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Natural language interactions enhanced by data visualization to explore insurance claims and manage risk

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Abstract

Claims analysis and risk management is key to avoiding fraud and managing risk in the life insurance industry. Though visualization plays a fundamental role in supporting analysis tasks in the business domain, exploring user behaviour remains a challenging task. The prevalence of natural language interactions enhanced with data visualization has become quite the norm. With the increasing demand for visualization tools and varying levels of user expertise, the use of a natural language interface (NLI) is common. However, the design of visual analytics tools enhanced by NLIs for risk management and claim analysis requires thorough task analysis and domain expertise. In this work, we investigate an alternative approach through a natural language interaction-based interactive visualization such as a chart, pie chart, or histogram, which can be used for insurance claim analysis and risk management. We design a new visual analytics solution named InsCRMVis which is enhanced by NLIs. We present an expert evaluation of InsCRMVis based on the Task Load Index questionnaire which is currently the standard metrics measure to investigate its usability (effectiveness, efficiency, satisfaction) to support human task performance. To evaluate its performance, we performed a user study of ten experts which suggests that InsCRMVis can provide better insights and assistance to insurance managers in reducing loss and guiding changes to insurance premium policies. Furthermore, we provide a concise set of guidelines that can be used when visualizing risk to avoid dangers in the insurance domain. We discuss the challenges associated with the use of a visualization system in the insurance industry, focusing on aspects related to visualization research.

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Acknowledgements

This work is partially supported by the Australian Research Council (ARC) under Grant No. DP200101374 and LP170100891. We would like to thank the Australian insurance company for providing us with the unique dataset and the authors of NL4DV system, especially Arpit Narechania, for supporting us to run the code.

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Authors and Affiliations

  1. Advanced Analytics Institute (AAi), University of Technology Sydney (UTS), Ultimo, Australia

    Md Rafiqul Islam & Guandong Xu

  2. School of Information Technology, Deakin University, Geelong, Australia

    Imran Razzak

  3. School of Computer Science, University of Technology Sydney (UTS), Ultimo, Australia

    Xianzhi Wang

  4. Zurich Financial Services Australia Limited, Sydney, Australia

    Peter Tilocca

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  1. Md Rafiqul Islam
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  2. Imran Razzak
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  3. Xianzhi Wang
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  4. Peter Tilocca
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  5. Guandong Xu
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Correspondence to Guandong Xu.

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Islam, M.R., Razzak, I., Wang, X. et al. Natural language interactions enhanced by data visualization to explore insurance claims and manage risk. Ann Oper Res (2022). https://doi.org/10.1007/s10479-021-04465-7

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