This paper examines the forecasting methods best suited to insurance company operations, arguing that time series analysis is the most appropriate approach for predicting future claims volume and processing times. The paper discusses how prior claims data can be used to project resource needs, evaluates the strengths and limitations of time series methods, and recommends the adoption of weighted moving averages calibrated against equivalent prior-year periods rather than continuous sequential data points. It concludes by connecting accurate forecasting to long-term facilities and human-resource planning, emphasizing that staffing levels for claims adjusters must be managed through sustained, forward-looking strategies rather than reactive scaling.
The type of forecasting most appropriate for an insurance company is time series analysis, as this approach uses prior demand data to predict future demand (Chase et al., 2005). At the particular insurance company in question, this is precisely the forecasting method in place. The number of claims expected in a given period is based on the number of claims — relative to the number of clients covered by insurance policies — in previous comparable time periods. Perhaps more importantly, the specific time demands of any given claim are predicted based on the average processing times of claims handled by the organization. For firms such as insurance companies, where per-unit "production" times and resource demands vary considerably, time series analysis is an essential forecasting tool for ensuring that adequate human and other resources are available (Armstrong, 2001).
Time series analysis forecasting methods are not always highly successful, as they depend on consistency in the environmental factors influencing demand and affecting supply capabilities — or at least on the ability to accurately predict and account for those factors (Makridakis et al., 2008). For insurance companies, however — and for the particular company in question, at least, as well as presumably for all insurance companies that manage to meet their growth and profitability targets — this method of forecasting proves quite effective. The averages used to generate forecasts are derived from large and ever-growing data populations, which increases their accuracy. The quality and accuracy of underlying data is recognized as one of the key determinants of forecast reliability (Chase et al., 2005; Armstrong, 2001).
"Year-over-year weighting refines claims projections"
"Staffing must be managed through long-term planning"
Always verify citation format against your institution’s current style guide requirements.