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Effect of Data Observation Intervals on Insurance Claim Forecasts

Abstract

Data observation intervals refer to the time intervals at which insurance claim information is collected, recorded, and analysed for forecasting purposes. The choice of observation interval, such as monthly, quarterly, or annual periods, influences the level of detail and variation captured in insurance claims data. Appropriate observation intervals are therefore important for producing reliable forecasts of future claim experience. This study will examine the effect of data observation intervals on insurance claim forecasts. It will assess how different observation intervals influence the estimation and prediction of insurance claim frequency, severity, and aggregate claim amounts. The study will also compare forecast results obtained from different data intervals to determine how the choice of observation period affects the stability and accuracy of insurance claim forecasts. The study will focus on data observation intervals, insurance claim forecasts, claim frequency, claim severity, aggregate claims, historical claims data, time series patterns, claim trends, forecasting accuracy, and actuarial forecasting. Monthly, quarterly, and annual observation intervals may be considered to identify differences in claims patterns and forecast outcomes. Statistical and actuarial forecasting techniques will be applied to assess the effect of different observation intervals on predicted insurance claim experience. A quantitative research approach will be adopted for the study. Historical insurance claims data will be organised according to different observation intervals and analysed using descriptive statistics, trend analysis, claim frequency and severity analysis, time series forecasting, forecast error analysis, and sensitivity analysis. Forecast results generated from alternative observation intervals will be compared to evaluate differences in predicted claim amounts, frequency, severity, and overall forecasting performance. The study is expected to reveal that the selection of data observation intervals may have a significant effect on insurance claim forecasts. Shorter intervals may capture more detailed changes and seasonal patterns in claims experience, while longer intervals may provide smoother observations but potentially conceal short-term variations. The magnitude of the effect may depend on claim volatility, seasonal patterns, portfolio characteristics, data volume, and the forecasting 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 selecting appropriate observation intervals for claims forecasting, improving forecast reliability, identifying changes in claims experience, and supporting actuarial pricing and reserving decisions. The findings may also assist insurers in organising historical claims data for effective forecasting applications. The study concludes that data observation intervals are important considerations in insurance claim forecasting because the frequency at which claims data are observed can influence the patterns identified and the resulting forecasts. It is therefore recommended that insurers carefully select observation intervals based on the characteristics of their claims data, evaluate seasonal and short-term variations, and compare alternative intervals through forecast accuracy and sensitivity analysis.

Keywords: Data observation intervals, insurance claim forecasts, claims forecasting, claim frequency, claim severity, aggregate claims, historical claims data, time series analysis, forecasting accuracy, actuarial forecasting, claim trends, seasonal patterns, insurance data, forecast errors, sensitivity analysis.