Advertisement

Argumentative SOX Compliant and Intelligent Decision Support Systems for the Suppliers Contracting Process

Chapter
  • 2.4k Downloads
Part of the Intelligent Systems Reference Library book series (ISRL, volume 87)

Abstract

More and more our society is linked to the stability of financial markets and this stability depends on key players like private companies, financial markets, investors, analysts, government control agencies and so on. Sarbanes-Oxley Act is a mandatory law in EEUU market and a facto standard in rest of the world and has as main objective to keep the desire financial stability. Within this chapter it will be shown a new decision support intelligent financial model over SOX compatibility based on Artificial Intelligent technology together with Theory of Argumentation . The main aim of this model is to help and support private companies, auditors, executive boards and regulatory bodies to take a SOX compliant decision over an specific process of a typical purchasing financial cycle: The Contracting Process. The decision will be supported by the whole argumentation process drive by this model and will be reinforce with quality measures with the final objective to create a very clear argumentative background about the suggested decision. This model directly contributes to both scientific research artificial intelligence area and business sector. From business perspective it empowers the use of intelligent models and techniques to drive decision making over financial statements. From scientific and research area the impact is based on the combination of the following innovative elements: (1) an specific Information Seeking Dialog Protocol, (2) a Facts Valuation based Protocol in which previous gathered facts are analyzed, (3) the already incorporated initial knowledge coming from human expert knowledge, (4) the Intra-Agent Decision Making Protocol based on deductive argumentation and (5) the Semi Automated Fuzzy Dynamic Knowledge Learning Protocol giving as a result a novel approach to this kind of problems.

Keywords

Multiagent systems (MAS) Decision support systems (DSS) Sarbanes-Oxley act (SOX) Argumentation Artificial intelligence (AI) Business intelligence (BI) Expert systems (ES) Fuzzy knowledge 

Notes

Conflict of Interest Disclosure

The content of this chapter reflects only the opinion of the authors with independence of their affiliations. The authors do not have a direct financial relation with the commercial entities mentioned in this chapter.

References

  1. Alden, M., Bryan, D., Lessley, B., Tripathy, A.: Detection of financial statement fraud using evolutionary algorithms. J. Emerg. Technol. Account. 9(1), 71–94 (2012)Google Scholar
  2. Amgoud, L., Maudet, N., Parsons, S.: Modelling dialogues using argumentation. In: Proceedings of the 4th International Conference on Multi-Agent Systems (ICMAS’2000), pp. 31–38 (2000)Google Scholar
  3. Amgoud, L.: A Unified setting for inference and decision: an argumentation-based approach. ArXiv Preprint 12071363 (2012)Google Scholar
  4. Amgoud, L.: Postulates for logic-based argumentation systems. Int. J. Approximate Reasoning 55, 2028–2048 (2013)Google Scholar
  5. Atkinson, K., Bench-Capon, T., Walton, D.: Distinctive features of persuasion and deliberation dialogues. Argument Comput. 4(2), 105–127 (2013)Google Scholar
  6. Azhar, M., Parsons, S., Sklar, E.: An Argumentation-based dialogue system for human-robot collaboration. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, International Foundation for Autonomous Agents and Multiagent Systems, pp. 1353–1354 (2013)Google Scholar
  7. Belesiotis, A., Rovatsos, M., Rahwan, I.: A generative dialogue system for arguing about plans in situation calculus. In: Proceedings of the Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ARGMAS’09), vol. 6057, pp. 23–41. Springer, Berlin, Germany (2010)Google Scholar
  8. Bench-Capon, T.J.M., Dunne, P.E.: Argumentation in artificial intelligence. Artif. Intell. 171(10–15), 619–641 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  9. Besnard, P., Hunter, A.: Elements of Argumentation. The MIT Press, Cambridge (2008)CrossRefGoogle Scholar
  10. Boella, G., Hulstijn, J., Torre, L.: A logic of abstract argumentation. In: Proceedings of the Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’06), vol. 4049, pp. 29–41. Springer, Berlin, Germany (2006)Google Scholar
  11. Capera, D., Georgé, P.J., Gleizes, M.P., Glize, P.: Emergence of organisations, emergence of functions. In: AISB03 Convention, Symposium on Adaptive Agents and Multi-Agent Systems, pp. 103–108 (2003)Google Scholar
  12. Capobianco, M., Chesñevar, C., Simari, G.: An argument based framework to model an agent’s beliefs in a dynamic enviroment. In: Proceedings of the 1st International Workshop, Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’04), vol. 3366, pp. 95–110. Springer, Berlin, Germany (2004)Google Scholar
  13. Changchit, C., Holsapple, C., Madden, D.: Positive impacts of an intelligent system on internal control problem recognition. In: Proceedings of the 32nd Hawaii International Conference on System Sciences (HICSS’99), vol. 6, p. 10 (1999)Google Scholar
  14. Changchit, C., Holsapple, C.W.: The development of an expert system for managerial evaluation of internal controls. Intell. Syst. Account. Fin. Manage. 12(2), 103–120 (2004)CrossRefGoogle Scholar
  15. Chen, F., Sutchliffe, C.: Pricing and Hedging Short Sterling Options using Neural Networks. Intelligent Systems in Accounting, pp. 128–149. Wiley, New York (2012)Google Scholar
  16. Coakley, J., Gammill, L., Brown, C.: Artificial neural networks in accounting and finance: modelling issues. Int. J. Intell. Syst. Account. Fin. Manage. 9(2), 119–144 (1995)CrossRefGoogle Scholar
  17. Cogan, E., Parsons, S., McBurney, P.: New types of interagent dialogues. In: Proceedings of the Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’05), vol. 4049, pp. 154–168. Springer, Berlin, Germany (2005)Google Scholar
  18. Corchado, J.M., Laza, R.: Constructing deliberative agents with case-based reasoning technology. Int. J. Intell. Syst. 18(12), 1227–1241 (2003)CrossRefGoogle Scholar
  19. Corchado, J.M., Laza, R., Borrajo, L., et al.: Increasing the autonomy of deliberative agents with a case-based reasoning system. Int. J. Comput. Intell. Appl. World Sci. 3(1), 101–118 (2003)CrossRefGoogle Scholar
  20. Deshmukh, A., Talluru, L.: A rule-based fuzzy reasoning system for assessing the risk of management fraud. Intell. Syst. Account. Fin. Manage. 7(4), 223–241 (1998)CrossRefGoogle Scholar
  21. Devereux, J., Reed, C.: Strategic argumentation in rigorous persuasion dialogue. In: Proceedings of the Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ARGMAS’09), vol. 6057, pp. 94–113. Springer, Berlin, Germany (2009)Google Scholar
  22. Dimpoulos, Y., Nebel, B., Toni, F.: Preferred arguments are harder to compute than stable extensions. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI’99), vol. 16, pp. 36–43. Lawrence Erlbaum Associated LTD (1999)Google Scholar
  23. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)Google Scholar
  24. Esteva, M., Rodriguez, J., Sierra, C., Garcia, P., Arcos, J.: On the formal specifications of electronic institutions. Agent Mediated Electron. Commer. 1991, 126–147 (2001)CrossRefGoogle Scholar
  25. Fanning, K.M., Cogger, K.O.: Neural network detection of management fraud using published financial data. Int. J. Intell. Syst. Account. Fin. Manage. 7(1), 21–41 (1998)CrossRefGoogle Scholar
  26. Fanning, K., Cogger, K.: A comparative analysis of artificial neural networks using financial distress prediction. Int. J. Intell. Syst. Account. Fin. Manage. 3, 241–252 (1994)Google Scholar
  27. Fernandez, J.A., Martin Q., Corchado, J.M.: Argumentative SOX compliance and quality decision support intelligent expert system over the purchase orders approval process, Appl. Math. Comput. Sci. 4(4), 215–268 (2013a). ISSN 0976-1586Google Scholar
  28. Fernandez, J.A., Martin Q., Corchado, J.M.: Argumentative SOX compliant and quality decision support intelligent expert system over the suppliers selection process. Appl. Comput. Intell. Softw. Comput. 2013(973704), 23 pp (2013b). doi: 10.1155/2013/973704
  29. Fernandez, J.A., Martin, Q., Corchado, J.M.: Business intelligence expert system on SOX compliance over the purchase orders creation process. Intell. Inf. Manage. 5(3), 49–72 (2013c). doi: 10.4236/iim.2013.53007, ISSN 2160-5912, 2160-5920
  30. Fernandez, J.A., Martin, Q., Corchado, J.M.: Decision making intelligent agent on SOX compliance over the goods receipt process. Comput. Eng. Intell. Syst. 4(10), 1–18 (2013d). ISSN 2222-1719, 2222-2863Google Scholar
  31. Fox, J., Krause, P., Ambler, S.: Arguments, contradictions and practical reasoning. In: Proceedings of the 10th European Conference on Artificial Intelligence (ECAI’92), pp. 623–627. Wiley, New York (1992)Google Scholar
  32. Fukumoto, T., Sawamura, H.: Argumentation-based learning. In: Proceedings of the 3rd International Workshop, Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’06), vol. 4766, pp. 17–35. Springer, Berlin, Germany (2006)Google Scholar
  33. Gabbriellini, S., Torroni, P.: NetArg: an agent-based social simulator with argumentative agents. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multiagent Systems, pp. 1365–1366 (2013)Google Scholar
  34. Gabbriellini, S., Torroni, P.: A New Framework for ABMs based on Argumentative Reasoning. Advances in Social Simulation, pp. 25–36. Springer, Berlin (2014)Google Scholar
  35. Gholipour, R., Jandaghi, G., Rajaei, R.: Contractor selection in MCMD context using fuzzy AHP. Iran. J. Manage. Stud. 7(1), 151–173 (2014)Google Scholar
  36. Ho, G., Lp, W., Wu, C., Tse, Y.: Using a fuzzy association rule mining approach to identify the financial data association. Expert Syst. Appl. 39(10), 9054–9063 (2012)Google Scholar
  37. Hubner, J.F., Sichman, J.S., Boissier, O.: Using the Moise+ for a cooperative framework of MAS reorganisation. Lecture Notes in Computer Science, vol. 3171, pp. 506–515 (2004)Google Scholar
  38. Kakas, A., Maudet, N., Moraitis, P.: Layered strategies and protocols for argumentation-based agent interaction. In: Proceedings of the 1st InternationalWorkshop on Argumentation in Multi-Agent Systems (ArgMAS’04), pp. 64–77, July 2004Google Scholar
  39. Korvin, A., Shipley, M., Omer, K.: Assessing risks due to threats to internal control in a computer-based accounting information system: a pragmatic approach based on fuzzy set theory. Intell. Syst. Account. Fin. Manage. 12(2), 139–152 (2004)CrossRefGoogle Scholar
  40. Kraus, S., Sycara, K., Evenchik, A.: Reaching agreements through argumentation: a logical model and Implementation. Artif. Intell. 104(1–2), 1–69 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  41. Krause, P., Ambler, S., Elvang-Goransson, M., Fox, J.: A logic of argumentation for reasoning under uncertainty. Comput. Intell. 11(1), 113–131 (1995)CrossRefMathSciNetGoogle Scholar
  42. Kumar, A., Liu, R.: A rule-based framework using role patterns for business process compliance. In: Proceedings of the International Symposium on Rule Representation, Interchange and Reasoning on the Web, Lecture Notes in Computer Science (RuleML’08), vol. 5321, pp. 58–72. Springer, Berlin, Germany (2008)Google Scholar
  43. Li, X., Krause, A.: An Evolutionary Multi-Objective Optimization of Trading Rules in Call Markets. Intelligent Systems in Accounting, pp. 1–14. Wiley, New York (2011)Google Scholar
  44. Liu, F., Tang, R., Song, Y.: Information fusion oriented fuzzy comprehensive evaluation model on enterprises’ internal control enviroment. In: Proceedings of the Asia-Pacific Conference on Information (APCIP’09), vol. 1, pp. 32–34 (2009)Google Scholar
  45. Marghescu, D., Sarlin, P., Liu, S.: Early-Warning Analysis for Currency Crises in Emerging Markets: A Revisit with Fuzzy Clustering. Intelligent Systems in Accounting, pp. 143–165. Wiley, New York (2012)Google Scholar
  46. Matt, P., Toni, F., Vaccari, J.: Dominant decisions by argumentation agents. In: Proceedings of the Argumentation inMulti-Agent Systems, Lecture Notes in Computer Science (ARGMAS’10), vol. 6057, pp. 42–59. Springer, Berlin, Germany (2010)Google Scholar
  47. Medellin-Gasque, R., Atkinson, K., Bech-Capon, T., McBurney, P.: Strategies for question selection in argumentative dialogues about plans. Argument Comput. 4(2), 151–179 (2013)Google Scholar
  48. Meservy, R.: Auditing Internal Controls: A Computational Model of the Review Process (Expert Systems, Cognitive, Knowledge Acquisition, Validation, Simulation), Ohio State University (1986)Google Scholar
  49. Moraitis, P., Spanoudakis, N.: Argumentation-based agent interaction in an ambient-intelligence context. IEEE Intell. Syst. 22(6), 84–93 (2007)CrossRefGoogle Scholar
  50. Morge, M., Mancarella, P.: The hedgehog and the fox. An argumentation-based decision support system. In: Proceedings of the Argumentation in Multi-Agent Systems, Fourth International Workshop, Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’07), vol. 4946, pp. 114–131. Springer, Berlin, Germany (2007)Google Scholar
  51. Morge, M., Mancarella, P.: Assumption-based argumentation for the minimal concession strategy. In: Proceedings of the Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ARGMAS’10), vol. 6057, pp. 114–133. Springer, Berlin, Germany (2010)Google Scholar
  52. Neri, F.: Agent-based modeling under partial and full knowledge learning settings to simulate financial markets. AI Commun. 25(4), 295–304 (2012)Google Scholar
  53. O’Callaghan, S.: An artificial intelligence application of backpropagation neural networks to simulate accountants’ assessments of internal control systems using COSO guidelines. Doctoral dissertation, University of Cincinnati (1994)Google Scholar
  54. Ontañon, S., Plaza, E.: Arguments and counterexamples in case-based joint deliberation. In: Proceedings of the 3rd International Workshop, Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’07), vol. 4766, pp. 36–53. Springer, Berlin, Germany (2007)Google Scholar
  55. Parsons, S., Sklar, E.: How agents alter their beliefs after an argumentation-based dialogue. In: Proceedings of the Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’06), vol. 4049, pp. 297–312. Springer, Berlin, Germany (2006)Google Scholar
  56. Parunak, H.V.D., Odell, J.: Representing social structures in UML. Agent-Oriented Softw. Eng. II 2222, 1–16 (2002)CrossRefGoogle Scholar
  57. Peat, M., Jones, M.: Using Neural Nets to Combine Information Sets in Corporate Bankruptcy Prediction. Intelligent Systems in Accounting, pp. 90–101. Wiley, New York (2012)Google Scholar
  58. Rahwan, I., Simari, G.R.: Argumentation in Artificial Intelligence. Springer, New York (2009)Google Scholar
  59. Razavi, R., Perrot, J., Guelfi, N.: Adaptive modeling: an approach and a method for implementing adaptive agents. In: Massively Multi-Agent Systems I, Lecture Notes in Computer Science, vol. 1, pp. 136–148 (2005)Google Scholar
  60. Reed, C.: Dialogue frames in agent communication. In: Proceedings of the 3rd International Conference on Multiagent Systems (ICMAS’98), pp. 246–253 (1998)Google Scholar
  61. Rodriguez, S., De Paz, Y., Bajo, J., Corchado, J.M.: Socialbased planning model for multiagent systems. Expert Syst. Appl. 38(10), 13005–13023 (2011)CrossRefGoogle Scholar
  62. Samakovitis, G., Kapetanakis, S.: Computer-aided financial fraud detection: promise and applicability in monitoring financial transaction fraud. In: Proceedings of International Conference on Business Management & IS, vol. 2, no. 1 (2013)Google Scholar
  63. Sarkar, S., Sriram, R.S., Joykutty, S.: Belief networks for expert system development in auditing. Int. J. Intell. Syst. Account. Fin. Manage. 5(3), 147–163 (1998)CrossRefGoogle Scholar
  64. Sarlin, P., Marghescu, D.: Visual Predictions of Currency Crises using Self-Organizing Maps. Intelligent Systems in Accounting, pp 15–38. Wiley, New York (2011)Google Scholar
  65. Srivastava, R.P., Dutta, S.K., Johns, R.W.: An expert system approach to audit planning and evaluation in the belief function framework. Int. J. Intell. Syst. Account. Fin. Manage. 5, 165–184 (1998)CrossRefGoogle Scholar
  66. Tang, Y., Parsons, S.: Argumentation-based multi-agent dialogues for deliberation. In: Proceedings of the Argumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ArgMAS’06), vol. 4049, pp. 229–244. Springer, Berlin, Germany (2006)Google Scholar
  67. Thakur, J.: Role of artificial intelligence and expert system in: business competitiveness. Gian Jyoti E-J. 1(2) (2012)Google Scholar
  68. Thimm, M.: Realizing argumentation in multi-agent systems using defeasible logic programming. In: Proceedings of theArgumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ARGMAS’09), vol. 6057, pp. 175–194. Springer, Berlin, Germany (2009)Google Scholar
  69. Vaez, S., Baghi, M., Shiralizadeh, M., Farzadi, S.: Prediction the relation between audit fee and financial variables by using of artificial neural networks. Int. Res. J. Fin. Econ. 107, 17 (2013)Google Scholar
  70. Walton, D.N., Krabbe, C.W.: Commitment inDialogue: Basic Concepts of Interpersonal Reasoning. Suny Press, Albany (1995)Google Scholar
  71. Wardeh, M., Bech-Capon, T., Coenen, F.: Multi-party argument from experience. In: Proceedings of theArgumentation in Multi-Agent Systems, Lecture Notes in Computer Science (ARGMAS’10), vol. 6057, pp. 216–235. Springer, Berlin, Germany (2010)Google Scholar
  72. Welch, O.J., Reeves, T.E., Welch, S.T.: Using a genetic algorithm-based classifier system for modeling auditor decision behaviour in a fraud setting. Int. J. Intell. Syst. Account. Fin. Manage. 7(3), 173–186 (1998)CrossRefGoogle Scholar
  73. Weyns, D., Schelfthout, K., Holvoet, T., Glorieux, O.: Towards adaptive role selection for behavior-based agents. Lecture Notes in Computer Science, vol. 3394, pp. 295–312 (2005)Google Scholar
  74. Zambonelli, F., Jennings, N.R., Wooldridge, M.: Developing multiagent systems: the Gaia methodology. ACM Trans. Softw. Eng. Methodol. 12(3), 317–370 (2003)CrossRefGoogle Scholar
  75. Zeng, Z., Zhang, H., Zhang, R., Xing, Y.: Combination algorithm of probabilistic argumentation systems based on evidence theory. J. Mod. Internet Things 2(1), 13–18 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Global ProcurementNokiaMadridSpain
  2. 2.Statistics DepartmentUniversity of SalamancaSalamancaSpain
  3. 3.Computer Science DepartmentUniversity of SalamancaSalamancaSpain

Personalised recommendations