IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v63y2022ics0275531922001556.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531922001556
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2022.101769?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Adrian BANARESCU & Aurel-Mihail BALOI, 2015. "Preventing and Detecting Fraud through Data Analytics in auto insurance field," Romanian Journal of Economics, Institute of National Economy, vol. 40(1(49)), pages 89-114, june.
    2. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    3. Steven B. Caudill & Mercedes Ayuso & Montserrat Guillén, 2005. "Fraud Detection Using a Multinomial Logit Model With Missing Information," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 539-550, December.
    4. Duan, Yuejiao & Goodell, John W. & Li, Haoran & Li, Xinming, 2022. "Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set," Finance Research Letters, Elsevier, vol. 46(PA).
    5. Shinichi Nakagawa, 2004. "A farewell to Bonferroni: the problems of low statistical power and publication bias," Behavioral Ecology, International Society for Behavioral Ecology, vol. 15(6), pages 1044-1045, November.
    6. Jean Pinquet & Mercedes Ayuso & Montserrat Guillén, 2007. "Selection Bias and Auditing Policies for Insurance Claims," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(2), pages 425-440, June.
    7. Viaene, Stijn & Ayuso, Mercedes & Guillen, Montserrat & Van Gheel, Dirk & Dedene, Guido, 2007. "Strategies for detecting fraudulent claims in the automobile insurance industry," European Journal of Operational Research, Elsevier, vol. 176(1), pages 565-583, January.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. Georges Dionne & Florence Giuliano & Pierre Picard, 2009. "Optimal Auditing with Scoring: Theory and Application to Insurance Fraud," Management Science, INFORMS, vol. 55(1), pages 58-70, January.
    10. Warren, Danielle E. & Schweitzer, Maurice E., 2021. "When weak sanctioning systems work: Evidence from auto insurance industry fraud investigations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 166(C), pages 68-83.
    11. El Bachir, Belhadji & Dionne, Georges, 1997. "Development of an expert system for the automatic detection of automobile insurance fraud," Working Papers 97-6, HEC Montreal, Canada Research Chair in Risk Management.
    12. Véronique Van Vlasselaer & Tina Eliassi-Rad & Leman Akoglu & Monique Snoeck & Bart Baesens, 2017. "GOTCHA! Network-Based Fraud Detection for Social Security Fraud," Management Science, INFORMS, vol. 63(9), pages 3090-3110, September.
    13. repec:ine:journl:v:40:y:2015:i:49:p:63-88 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    2. Bermúdez, Ll. & Pérez, J.M. & Ayuso, M. & Gómez, E. & Vázquez, F.J., 2008. "A Bayesian dichotomous model with asymmetric link for fraud in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 779-786, April.
    3. Denisa BANULESCU-RADU & Meryem YANKOL-SCHALCK, 2021. "Fraud detection in the era of Machine Learning: a household insurance case," LEO Working Papers / DR LEO 2904, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    4. Dionne, Georges, 2012. "The empirical measure of information problems with emphasis on insurance fraud and dynamic data," Working Papers 12-10, HEC Montreal, Canada Research Chair in Risk Management.
    5. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    6. Katja Müller & Hato Schmeiser & Joël Wagner, 2016. "The impact of auditing strategies on insurers’ profitability," Journal of Risk Finance, Emerald Group Publishing, vol. 17(1), pages 46-79, January.
    7. Chu-Shiu Li & Chwen-Chi Liu & Sheng-Chang Peng, 2013. "Expiration Dates in Automobile Insurance Contracts: The Curious Case of Last Policy Month Claims in Taiwan," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 38(1), pages 23-47, March.
    8. Lina Bouayad & Balaji Padmanabhan & Kaushal Chari, 2019. "Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms," Information Systems Research, INFORMS, vol. 30(2), pages 466-485, June.
    9. Ming-Jyh Wang & Chieh-Hua Wen & Lawrence W Lan, 2010. "Modelling Different Types of Bundled Automobile Insurance Choice Behaviour: The Case of Taiwan*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 35(2), pages 290-308, April.
    10. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," MPRA Paper 75726, University Library of Munich, Germany, revised Dec 2013.
    11. Farbmacher, Helmut & Löw, Leander & Spindler, Martin, 2022. "An explainable attention network for fraud detection in claims management," Journal of Econometrics, Elsevier, vol. 228(2), pages 244-258.
    12. Höppner, Sebastiaan & Baesens, Bart & Verbeke, Wouter & Verdonck, Tim, 2022. "Instance-dependent cost-sensitive learning for detecting transfer fraud," European Journal of Operational Research, Elsevier, vol. 297(1), pages 291-300.
    13. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
    14. Mavruk, Taylan, 2022. "Analysis of herding behavior in individual investor portfolios using machine learning algorithms," Research in International Business and Finance, Elsevier, vol. 62(C).
    15. Michele Tumminello & Andrea Consiglio & Pietro Vassallo & Riccardo Cesari & Fabio Farabullini, 2023. "Insurance fraud detection: A statistically validated network approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 381-419, June.
    16. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    17. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    18. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    19. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    20. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:riibaf:v:63:y:2022:i:c:s0275531922001556. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.