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CBR Based Engine for Business Internal Control

  • M. L. Borrajo
  • E. S. Corchado
  • M. A. Pellicer
  • J. M. Corchado
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 230)

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

Keywords

Radial Base Function Neural Network Independent Component Analysis Case Base Reasoning Internal Auditor Case Base Reasoning System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • M. L. Borrajo
    • 1
  • E. S. Corchado
    • 3
  • M. A. Pellicer
    • 3
  • J. M. Corchado
    • 2
  1. 1.Dept. InformáticaUniversity of Vigo, Escuela Superior de Ingeniería Informática, Edificio PolitécnicoOurenseSpain
  2. 2.Dept. de Ingeniería CivilUniversity of Burgos, Esc. Politécnica Superior, Edificio CBurgosSpain
  3. 3.Dept. de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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