Modeling the determinants of Insurtech Adoption: Evidence from the Saudi Insurance Industry

Abstract

This research examines the determinants of InsurTech adoption in the insurance sector in Saudi Arabia and their alignment with the Kingdom's Vision 2030 objective of transforming the economy digitally and diversifying it. Employing a previously unutilized, manually created panel dataset of 23 Saudi insurance companies from 2020 to 2022, the research formulates an InsurTech Adoption Index using annual report keyword frequency. The three models of Multiple Linear Regression (MLR), Generalized Additive Models (GAM), and Random Forest (RF) are used to test the impact of firm age, size, profitability, and capital adequacy on the level of InsurTech uptake. The MLR model considers firm age as a linear predictor of increased InsurTech adoption, as a sign of organizational maturity. The GAM model discovers a nonlinear effect of firm size on digital uptake with declining marginal returns at larger sizes. The RF model shows profitability and capital adequacy as prime predictors and demonstrates how interaction terms imply that digitally inclined firms with robust capital buffers are more active in terms of InsurTech uptake. The research concludes that InsurTech uptake is influenced by subtle but multifaceted interactions between structural and financial firm attributes. The linear models might mask vital dynamics and highlight the importance of elastic and machine learning models to comprehend diffusion in insurance. The insights presented are valuable to insurers and policymakers to formulate targeted digitalization plans. Aligning the firm capabilities with the country's goals of digitalization could better foster InsurTech uptake in the Saudi market and contribute to overall financial sector modernization under Vision 2030.