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A decision support system for liability in civil litigation: a case study from an insurance company

Author

Listed:
  • Wen Zhang

    (University of Essex)

  • Andrew Dunkley

    (Herbert Smith Freehills)

  • Urvi Kanabar

    (BLM LLP)

  • David Elliott

    (BLM LLP)

  • Henry P. Wynn

    (The London School of Economics and Political Science)

Abstract

The use of statistical and AI methods in civil litigation is an area likely to expand. As with many areas of social science, the data requirements are high but complex, because of the complexity of the legal process and the nature of the causal connections. This paper looks at the early stage of the process where the initial establishment of liability acts as a legal triage which affects the route through the litigation process. A simple model is used in which the training set is the assessment of the probability of liability given hypothetical scenarios in road traffic accidents. The model is augmented by additional “weight of evidence” assessments. The model, once built, is used as a decision support system for claim handlers on a routine basis. The methods can be seen as a way of utilising a special type of expert judgment elicitation.

Suggested Citation

  • Wen Zhang & Andrew Dunkley & Urvi Kanabar & David Elliott & Henry P. Wynn, 2022. "A decision support system for liability in civil litigation: a case study from an insurance company," Annals of Operations Research, Springer, vol. 315(2), pages 695-706, August.
    • Handle: RePEc:spr:annopr:v:315:y:2022:i:2:d:10.1007_s10479-020-03905-0
    DOI: 10.1007/s10479-020-03905-0
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    References listed on IDEAS

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    1. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    2. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
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