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Effect of Synthetic Claims Data on Actuarial Model Validation

Abstract

Synthetic claims data are artificially generated datasets designed to reproduce selected characteristics and statistical patterns of real insurance claims while reducing reliance on sensitive or restricted information. Such data can provide actuaries with additional observations for testing, developing, and validating actuarial models. However, differences between synthetic and real claims data may influence the reliability of model validation results and the conclusions drawn about actuarial model performance. This study will examine the effect of synthetic claims data on actuarial model validation. It will assess how the use of synthetic claims datasets influences the evaluation of actuarial models and determine whether models validated with synthetic data produce results comparable to those obtained using real claims data. The study will also examine the effect of different levels of similarity between synthetic and real claims data on model validation outcomes. The study will focus on synthetic claims data, actuarial model validation, insurance claims, data simulation, model accuracy, predictive performance, claims distributions, actuarial modelling, validation techniques, and insurance risk estimation. Statistical and actuarial methods will be applied to generate synthetic claims data and evaluate how well the simulated datasets preserve important characteristics of actual claims, including frequency, severity, variability, and distributional patterns. A quantitative research approach will be adopted for the study. Real or appropriately sourced insurance claims data will be used to develop synthetic datasets based on relevant statistical characteristics. Actuarial models will then be validated using both synthetic and real claims data. Descriptive statistics, distributional comparison, prediction error measures, goodness-of-fit tests, sensitivity analysis, and comparative model validation techniques will be applied to determine differences in validation results. The study is expected to reveal that synthetic claims data may have a significant effect on actuarial model validation outcomes, depending on how accurately the synthetic data reproduce the characteristics of real claims. High-quality synthetic datasets may provide useful alternatives for model testing and validation, while poorly generated datasets may produce misleading validation results. The magnitude of the effect may depend on the data generation method, claims distribution, sample size, model structure, and similarity between synthetic and real claims experience. The study will be useful to actuaries, insurance companies, data scientists, statistical analysts, regulators, and researchers. It may provide useful information on the application of synthetic data in actuarial model development and validation while reducing dependence on sensitive insurance records. The findings may also assist insurers in assessing whether synthetic claims datasets are suitable for testing pricing, reserving, claims prediction, and risk assessment models. The study concludes that synthetic claims data can influence actuarial model validation because the quality and statistical representativeness of generated claims data determine how reliably models can be evaluated. It is therefore recommended that insurers carefully assess the statistical similarity between synthetic and real claims data, apply appropriate validation procedures, and conduct comparative testing before relying on synthetic datasets for actuarial model validation.

Keywords: Synthetic claims data, actuarial model validation, insurance claims, synthetic data generation, actuarial modelling, model accuracy, predictive performance, claims frequency, claims severity, data simulation, model testing, statistical validation, insurance risk estimation, actuarial analysis, model performance.