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AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods

Author

Listed:
  • Lu Xiong

    (Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA)

  • Vajira Manathunga

    (Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA)

  • Jiyao Luo

    (Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA)

  • Nicholas Dennison

    (Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA)

  • Ruicheng Zhang

    (Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA)

  • Zhenhai Xiang

    (Department of Mathematical Sciences, College of Basic and Applied Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA)

Abstract

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing risks and determining insurance premiums for personal vehicles. However, the app is not limited exclusively to actuaries. Other individuals or entities, such as insurance companies, researchers, or analysts, who have access to the necessary data and require insights or analysis related to personal auto insurance, can also benefit from using the app. It is the first web-based application of its kind that is free to use and deployable from the personal computer or mobile device. AutoReserve is a software solution that caters to the needs of insurance professionals where only a few existing web-based applications are available. The application is divided into three parts: a summary of the loss data, a classical loss reserving tool, and a machine learning loss reserving tool. Each component of the application functions differently and allows for inputs from the user to analyze the provided loss data. The user, in other words, individuals or entities who utilize the Auto Reserve application, can then use the outputs for these three sections to improve his or her risk management or loss reserving process. AutoReserve is unique compared to other loss reserving tools because of its ability to employ both traditional, spreadsheet-based and modern, machine-learning-based loss reserving tools. AutoReserve is accessible on the web. The app is currently usable and is still undergoing frequent updates with new features and bug fixes.

Suggested Citation

  • Lu Xiong & Vajira Manathunga & Jiyao Luo & Nicholas Dennison & Ruicheng Zhang & Zhenhai Xiang, 2023. "AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods," Risks, MDPI, vol. 11(7), pages 1-17, July.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:7:p:131-:d:1194165
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    References listed on IDEAS

    as
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