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AI-powered decision-making in facilitating insurance claim dispute resolution

Author

Listed:
  • Zhang, Wen
  • Shi, Jingwen
  • Wang, Xiaojun
  • Wynn, Henry

Abstract

Leveraging Artificial Intelligence (AI) techniques to empower decision-making can promote social welfare by generating significant cost savings and promoting efficient utilization of public resources, besides revolutionizing commercial operations. This study investigates how AI can expedite dispute resolution in road traffic accident (RTA) insurance claims, benefiting all parties involved. Specifically, we devise and implement a disciplined AI-driven approach to derive the cost estimates and inform negotiation decision-making, compared to conventional practices that draw upon official guidance and lawyer experience. We build the investigation on 88 real-life RTA cases and detect an asymptotic relationship between the final judicial cost and the duration of the most severe injury, marked by a notable predicted R2 value of 0.527. Further, we illustrate how various AI-powered toolkits can facilitate information processing and outcome prediction: (1) how regular expression (RegEx) collates precise injury information for subsequent predictive analysis; (2) how alternative natural language processing (NLP) techniques construct predictions directly from narratives. Our proposed RegEx framework enables automated information extraction that accommodates diverse report formats; different NLP methods deliver comparable plausible performance. This research unleashes AI’s untapped potential for social good to reinvent legal-related decision-making processes, support litigation efforts, and aid in the optimization of legal resource consumption.

Suggested Citation

  • Zhang, Wen & Shi, Jingwen & Wang, Xiaojun & Wynn, Henry, 2023. "AI-powered decision-making in facilitating insurance claim dispute resolution," LSE Research Online Documents on Economics 120649, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:120649
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    File URL: http://eprints.lse.ac.uk/120649/
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    professional service operation; insurance claim; civil litigation; AI; natural language processing;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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