Abstract
Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and computational capabilities, InsurTech is able to integrate a multitude of emerging data sources, shedding light on opportunities to enhance risk classification and claims management. This article presents a collaborative effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies’ risk management. Our study further utilizes a tree-based model and a conventional linear model to quantify the predictive improvement of the InsurTech-enhanced loss model over that of the insurance in-house model. The quantification process provides a deeper understanding of the value of InsurTech innovation and advocates potential risk factors that are unexplored in traditional insurance loss modeling. This study represents a successful undertaking of an academic–industry collaboration, suggesting an inspiring path for future partnerships between industry and academic institutions.
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