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Effect of Actuarial Data Quality on Insurance Valuation Accuracy

Abstract

Actuarial data provides the foundation for estimating insurance premiums, liabilities, reserves, claims, and other financial obligations. The quality of data used in actuarial calculations can directly influence the reliability of valuation results. Inaccurate, incomplete, inconsistent, or outdated data may lead to errors in assumptions and estimates, making actuarial data quality an important consideration in achieving accurate insurance valuation. This study examines the effect of actuarial data quality on insurance valuation accuracy. The study will assess how the completeness, accuracy, consistency, relevance, and timeliness of actuarial data influence the reliability of insurance valuation results. It will focus on how variations in data quality affect estimates of insurance liabilities and other actuarial values. The study will consider factors such as data completeness, data accuracy, data consistency, data timeliness, missing observations, duplicate records, claims information, policyholder information, exposure data, and historical loss experience. These factors will be examined to determine how deficiencies or improvements in actuarial data quality influence valuation estimates and the level of variation between projected and observed insurance outcomes. A quantitative research approach will be adopted for the study. Relevant actuarial, claims, policy, and exposure data will be assessed using descriptive statistics, data quality measures, actuarial valuation techniques, error analysis, and sensitivity analysis. Where appropriate, regression analysis will be applied to examine the relationship between data quality indicators and insurance valuation accuracy. Alternative data quality scenarios will also be considered to determine their effects on valuation results. The study is expected to reveal that higher-quality actuarial data may improve insurance valuation accuracy and reduce estimation errors. Complete, accurate, consistent, relevant, and timely data are expected to produce more reliable actuarial assumptions and valuation outcomes, while missing, inaccurate, or inconsistent data may result in greater uncertainty and deviations in estimated liabilities and other insurance values. The findings may also show that the effect of poor data quality varies according to the type and complexity of insurance valuation being performed. The study will be useful to actuaries, insurance companies, data analysts, financial managers, auditors, regulators, and other stakeholders involved in insurance valuation. It may provide useful information for strengthening actuarial data management, improving data validation procedures, enhancing the reliability of valuation models, and supporting more accurate financial reporting and risk management decisions. The study concludes that actuarial data quality is an important determinant of insurance valuation accuracy because the reliability of actuarial estimates depends substantially on the quality of the underlying data. It is therefore recommended that insurers establish effective data quality controls, regularly validate and reconcile actuarial datasets, address missing and inconsistent information, maintain accurate historical records, and ensure that data used for valuation is relevant and up to date.

Keywords: Actuarial data quality, insurance valuation accuracy, actuarial valuation, insurance data, data completeness, data accuracy, data consistency, data timeliness, claims data, policyholder data, exposure data, actuarial assumptions, valuation errors, insurance liabilities, data management.