publications

2024

1. 

   Improving Business Insurance Loss Models by Leveraging InsurTech Innovation

   Zhiyu Quan, Changyue Hu, Panyi Dong, and 1 more author

   North American Actuarial Journal 2024

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   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.

   @article{quan2024improving,
     title = {Improving Business Insurance Loss Models by Leveraging InsurTech Innovation},
     year = {2024},
     author = {Quan, Zhiyu and Hu, Changyue and Dong, Panyi and Valdez, Emiliano A.},
     journal = {North American Actuarial Journal},
     volume = {0},
     pages = {1--28},
     publisher = {Routledge},
     doi = {10.48550/arXiv.2401.16723},
     url = {https://doi.org/10.1080/10920277.2024.2400648},
   }

2. 

   NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit

   Linfeng Zhang, Changyue Hu, and Zhiyu Quan

   2024

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   As the body of academic literature continues to grow, researchers face increasing difficulties in effectively searching for relevant resources. Existing databases and search engines often fall short of providing a comprehensive and contextually relevant collection of academic literature. To address this issue, we propose a novel framework that leverages Natural Language Processing (NLP) techniques. This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain. To demonstrate the effectiveness of our approach, we introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature. CyLit empowers researchers by providing access to context-specific resources and enabling the tracking of trends in the dynamic and rapidly evolving field of cyber risk. Through the automatic processing of large volumes of data, our NLP-powered solution significantly enhances the efficiency and specificity of academic literature searches. We compare the literature categorization results of CyLit to those presented in survey papers or generated by ChatGPT, highlighting the distinctive insights this tool provides into cyber risk research literature. Using NLP techniques, we aim to revolutionize the way researchers discover, analyze, and utilize academic resources, ultimately fostering advancements in various domains of knowledge.

   @misc{zhang2024nlp,
     title = {NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit},
     year = {2024},
     author = {Zhang, Linfeng and Hu, Changyue and Quan, Zhiyu},
     eprint = {2409.06226},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
     doi = {10.48550/arXiv.2409.06226},
     url = {https://doi.org/10.48550/arXiv.2409.06226},
   }

2022

1. 

   Imbalanced Learning for Insurance Using Modified Loss Functions in Tree-based Models

   Changyue Hu, Zhiyu Quan, and Wing Fung Chong

   Insurance: Mathematics and Economics 2022

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   Tree-based models have gained momentum in insurance claim loss modeling; however, the point mass at zero and the heavy tail of insurance loss distribution pose the challenge to apply conventional methods directly to claim loss modeling. With a simple illustrative dataset, we first demonstrate how the traditional tree-based algorithm’s splitting function fails to cope with a large proportion of data with zero responses. To address the imbalance issue presented in such loss modeling, this paper aims to modify the traditional splitting function of Classification and Regression Tree (CART). In particular, we propose two novel modified loss functions, namely, the weighted sum of squared error and the sum of squared Canberra error. These modified loss functions impose a significant penalty on grouping observations of non-zero response with those of zero response at the splitting procedure, and thus significantly enhance their separation. Finally, we examine and compare the predictive performance of such modified tree-based models to the traditional model on synthetic datasets that imitate insurance loss. The results show that such modification leads to substantially different tree structures and improved prediction performance.

   @article{hu2022imbalanced,
     title = {Imbalanced Learning for Insurance Using Modified Loss Functions in Tree-based Models},
     author = {Hu, Changyue and Quan, Zhiyu and Chong, Wing Fung},
     journal = {Insurance: Mathematics and Economics},
     volume = {106},
     pages = {13--32},
     year = {2022},
     publisher = {Elsevier},
     doi = {10.1016/j.insmatheco.2022.04.010},
     url = {https://doi.org/10.1016/j.insmatheco.2022.04.010},
   }