Risk Assessment for Accounting Professional Liability Insurance

Yıl 2016, , 93 - 112, 29.10.2016

Şerafettin Sevim , Birol Yıldız Nilüfer Dalkılıç

https://doi.org/10.17233/se.2016.06.004

Cited By: 1

Öz

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.

Kaynakça

• Bonner, S.E. & Z. Palmrose & S.M. Young (1998), “Fraud Type and Auditor Litigation: An Analysis of SEC Accounting and Auditing Enforcement Releases”, The Accounting Review, 73, 503-532.
• Carcello, J.V. & Z. Palmrose (1994), “Auditor Litigation and Modified Reporting on Bankrupt Clients”, Journal of Accounting Research, 32, 1-30.
• Craig, F.L. (1995), “With Neural Networks, Computers do More than Just Follow Orders”, The Michigan CPA, Summer, 47(1), 9.
• Dorfman, M.S. (1991), Introduction to Risk Management and Insurance, Prentice-Hall Inc., NJ.
• Ellacott, S. & D. Bose (1996), Neural Networks: Deterministic Methods of Analyis, International Thomson Computer Press, London.
• Fausett, L.V. (1994), Fundamentals of Neural Networks: Architectures, Algorithms, and Application, Prentice-Hall Inc., NJ.
• Ferguson, M.J. & A. Majid (2003), “To Sue or Not to Sue: An Experimental Study of Factors Affecting Hong Kong Liquidators Audit Litigation Decisions”, Journal of Business Ethics, 46, 363-374.
• Francis, J. & D. Philbrick & K. Schipper (1998), “Earnings Surprises and Litigation Risk”, Journal of Financials Statement Analysis, 3(2), 15-27.
• He, H. & J. Wang & W. Graco & S. Hawkins (1997), “Application of Neural Networks to Detection of Medical Fraud”, Expert Systems with Applications, 13(4), 329-336.
• Hsu, S. & C. Lin & Y. Yang (2008), “Integrating Neural Networks for Risk-Adjustment Models”, Journal of Risk and Insurance, 75(3), 617-642.
• Ismael, M.B. (1999), “Prediction of mortality and in-hospital complications for acute myocardial infarction patients using artificial neural networks”, Ph.D. Dissertation, Duke University, NC.
• Jablonoeski, M. (1998), “Automating the Risk Assessment Process”, CPCU Journal, 51(2), 101-107.
• Lin, C. (2009), “Using Neural Networks as a Support Tool in the Decision Making for Insurance Industry”, Expert Systems with Applications, 36, 6914-6917.
• Linville, M. (2001), “The Effects of State Tort Provisions and Perceptions of Litigation Risk On Malpractice Insurance”, Journal of Applied Business Research, 17(3), 61-71.
• Linville, M. & J. Thornton (2001), “Litigation Risk Factors As Identified By Malpractice Insurance Carriers”, Journal of Applied Business Research, 17(4), 93-105.
• Lowe, J. & L. Pryor (1996), “Neural Network v. GLMs in Pricing General Insurance”, 1996 General Insurance Convention, 1, 417-438.
• Lys, T. & R. Watts (1994), “Lawsuits Againts Auditors”, Journal of Accounting Research, 32, 65-93.
• Paliwal, M. & U.A. Kumar (2009), “Neural Networks and Statistical Techniques: A Review of Applications”, Expert Systems with Applications, 36, 2-17.
• Palmrose, Z. (1987), “Litigation and Independent Auditors: The Role of Business Failures and Management Fraud”, Auditing: A Journal of Practice & Theory, 6(2), 90-103.
• Schultz, J.J. & S.G. Gustavson (1978), “Actuaries’ Perceptions of Variables Affecting the Independent Auditor’s Legal Liability”, The Acoounting Rewiew, 3, 626-641.
• Shah, P. & A. Guez (2009), “Mortality Forecasting Using Neural Networks and an Application to Cause-Specific Data for Insurance Purposes”, Journal of Forecasting, 28(6), 535-548.
• Shapiro, A.F. (2002), “The Merging of Neural Networks, Fuzzy Logic and Genetic Algorithms”, Insurance. Mathematics and Economics, 31(1), 115-131.
• Shapiro, A.F. & L.C. Jean (2003), Intelligent and Other Computational Techniques in Insurance: Theory and Applications, World Scientific Publishing, Danvers.
• Shapiro, L.E. (2004), “Professional Liability Insurance”, Ashrae Journal, 46(10), 59-60.
• Simunic, D.A. & M.T. Stein (1996), “The Impact of Litigation Risk on Audit Pricing: A Review of the Economics and the Evidence”, Auditing: A Journal of Practice & Theory, 15, 119-134.
• Smith, K.A. & R.J. Willis & M. Brooks (2000), “An Analysis of Customer Retention and Insurance Claim Patterns Using Data Mining: A Case Study”, The Journal of the Operational Research Society, 51(5), 532-541.
• Stice, J.D. (1991), “Using Financial and Market Information to Identify Pre-Engagement Factors Associated with Lawsuits Against Auditors”, The Accounting Review, 56(3), 516-533.
• St. Pierre, K. & J.A. Anderson (1984), “An Analysis of the Factors Associated with Lawsuits against Public Accountants”, The Accountant Review, 2, 242-263.
• Tauhet, C. (1997), “Neural Networks: Not Just A Black Box”, Insurance & Technology, 22(4), 30-32.
• Trippi, R.R. & E. Turban (1996), Neural Networks in Finance and Investing, Irwin Publishing, Chicago.
• TSRŞB (2010), Mesleki Sorumluluk Sigortası Genel Şartları [General Conditions for Accounting Professional Liability Insurance], <http://tsrsb.org.tr/sayfa/mesleki-sorumluluk-sigortasi-genel-sartlari>, 16.7.2010.
• Vaughan, E.J. & T. Vaughan (2007), Fundamentals of Risk and Insurance, John Wiley & Sons, Inc., NJ.
• Vaugh, M. & E. Ong & S.J. Cavill (1997), “Interpretation and Knowledge Discovery from a Multilayer Perceptron Network that Performs Whole Life Assurance Risk Assesment”, Neural Computing Applications, 6, 201-213.
• Yeo, A.C. & K.A. Smith & R.J. Willis & M. Brooks (2002), “A Mathematical Programming Approach to Optimise Insurance Premium Pricing within a Data Mining Framework”, The Journal of the Operational Research Society, 53(11), 1197-1203.
• Yıldız, B. (2001), “Prediction of Financial Failure with Artificial Neural Network Technology and an Emprical Application on Publicly Held Companies”, ISE Review, 5(17), 47-62.