  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 paper presents a
groundbreaking 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 various machine learning techniques to quantify the predictive
improvement of the InsurTech-enhanced loss model over that of the insurance
in-house. The quantification process provides a deeper understanding of the
value of the 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.
