Abstract
The complexity of current organization systems and the increase in importance of the realization of internal controls in firms makes the construction of models that automate and facilitate the work of the auditors crucial. A tool for the decision support process has been developed based on a multi-cbr system that incorporates two case-based reasoning systems and automates the business control process. The objective of the system is to facilitate the process of internal audit in small and medium firms. The multi-cbr system analyses the data that characterises each one of the activities carried out by the firm, then determines the state of each activity, calculates the associated risk, detects the erroneous processes, and generates recommendations to improve these processes. Therefore, the system is a useful tool for the internal auditor in order to make decisions based on the obtained risk. Each one of the case-based reasoning systems that integrates the multi-agent system uses a different problem solving method in each step of the reasoning cycle: a Maximum Likelihood Hebbian learning-based method that automates the organization of cases and the retrieval phase, an Radial Based Function neural network and a multi-criterion discreet method during the reuse phase and a rule based system for recommendation generation. The multi-cbr system has been tested in 14 small and medium size companies during the last 26 months in the textile sector, which are located in the northwest of Spain. The achieved results have been very satisfactory
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References
Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AICOM 7(1) (1994)
American Institute of Certified Public Accountants (AICPA), Statements on Auditing Standards No. 78 (SAS No. 78). Consideraciones de la Estructura del Control Interno en una Auditoría de Estados Financieros (Amendment to SAS núm. 55), The Auditing Standards Executive Committee, New York (1996)
Barba-Romero, S., Pomeral, J.: Decisiones Multicriterio. Fundamentos teóricos y utilización práctica. Colección de Economía. Servicio de publicaciones Universidad de Alcalá (1997)
Borrajo, L.: Sistema híbrido inteligente aplicado a la auditoría de los sistemas internos. Ph.D. thesis. Universidade de Vigo (Spain) (2003) ISBN: 84-8158-274-3.
Corchado, E., MacDonald, D., Fyfe, C.: Optimal Projections of High Dimensional Data. In: ICDM 2002 the 2002 IEEE International Conference on Data Mining, Maebashi TERRSA, Maebashi City, Japan, IEEE Computer Society, Los Alamitos (2002)
Corchado, J.M., Borrajo, L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic System for Business Internal Control. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 302–9743. Springer, Heidelberg (2004)
Corchado, J.M., Díaz, F., Borrajo, L., Fdez-Riverola, F.: Redes Neuronales Artificiales: Un enfoque práctico. Departamento de publicaciones de la Universidad de Vigo (2000)
Denna, E.L., Hansen, J.V., Meservy, R.: Development and application of expert systems in audit services. Transactions on Knowledge and Data Engineering (1991)
Diaconis, P., Freedman, D.: Asymptotics of Graphical Projections. The Annals of Statistics 12(3), 793–815 (1984)
Fdez-Riverola, F., Corchado, J.M.: FSfRT: Forecasting System for Red Tides. Applied Intelligence. Special Issue on Soft Computing in Case-Based Reasoning 21(3), 251–264 (2004)
Fritzke, B.: Fast Learning with Incremental RBF Networks. Neural Processing Letters 1(1), 2–5 (1994)
Fyfe, C., Baddeley, R.: Non-linear data structure extraction using simple Hebbian networks. Biological Cybernetics 72(6), 533–541 (1995)
Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: 10th European Symposium on Artificial Neural Networks, ESANN 2002, Bruges, April 24-25-26 (2002a)
Fyfe, C., Corchado, E.: A New Neural Implementation of Exploratory Projection Pursuit. In: Yin, H., Allinson, N.M., Freeman, R., Keane, J.A., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, Springer, Heidelberg (2002)
Fyfe, C., MacDonald, D.: ε-Insensitive Hebbian learning. Neuro Computing (2001)
Hunt, J., Miles, R.: Hybrid case-based reasoning. The Knowledge Engineering Review 9(4), 383–397 (1994)
Hyvärinen, A.: Complexity Pursuit: Separating interesting components from time series. Neural Computation 13, 883–898 (2001)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Chichester (2002)
Karhunen, J., Joutsensalo, J.: Representation and Separation of Signals Using Non-linear PCA Type Learning. Neural Networks 7, 113–127 (1994)
Kashyap, R.L., Blaydon, C.C., Fu, K.S.: Stochastic Approximation. In: Mendel, J.M. (ed.) A Prelude to Neural Networks: Adaptive and Learning Systems. Prentice Hall, Englewood Cliffs (1994)
Kolodner, J.: Case-Based Reasoning, San Mateo, CA. Morgan Kaufmann, San Francisco (1993)
Lai, P.L., Charles, D., Fyfe, C.: Seeking Independence using Biologically Inspired Artificial Neural Networks, in Developments. In: Girolami, M.A. (ed.) Artificial Neural Network Theory: Independent Component Analysis and Blind Source Separation. Springer, Heidelberg (2000)
Lenz, M., Bartsch-Spörl, B., Burkhard, D., Wees, S. (eds.): Case-based Reasoning Technology: From Fundations to Applications. LNCS (LNAI), vol. 1400. Springer, Heidelberg (1998)
Mas, J., Ramió, C.: La Auditoría Operativa en la Práctica. Ed. Marcombo, Barcelona (1997)
Medsker, L.R.: Hybrid Intelligent Systems. Kluwer Academic Publishers, Dordrecht (1995)
Messier, W.F., Hansen, J.V.: Inducing rules for expert systems development: An example using default and bankruptcy data. Management Science 34(12), 1403–1415 (1988)
Oja, E.: Neural Networks, Principal Components and Subspaces. International Journal of Neural Systems 1, 61–68 (1989)
Oja, E., Ogawa, H., Wangviwattana, J.: Principal Components Analysis by Homogeneous Neural Networks, part 1, The Weighted Subspace Criterion. IEICE Transaction on Information and Systems, E75D, 366–375 (1992)
Riesbeck, C.K., Schank, R.C.: Inside case-based reasoning. Lawrence Erlbaum Associates, Hillsdale, NJ (1989)
Sánchez, A.: Los Sistemas Expertos en la Contabilidad. Biblioteca Electrónica de Contabilidad 1(2) (1995)
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 Technical Report Series (1998)
Watson, I.: Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Francisco (1997)
Watson, I., Marir, F.: Case-Based Reasoning: A Review. The Knowledge Engineering Review 9(4), 355–381 (1994)
Xu, L.: Least Mean Square Error Reconstruction for Self-Organizing Nets. Neural Networks 6, 627–648 (1993)
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Borrajo, M.L., Corchado, E.S., Pellicer, M.A., Corchado, J.M. (2008). CBR Based Engine for Business Internal Control. In: Prasad, B. (eds) Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79005-1_13
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