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Effect of Privacy-Preserving Data Methods on Actuarial Model Accuracy

Abstract

Privacy-preserving data methods are increasingly important in insurance because actuarial models often require access to sensitive policyholder, claims, health, financial, and demographic information. Techniques such as data anonymisation, pseudonymisation, aggregation, masking, and other privacy-preserving approaches can reduce the exposure of personal information while allowing organisations to retain data for analytical purposes. However, changes made to protect data privacy may also influence the completeness, granularity, and statistical characteristics of information used in actuarial modelling. This study will examine the effect of privacy-preserving data methods on actuarial model accuracy. It will assess how different approaches to protecting sensitive insurance data influence the predictive and estimation accuracy of actuarial models. The study will also examine differences in model results when actuarial models are developed using original datasets and datasets processed through alternative privacy-preserving techniques. The study will focus on privacy-preserving data methods, actuarial model accuracy, data anonymisation, pseudonymisation, data masking, data aggregation, actuarial modelling, insurance data quality, predictive accuracy, and risk estimation. Statistical and actuarial techniques will be used to evaluate how privacy-preserving transformations affect important model inputs, relationships among variables, claim predictions, premium estimates, reserve calculations, and other actuarial outcomes. A quantitative research approach will be adopted for the study. Insurance claims, policy, premium, and demographic datasets will be analysed using data transformation techniques and actuarial or statistical models. Descriptive statistics, model comparison, prediction error measures, correlation analysis, sensitivity analysis, and accuracy assessment will be used to compare models developed from unmodified and privacy-preserved datasets. Alternative privacy-preserving methods will be evaluated to determine their effects on actuarial model performance. The study is expected to reveal that privacy-preserving data methods may have varying effects on actuarial model accuracy. Methods that substantially alter the structure, detail, or distribution of insurance data may reduce model accuracy, while appropriately designed techniques may provide privacy protection with relatively limited effects on analytical performance. The magnitude of the effect may depend on the type of privacy-preserving method, degree of data transformation, dataset characteristics, model complexity, and the level of information retained after processing. The study will be useful to actuaries, insurance companies, data scientists, information security professionals, regulators, financial analysts, and researchers. It may provide useful information for balancing policyholder data protection with the need for reliable actuarial modelling. The findings may also assist insurers in selecting appropriate privacy-preserving methods that protect sensitive information while maintaining sufficient data quality for pricing, reserving, risk assessment, and forecasting activities. The study concludes that privacy-preserving data methods are important considerations in actuarial modelling because protecting sensitive insurance information can influence the quality and analytical usefulness of data used in actuarial models. It is therefore recommended that insurers carefully evaluate the effects of privacy-preserving techniques on model accuracy, adopt suitable data protection methods, and conduct regular model validation to ensure that privacy measures do not unnecessarily reduce the reliability of actuarial estimates.

Keywords: Privacy-preserving data methods, actuarial model accuracy, data anonymisation, pseudonymisation, data masking, data aggregation, actuarial modelling, insurance data, data privacy, predictive accuracy, risk estimation, data quality, actuarial analysis, insurance analytics, model validation.