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Effect of Actuarial Data Granularity on Insurance Risk Estimates

Abstract

Actuarial data granularity refers to the level of detail at which insurance data are collected, classified, and analysed. Insurance datasets may be available at highly detailed levels, such as individual policyholder, claim, vehicle, or transaction records, or at more aggregated levels based on groups, periods, or geographical areas. The level of detail contained in actuarial data can influence the accuracy of risk assessment because different levels of aggregation may reveal or conceal important patterns in insurance experience. This study will examine the effect of actuarial data granularity on insurance risk estimates. It will assess whether the use of data at different levels of detail produces variations in estimated insurance risks. The study will also investigate how granular data can influence the measurement of claim frequency, claim severity, expected losses, and other actuarial risk indicators. The study will focus on different levels of actuarial data aggregation and their effects on insurance risk estimation. Individual policy-level, claim-level, group-level, and aggregated data structures may be considered in the analysis. Actuarial and statistical techniques will be used to compare risk estimates generated from different levels of data granularity and determine whether more detailed information provides different estimates of underlying insurance risk. A quantitative research approach will be adopted for the study. Relevant insurance policy and claims data will be organised at different levels of granularity and analysed using descriptive statistics, regression analysis, probability distributions, and appropriate actuarial modelling techniques. Estimated claim frequencies, claim severities, expected losses, and risk measures will be compared across the different data structures. The analysis will provide empirical evidence on the relationship between data granularity and insurance risk estimates. The study is expected to reveal that differences in actuarial data granularity may produce variations in insurance risk estimates. More detailed data may capture differences in policyholder characteristics, claim patterns, and exposure levels that can be less visible in aggregated datasets. However, the usefulness of increased granularity may depend on data quality, completeness, sample size, and the type of insurance risk being assessed. The study will be useful to actuaries, insurance companies, data analysts, underwriters, regulators, and researchers. It may assist insurance organisations in determining appropriate levels of data detail for actuarial modelling and risk estimation. The findings may also support improved data management practices, more precise risk assessment, and more reliable actuarial decision-making. The study concludes that actuarial data granularity can influence the estimation of insurance risks by affecting the level of detail available for identifying and modelling risk patterns. It is therefore recommended that insurers adopt appropriate levels of data granularity that provide sufficient detail for reliable actuarial analysis while maintaining data quality, consistency, and practical usability.

Keywords: Actuarial data granularity, insurance risk estimates, actuarial data, insurance risk assessment, data aggregation, policy-level data, claims data, risk modelling, claim frequency, claim severity, expected losses, actuarial analysis, insurance data quality, statistical modelling, insurance risk estimation.