Enhancing Actuarial Ratemaking with Synthetic Data for Privacy Preservation

# MotivationThe insurance industry must balance data-driven decision-making with stringent privacy regulations. Actuarial ratemaking depends on comprehensive datasets, but traditional anonymization techniques often degrade data utility, compromising predictive accuracy. This study explores the use of synthetic data to maintain privacy while preserving analytical integrity.# MethodWe employ a synthetic data generation approach based on kernel density estimation. This method ensures that the statistical properties of the original dataset are retained while mitigating privacy risks. The approach is evaluated on an actuarial dataset, comparing univariate, bivariate, and multivariate relationships between synthetic and real data.# ResultsOur results demonstrate high fidelity in preserving statistical relationships while enhancing privacy protection. The synthetic data maintains the original dataset's structure, supporting its use in actuarial modeling. Moreover, privacy assessments confirm that no direct re-identification risk exists, and synthetic records are sufficiently distinct from original data points.# ConclusionsSynthetic data offers a viable solution to balancing privacy and utility in actuarial ratemaking. By preserving statistical integrity while complying with privacy standards, it enables secure data sharing and model development without exposing sensitive information. This study lays the groundwork for future actuarial applications of synthetic data and the establishment of industry standards for privacy-preserving analytics.