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Data mining applications in accounting: A review of the literature and organizing framework

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  • Amani, Farzaneh A.
  • Fadlalla, Adam M.

Abstract

This paper explores the applications of data mining techniques in accounting and proposes an organizing framework for these applications. A large body of literature reported on specific uses of the important data mining paradigm in accounting, but research that takes a holistic view of these uses is lacking. To organize the literature on the applications of data mining in accounting, we create a framework that combines the two well-known accounting reporting perspectives (retrospection and prospection), and the three well-accepted goals of data mining (description, prediction, and prescription). The framework encapsulates a taxonomy of four categories (retrospective-descriptive, retrospective-prescriptive, prospective-prescriptive, and prospective-predictive) of data mining applications in accounting. The proposed framework revealed that the area of accounting that benefited the most from data mining is assurance and compliance, including fraud detection, business health and forensic accounting. The clear gaps seem to be in the two prescriptive application categories (retrospective-prescriptive and prospective-prescriptive), indicating opportunities for benefiting from data mining in these application categories. The framework presents a holistic view of the literature and systematically organizes it in a structurally logical and thematically coherent manner.

Suggested Citation

  • Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
  • Handle: RePEc:eee:ijoais:v:24:y:2017:i:c:p:32-58
    DOI: 10.1016/j.accinf.2016.12.004
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    1. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 495-540, May.
    2. Chodak, Grzegorz & Suchacka, Grażyna, 2012. "Cost-oriented recommendation model for e-commerce," MPRA Paper 39542, University Library of Munich, Germany.
    3. Sumit Chakraborty & Sushil K. Sharma, 2007. "Prediction of corporate financial health by Artificial Neural Network," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(4), pages 442-459.
    4. Burcu Dikmen & Güray Küçükkocaoğlu, 2010. "The detection of earnings manipulation: the three-phase cutting plane algorithm using mathematical programming," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 442-466.
    5. Jones, Jj, 1991. "Earnings Management During Import Relief Investigations," Journal of Accounting Research, Wiley Blackwell, vol. 29(2), pages 193-228.
    6. Hian Koh & Sen Tan, 1999. "A neural network approach to the prediction of going concern status," Accounting and Business Research, Taylor & Francis Journals, vol. 29(3), pages 211-216.
    7. Cathy Beaudoin & Nandini Chandar & Edward M. Werner, 2010. "Are potential effects of SFAS 158 associated with firms' decisions to freeze their defined benefit pension plans?," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 9(4), pages 424-451, November.
    8. Maria Krambia-Kapardis & Chris Christodoulou & Michalis Agathocleous, 2010. "Neural networks: the panacea in fraud detection?," Managerial Auditing Journal, Emerald Group Publishing, vol. 25(7), pages 659-678, July.
    9. Garry D. Carnegie, 2012. "The special issue:AAAJand research innovation," Accounting, Auditing & Accountability Journal, Emerald Group Publishing Limited, vol. 25(2), pages 216-227, February.
    10. Golam Kabir & M. Ahsan Akhtar Hasin, 2013. "Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 14(1), pages 74-103.
    11. Mauldin, Elaine G. & Ruchala, Linda V., 1999. "Towards a meta-theory of accounting information systems," Accounting, Organizations and Society, Elsevier, vol. 24(4), pages 317-331, May.
    12. Khlif, Hichem & Chalmers, Keryn, 2015. "A review of meta-analytic research in accounting," Journal of Accounting Literature, Elsevier, vol. 35(C), pages 1-27.
    13. Yigitbasioglu, Ogan M. & Velcu, Oana, 2012. "A review of dashboards in performance management: Implications for design and research," International Journal of Accounting Information Systems, Elsevier, vol. 13(1), pages 41-59.
    14. Angela K. Davis & Isho Tama†Sweet, 2012. "Managers’ Use of Language Across Alternative Disclosure Outlets: Earnings Press Releases versus MD&A," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 804-837, September.
    15. Beynon, Malcolm J. & Peel, Michael J. & Tang, Yu-Cheng, 2004. "The application of fuzzy decision tree analysis in an exposition of the antecedents of audit fees," Omega, Elsevier, vol. 32(3), pages 231-244, June.
    16. Granlund, Markus, 2011. "Extending AIS research to management accounting and control issues: A research note," International Journal of Accounting Information Systems, Elsevier, vol. 12(1), pages 3-19.
    17. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
    18. Vasarhelyi, Miklos A. & Alles, Michael & Kuenkaikaew, Siripan & Littley, James, 2012. "The acceptance and adoption of continuous auditing by internal auditors: A micro analysis," International Journal of Accounting Information Systems, Elsevier, vol. 13(3), pages 267-281.
    19. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    20. Chakraborty, Vasundhara & Chiu, Victoria & Vasarhelyi, Miklos, 2014. "Automatic classification of accounting literature," International Journal of Accounting Information Systems, Elsevier, vol. 15(2), pages 122-148.
    21. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    22. Cho, Charles H. & Roberts, Robin W. & Patten, Dennis M., 2010. "The language of US corporate environmental disclosure," Accounting, Organizations and Society, Elsevier, vol. 35(4), pages 431-443, May.
    23. E Koskivaara, 2000. "Artificial neural network models for predicting patterns in auditing monthly balances," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(9), pages 1060-1069, September.
    24. Ting†Peng Liang & John S. Chandler & Ingoo Han & Jinsheng Roan, 1992. "An empirical investigation of some data effects on the classification accuracy of probit, ID3, and neural networks," Contemporary Accounting Research, John Wiley & Sons, vol. 9(1), pages 306-328, September.
    25. Efstathios KIRKOS, 2012. "Predicting Auditor Switches By Applying Data Mining," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 7(3(21)/ Fa), pages 246-261.
    26. Davis, Jefferson T. & Massey, Anne P. & Lovell, Ronald E. R., 1997. "Supporting a complex audit judgment task: An expert network approach," European Journal of Operational Research, Elsevier, vol. 103(2), pages 350-372, December.
    27. Kim, Hyo-Jeong & Mannino, Michael & Nieschwietz, Robert J., 2009. "Information technology acceptance in the internal audit profession: Impact of technology features and complexity," International Journal of Accounting Information Systems, Elsevier, vol. 10(4), pages 214-228.
    28. C C Reyes-Aldasoro & A R Ganguly & G Lemus & A Gupta, 1999. "A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimisation applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(1), pages 85-94, January.
    29. Gareth Owen, 2013. "Integrated Reporting: A Review of Developments and their Implications for the Accounting Curriculum," Accounting Education, Taylor & Francis Journals, vol. 22(4), pages 340-356, August.
    30. Gumus, Alev Taskin & Guneri, Ali Fuat & Ulengin, Fusun, 2010. "A new methodology for multi-echelon inventory management in stochastic and neuro-fuzzy environments," International Journal of Production Economics, Elsevier, vol. 128(1), pages 248-260, November.
    31. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
    32. Sunita Goel & Jagdish Gangolly, 2012. "Beyond The Numbers: Mining The Annual Reports For Hidden Cues Indicative Of Financial Statement Fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 75-89, April.
    33. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    34. Ittner, Christopher D. & Larcker, David F., 2001. "Assessing empirical research in managerial accounting: a value-based management perspective," Journal of Accounting and Economics, Elsevier, vol. 32(1-3), pages 349-410, December.
    35. Minna Kallio & Barbro Back, 2011. "The Self-Organizing Map in Selecting Companies for Tax Audit," Springer Books, in: Andrea Carugati & Cecilia Rossignoli (ed.), Emerging Themes in Information Systems and Organization Studies, pages 347-358, Springer.
    36. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    37. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    38. Richardson, Peter & Dellaportas, Steven & Perera, Luckmika & Richardson, Ben, 2015. "Towards a conceptual framework on the categorization of stereotypical perceptions in accounting," Journal of Accounting Literature, Elsevier, vol. 35(C), pages 28-46.
    39. Sumit Lodhia & Nigel Martin, 2011. "Stakeholder responses to the National Greenhouse and Energy Reporting Act: An agenda setting perspective," Accounting, Auditing & Accountability Journal, Emerald Group Publishing, vol. 25(1), pages 126-145, December.
    40. Dan-Bee Song & Ho-Young Lee & Eun-Jung Cho, 2013. "The association between earnings management and asset misappropriation," Managerial Auditing Journal, Emerald Group Publishing, vol. 28(6), pages 542-567, June.
    41. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    42. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    43. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    44. Salterio, Steven, 1996. "The effects of precedents and client position on auditors' financial accounting policy judgment," Accounting, Organizations and Society, Elsevier, vol. 21(5), pages 467-486, July.
    45. Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    46. Altay Guvenir, H. & Erel, Erdal, 1998. "Multicriteria inventory classification using a genetic algorithm," European Journal of Operational Research, Elsevier, vol. 105(1), pages 29-37, February.
    47. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    48. Gareth Owen, 2013. "A Rejoinder to Commentaries on 'Integrated Reporting: A Review of Developments and their Implications for the Accounting Curriculum'," Accounting Education, Taylor & Francis Journals, vol. 22(4), pages 363-365, August.
    49. Blacconiere, Walter G. & DeFond, Mark L., 1997. "An investigation of independent audit opinions and subsequent independent auditor litigation of publicly-traded failed savings and loans," Journal of Accounting and Public Policy, Elsevier, vol. 16(4), pages 415-454.
    50. Landajo, Manuel & de Andres, Javier & Lorca, Pedro, 2007. "Robust neural modeling for the cross-sectional analysis of accounting information," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1232-1252, March.
    51. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    52. James R. Coakley & Carol E. Brown, 1993. "Artificial Neural Networks Applied to Ratio Analysis in the Analytical Review Process," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 2(1), pages 19-39, January.
    53. Gaafar, Lotfi K. & Choueiki, M. Hisham, 2000. "A neural network model for solving the lot-sizing problem," Omega, Elsevier, vol. 28(2), pages 175-184, April.
    54. Chan, David Y. & Vasarhelyi, Miklos A., 2011. "Innovation and practice of continuous auditing," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 152-160.
    55. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
    56. Huerta, Esperanza & Glandon, TerryAnn & Petrides, Yanira, 2012. "Framing, decision-aid systems, and culture: Exploring influences on fraud investigations," International Journal of Accounting Information Systems, Elsevier, vol. 13(4), pages 316-333.
    57. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    58. Amelia A. Baldwin & Carol E. Brown & Brad S. Trinkle, 2006. "Opportunities for artificial intelligence development in the accounting domain: the case for auditing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 77-86, July.
    59. Gray, Glen L. & Debreceny, Roger S., 2014. "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 357-380.
    60. Williams, James W., 2013. "Regulatory technologies, risky subjects, and financial boundaries: Governing ‘fraud’ in the financial markets," Accounting, Organizations and Society, Elsevier, vol. 38(6), pages 544-558.
    61. Jans, Mieke & Lybaert, Nadine & Vanhoof, Koen, 2010. "Internal fraud risk reduction: Results of a data mining case study," International Journal of Accounting Information Systems, Elsevier, vol. 11(1), pages 17-41.
    62. Bhimani, Alnoor & Gulamhussen, Mohamed Azzim & Lopes, Samuel, 2009. "The effectiveness of the auditor's going-concern evaluation as an external governance mechanism: Evidence from loan defaults," The International Journal of Accounting, Elsevier, vol. 44(3), pages 239-255, September.
    63. Birnberg, Jacob G., 1980. "The role of accounting in financial disclosure," Accounting, Organizations and Society, Elsevier, vol. 5(1), pages 71-80, January.
    64. Chris Charalambous & Andreas Charitou & Froso Kaourou, 2000. "Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction," Annals of Operations Research, Springer, vol. 99(1), pages 403-425, December.
    65. Jans, Mieke & Alles, Michael & Vasarhelyi, Miklos, 2013. "The case for process mining in auditing: Sources of value added and areas of application," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 1-20.
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