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Effect of Actuarial Model Complexity on Insurance Forecast Accuracy

Abstract

Actuarial model complexity refers to the level of sophistication, number of variables, assumptions, and mathematical structures incorporated into a model used for insurance forecasting. Complex actuarial models can capture multiple risk characteristics and relationships within insurance data, while simpler models may provide more straightforward forecasting structures. The level of model complexity may therefore influence the accuracy and reliability of insurance forecasts. This study will examine the effect of actuarial model complexity on insurance forecast accuracy. It will assess how different levels of model complexity influence the accuracy of predicted insurance outcomes and determine whether increasing model sophistication produces meaningful improvements in forecasting performance. The study will also compare the forecasting results of actuarial models with different levels of complexity using similar insurance data. The study will focus on actuarial model complexity, insurance forecast accuracy, predictive modelling, claims experience, premium forecasting, risk factors, model performance, forecasting errors, actuarial assumptions, and insurance analytics. Alternative actuarial models with varying numbers of predictors, assumptions, and structural components will be examined. Forecast accuracy will be assessed using appropriate statistical measures to identify differences in prediction errors and forecasting reliability across model specifications. A quantitative research approach will be adopted for the study. Historical insurance policy, premium, claims, and risk-factor data will be analysed using descriptive statistics, actuarial forecasting models, comparative analysis, prediction error analysis, model performance evaluation, and sensitivity analysis. Models of different complexity levels will be developed and tested using the same insurance dataset, and their forecast results will be compared to determine the relationship between model complexity and prediction accuracy. The study is expected to reveal that actuarial model complexity may have a significant effect on insurance forecast accuracy. More complex models may capture additional relationships within insurance data and improve forecasting performance in some circumstances, while excessive complexity may introduce estimation uncertainty or reduce predictive stability. The magnitude of the effect may depend on data quality, sample size, number of risk factors, model assumptions, portfolio characteristics, and the forecasting period. The study will be useful to actuaries, insurance companies, underwriters, pricing analysts, data scientists, risk managers, regulators, and researchers. It may provide useful information for selecting appropriate levels of model complexity, improving insurance forecasting practices, evaluating predictive performance, and reducing unnecessary modelling errors. The findings may also assist insurers in balancing model sophistication with reliability when developing actuarial forecasting systems. The study concludes that actuarial model complexity is an important consideration in insurance forecasting because differences in model structure and sophistication can influence prediction accuracy. It is therefore recommended that insurers and actuarial analysts compare models of different complexity levels, evaluate their forecasting performance using appropriate statistical measures, and select model structures that provide reliable predictions without unnecessary complexity.

Keywords: Actuarial model complexity, insurance forecast accuracy, predictive modelling, actuarial forecasting, insurance forecasting, claims prediction, premium forecasting, model performance, forecasting errors, actuarial assumptions, risk factors, model selection, insurance analytics, predictive accuracy, actuarial models.