Paper: arXiv 2401.16723

Authors: Zhiyu Quan, Changyue Hu, Panyi Dong, Emiliano A. Valdez

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 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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on the empirical application of machine learning techniques to real-world proprietary claims and InsurTech data rather than heavy mathematical derivations. While the findings offer novel insights for actuarial loss modeling and industry practice, reproducibility is limited due to the reliance on private data sources.

Research Flowchart

  flowchart TD
    A[Research Goal: Enhance Insurance Loss Models via InsurTech] --> B[Data Integration]
    
    B --> C[Proprietary Claims Data]
    B --> D[Emerging InsurTech Data Sources]
    
    C & D --> E[Machine Learning Modeling & Predictive Analytics]
    
    E --> F[Performance Quantification & Risk Factor Analysis]
    
    F --> G[Outcomes: Improved Risk Classification, Claims Management & Academic-Industry Blueprint]

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0 of 51 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.