assist insurers in understanding the trade-off between detailed claims information and aggregated data when developing actuarial models. The study concludes that claims data granularity is an important consideration in insurance loss modelling because the level of detail available in claims information can influence the representation and estimation of insurance losses. It is therefore recommended that insurers carefully assess the appropriate level of claims data granularity, preserve relevant individual claim information where necessary, and evaluate alternative aggregation levels through model validation and sensitivity analysis.

Keywords: Claims data granularity, insurance loss modelling, claims data, individual claims, aggregated claims, claim frequency, claim severity, loss distributions, expected losses, actuarial models, data aggregation, model accuracy, insurance risk, loss estimation, sensitivity analysis.