IDEAS home Printed from https://ideas.repec.org/a/sos/sosjrn/160305.html
   My bibliography  Save this article

Risk Assessment for Accounting Professional Liability Insurance

Author

Listed:
  • Şerafettin SEVİM
  • Birol YILDIZ
  • Nilüfer DALKILIÇ

Abstract

In this study, litigation risk factors were determined for accounting professional liability insurance and an artificial neural network was developed to determine the litigation risks. A training data set comprised of data from 201 policies was used to train an artificial neural network. The performance of the artificial neural network model was then assessed using a test data set comprised of data from 100 policies. In the research, a litigation risk estimation model was formed for liability insurance via an artificial neural network model. By comparing the litigation risks occurring in accounting professional liability insurance to those foreseen by the artificial neural network system, it was determined that the results were quite consistent. It was also determined that the realized results and the risks foreseen in the artificial neural network model provided data close to the real values and that the artificial neural network model could foresee the litigation risks in accounting professional liability insurance with a 99% success rate.

Suggested Citation

  • Şerafettin SEVİM & Birol YILDIZ & Nilüfer DALKILIÇ, 2016. "Risk Assessment for Accounting Professional Liability Insurance," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
    • Handle: RePEc:sos:sosjrn:160305
    as

    Download full text from publisher

    File URL: http://dergipark.gov.tr/download/article-file/227819
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A C Yeo & K A Smith & R J Willis & M Brooks, 2002. "A mathematical programming approach to optimise insurance premium pricing within a data mining framework," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1197-1203, November.
    2. K A Smith & R J Willis & M Brooks, 2000. "An analysis of customer retention and insurance claim patterns using data mining: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 532-541, May.
    3. Paras Shah & Allon Guez, 2009. "Mortality forecasting using neural networks and an application to cause-specific data for insurance purposes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 535-548.
    4. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
    5. Carcello, Jv & Palmrose, Zv, 1994. "Auditor Litigation And Modified Reporting On Bankrupt Clients," Journal of Accounting Research, Wiley Blackwell, vol. 32, pages 1-30.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.

    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. Yann Braouezec, 2015. "Public versus Private Insurance System with (and without) Transaction Costs: Optimal Segmentation Policy of an Informed monopolistPublic versus Private Insurance System with (and without) Transaction ," Working Papers 2013-ECO-23, IESEG School of Management, revised May 2014.
    2. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
    3. Waymond Rodgers & Andrés Guiral & José Gonzalo, 2009. "Different Pathways that Suggest Whether Auditors’ Going Concern Opinions are Ethically Based," Journal of Business Ethics, Springer, vol. 86(3), pages 347-361, May.
    4. G'abor Petneh'azi & J'ozsef G'all, 2019. "Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?," Papers 1909.05501, arXiv.org, revised Oct 2019.
    5. repec:bof:bofrdp:urn:nbn:fi:bof-201508131351 is not listed on IDEAS
    6. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    7. Muhammad Farhan Malik & Yuan George Shan & Jamie Yixing Tong, 2022. "Do auditors price litigious tone?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1715-1760, April.
    8. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    9. Sattar A. Mansi & William F. Maxwell & Darius P. Miller, 2004. "Does Auditor Quality and Tenure Matter to Investors? Evidence from the Bond Market," Journal of Accounting Research, Wiley Blackwell, vol. 42(4), pages 755-793, September.
    10. Karla M. Johnstone & Jean C. Bedard, 2004. "Audit Firm Portfolio Management Decisions," Journal of Accounting Research, Wiley Blackwell, vol. 42(4), pages 659-690, September.
    11. Ja-Shen Chen & Russell K H Ching & Yi-Shen Lin, 2004. "An extended study of the K-means algorithm for data clustering and its applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 976-987, September.
    12. Benito Arrunada, 2000. "Audit quality: attributes, private safeguards and the role of regulation," European Accounting Review, Taylor & Francis Journals, vol. 9(2), pages 205-224.
    13. Richard Chung & Michael Firth & Jeong-Bon Kim, 2003. "Auditor conservatism and reported earnings," Accounting and Business Research, Taylor & Francis Journals, vol. 33(1), pages 19-32.
    14. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    15. Emiliano Ruiz-Barbadillo & Nieves Gomez-Aguilar & Cristina De Fuentes-Barbera & Maria Antonia Garcia-Benau, 2004. "Audit quality and the going-concern decision-making process: Spanish evidence," European Accounting Review, Taylor & Francis Journals, vol. 13(4), pages 597-620.
    16. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    17. Bailey, Wendy J. & Sawers, Kimberly M., 2018. "Moving toward a principle-based approach to U.S. accounting standard setting: A demand for procedural justice and accounting reform," Advances in accounting, Elsevier, vol. 43(C), pages 1-13.
    18. Piet Sercu & Heidi Bauwhede & Marleen Willekens, 2006. "Post-Enron Implicit Audit Reporting Standards: Sifting through the Evidence," De Economist, Springer, vol. 154(3), pages 389-403, September.
    19. Daniel Aobdia & Luminita Enache & Anup Srivastava, 2021. "Changes in Big N auditors’ client selection and retention strategies over time," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 715-754, February.
    20. Peecher, Mark E. & Solomon, Ira & Trotman, Ken T., 2013. "An accountability framework for financial statement auditors and related research questions," Accounting, Organizations and Society, Elsevier, vol. 38(8), pages 596-620.
    21. Sanoran, Kanyarat (Lek), 2018. "Auditors’ going concern reporting accuracy during and after the global financial crisis," Journal of Contemporary Accounting and Economics, Elsevier, vol. 14(2), pages 164-178.

    More about this item

    Keywords

    Insurance Industry; Litigation Risk; Accounting Professional Liability Insurance; Risk Assessment; Artificial Neural Network.;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

    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:sos:sosjrn:160305. 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: Aysen Sivrikaya (email available below). General contact details of provider: http://www.sosyoekonomijournal.org/home.html .

    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.