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Effect of Data Quality on Actuarial Risk Modelling

Abstract

The study examines the effect of data quality on actuarial risk modelling, focusing on how the accuracy, completeness, consistency, and reliability of data influence the development and performance of actuarial models. Actuarial risk modelling relies heavily on historical and current data to estimate insurance risks, expected losses, premiums, reserves, and other financial outcomes. Poor-quality data can therefore introduce errors into actuarial calculations and reduce the reliability of risk assessments and financial decisions. The study will investigate the effect of data quality on actuarial risk modelling by examining how different dimensions of data quality influence the accuracy and reliability of actuarial models. Particular attention will be given to data accuracy, completeness, consistency, timeliness, and relevance. The study will assess how these characteristics affect the estimation of claims frequency, claims severity, loss probabilities, expected claims costs, and other important actuarial risk measures. The study will further examine the implications of inaccurate, incomplete, or inconsistent data for actuarial modelling outcomes. It will consider how data deficiencies may influence model assumptions, parameter estimation, prediction accuracy, and the interpretation of risk patterns. Understanding these effects is important because unreliable data may lead to inappropriate pricing decisions, inadequate reserves, inaccurate forecasts, and ineffective insurance risk management. A quantitative research approach will be adopted for the study. Relevant actuarial and insurance datasets containing information on claims, policies, premiums, exposure, and other risk characteristics will be collected and analyzed. Descriptive statistics, correlation analysis, regression techniques, and model performance measures may be employed to examine the relationship between data quality and actuarial risk modelling. Comparative analysis may also be conducted using datasets with varying levels of completeness, accuracy, and consistency. The study is expected to reveal that data quality has a significant effect on actuarial risk modelling. Higher-quality data are expected to produce more reliable parameter estimates, improved model predictions, and more accurate assessments of insurance risks. Conversely, poor-quality data may increase modelling errors, distort estimated risk levels, and reduce the reliability of actuarial forecasts and decisions. The study is further expected to establish that effective data quality management can strengthen actuarial modelling and improve insurance decision-making. The findings may assist insurers and actuaries in developing stronger data collection and validation procedures, improving data storage and management systems, and conducting regular data-quality assessments before applying actuarial models. Improved data quality may also support more accurate pricing, reserving, forecasting, and risk management. The study concludes that data quality is a fundamental determinant of the reliability and effectiveness of actuarial risk modelling. It is therefore recommended that insurance companies establish comprehensive data governance procedures, regularly validate actuarial datasets, correct data inconsistencies, and ensure that modelling activities are based on accurate and sufficiently complete information. These measures will help improve actuarial model performance, strengthen risk assessment, and support sound and sustainable insurance decisions.

Keywords: Data Quality, Actuarial Risk Modelling, Actuarial Models, Insurance Data, Risk Assessment, Data Accuracy, Data Completeness, Data Consistency, Data Reliability, Claims Data, Risk Modelling, Actuarial Analysis, Insurance Pricing, Risk Management, Model Accuracy.