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The value of cross-data set analysis for automobile insurance fraud detection

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  • Yankol-Schalck, Meryem

Abstract

This study focuses on personal automobile policies underwritten. Its aim is to provide decision support and to apply new models with good predictive performance and high operational efficiency. We propose a new approach by constructing a score that evolves over the life of a claim. It consists of creating a score at the opening of a claim and another derived from the information of the first adjuster’s report. Natural language processing is also used on a textual variable relating to the description of the claim provided by the agency. The fraud score is estimated by using a gradient boosting machine (GBM) and a neural network. The results are interpreted using the local interpretable model-agnostic explanations (LIME). They show that fraud detection is improved when all the information and the textual variable are included. Furthermore, we observe that the GBM method overperforms the neural network approach.

Suggested Citation

  • Yankol-Schalck, Meryem, 2022. "The value of cross-data set analysis for automobile insurance fraud detection," Research in International Business and Finance, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:riibaf:v:63:y:2022:i:c:s0275531922001556
    DOI: 10.1016/j.ribaf.2022.101769
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    References listed on IDEAS

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    More about this item

    Keywords

    Fraud detection; Automobile insurance; Cross-data set; Natural language processing; Boosting; Neutral network;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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