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Effect of Data Aggregation Levels on Insurance Loss Forecasts

Abstract

Data aggregation levels refer to the degree at which insurance data are grouped for actuarial analysis, such as daily, monthly, quarterly, annual, policy-level, or portfolio-level observations. The level of aggregation can influence the visibility of claim patterns, loss variability, trends, and unusual observations within an insurance dataset. Consequently, the choice of aggregation level may affect the accuracy and reliability of insurance loss forecasts. This study examines the effect of data aggregation levels on insurance loss forecasts. It will assess how different levels of data aggregation influence the estimation and projection of future insurance losses. The study will also examine how aggregation affects the identification of claim frequency patterns, loss severity, seasonal variations, trends, and overall forecast outcomes. The study will focus on actuarial factors including data frequency, claim frequency, claim severity, loss amounts, exposure levels, seasonal patterns, historical claims experience, and forecasting errors. Insurance loss forecasting models will be applied to data organized at different aggregation levels to determine how the structure and frequency of observations influence projected loss values and forecasting performance. A quantitative research approach will be adopted for the study. Historical insurance claims data containing claim dates, claim amounts, exposure information, and relevant policy characteristics will be organized into alternative aggregation levels for analysis. Descriptive statistics, trend analysis, time series techniques, probability models, regression analysis, and forecast error measures will be employed to compare insurance loss forecasts produced from different data aggregation levels. The study is expected to find that differences in data aggregation levels may produce measurable variations in insurance loss forecasts. More detailed data may reveal short-term fluctuations, seasonal patterns, and variations in claim experience that can be less visible in highly aggregated data. Conversely, higher levels of aggregation may reduce short-term volatility and provide a broader view of portfolio-level loss patterns. The forecasting effects may therefore vary according to the nature and stability of the insurance claims data. The findings may be useful to actuaries, insurance companies, claims managers, data analysts, and risk professionals. Understanding the effect of data aggregation can support appropriate data preparation, loss forecasting, reserve estimation, premium assessment, and actuarial modelling. It may also assist insurers in selecting suitable data structures for forecasting models and improving the interpretation of projected insurance losses. The study concludes that data aggregation levels are important considerations in insurance loss forecasting because they can influence the patterns and variability captured by actuarial models. It is therefore recommended that insurers evaluate alternative aggregation levels before developing loss forecasting models and select data structures that adequately represent their claims experience. Regular assessment of forecast accuracy across different aggregation levels may improve the reliability of insurance loss projections.

Keywords: Data aggregation levels, insurance loss forecasts, insurance losses, actuarial forecasting, claims data, claim frequency, claim severity, data aggregation, loss forecasting, insurance exposure, time series analysis, forecast accuracy, actuarial models, claims experience, insurance risk analysis.