A multi-agent system for web-based risk management in small and medium business

Business Intelligence has gained relevance during the last years to improve business decision making. However, there is still a growing need of developing innovative tools that can help small to medium sized enterprises to predict risky situations and manage inefficient activities. This article present a multi-agent system especially created to detect risky situations and provide recommendations to the internal auditors of SMEs. The core of the multi-agent system is a type of agent with advanced capacities for reasoning to make predictions based on previous experiences. This agent type is used to implement a evaluator agent specialized in detect risky situations and an advisor agent aimed at providing decision support facilities. Both agents incorporate innovative techniques in the stages of the CBR system. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

[1]  Cynthia R. Marling,et al.  Integrations with case-based reasoning , 2005, Knowl. Eng. Rev..

[2]  B. Mallick,et al.  Bayesian Methods for DNA Microarray Data Analysis , 2005 .

[3]  Hui Li,et al.  Financial distress prediction based on serial combination of multiple classifiers , 2009, Expert Syst. Appl..

[4]  Juan Manuel Corchado Rodríguez.  Redes neuronales artificiales: un enfoque práctico , 2000 .

[5]  Chih-Hung Wu,et al.  Developing a business failure prediction model via RST, GRA and CBR , 2009, Expert Syst. Appl..

[6]  Hui Li,et al.  On performance of case-based reasoning in Chinese business failure prediction from sensitivity, specificity, positive and negative values , 2011, Appl. Soft Comput..

[7]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[8]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[9]  Grgoire Montavon,et al.  Neural Networks: Tricks of the Trade , 2012, Lecture Notes in Computer Science.

[10]  Hui Li,et al.  Financial distress early warning based on group decision making , 2009, Comput. Oper. Res..

[11]  Hui Li,et al.  Data mining method for listed companies' financial distress prediction , 2008, Knowl. Based Syst..

[12]  B Fritzke,et al.  A growing neural gas network learns topologies. G. Tesauro, DS Touretzky, and TK Leen, editors , 1995, NIPS 1995.

[13]  Winfried Lamersdorf,et al.  Jadex: Implementing a BDI-Infrastructure for JADE Agents , 2003 .

[14]  Hussein A. Abbass,et al.  A novel mixture of experts model based on cooperative coevolution , 2006, Neurocomputing.

[15]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[16]  Yongsheng Ding,et al.  Forecasting financial condition of Chinese listed companies based on support vector machine , 2008, Expert Syst. Appl..

[17]  Nick Cercone,et al.  Rule-Induction and Case-Based Reasoning: Hybrid Architectures Appear Advantageous , 1999, IEEE Trans. Knowl. Data Eng..

[18]  T. Martínez,et al.  Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps , 1993 .

[19]  David C. Yen,et al.  A hybrid financial analysis model for business failure prediction , 2008, Expert Syst. Appl..

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Hui Li,et al.  Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers , 2008, Expert Syst. Appl..

[22]  Frank Dellaert,et al.  The Expectation Maximization Algorithm , 2002 .

[23]  Juan M. Corchado,et al.  Neuro-symbolic System for Business Internal Control , 2004, ICDM.

[24]  N. Saitou,et al.  The neighbor-joining method: a new method for reconstructing phylogenetic trees. , 1987, Molecular biology and evolution.

[25]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[26]  Javier Bajo,et al.  Integrating case-based planning and RPTW neural networks to construct an intelligent environment for health care , 2009, Expert Syst. Appl..

[27]  David J. Israel,et al.  Plans and resource‐bounded practical reasoning , 1988, Comput. Intell..

[28]  N. Sundararajan,et al.  A fully complex-valued radial basis function network and its learning algorithm. , 2009 .

[29]  Xia Li,et al.  Cellular automata for simulating land use changes based on support vector machines , 2008, Comput. Geosci..

[30]  Roberto Avogadri,et al.  Fuzzy ensemble clustering based on random projections for DNA microarray data analysis , 2009, Artif. Intell. Medicine.

[31]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[32]  Bernd Fritzke,et al.  Fast learning with incremental RBF networks , 1994, Neural Processing Letters.

[33]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[34]  Stefan Lessmann,et al.  A reference model for customer-centric data mining with support vector machines , 2009, Eur. J. Oper. Res..

[35]  Gun Ho Lee.  Rule-based and case-based reasoning approach for internal audit of bank , 2008, Knowl. Based Syst..

[36]  Thomas G. Calderon,et al.  A roadmap for future neural networks research in auditing and risk assessment , 2002, Int. J. Account. Inf. Syst..

[37]  Victor R. Lesser,et al.  Design-to-time real-time scheduling , 1993, IEEE Trans. Syst. Man Cybern..

[38]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[39]  Fernando José Von Zuben,et al.  Hybridizing mixtures of experts with support vector machines: Investigation into nonlinear dynamic systems identification , 2007, Inf. Sci..

[40]  Raffaele Calogero,et al.  Kohonen neural networks and genetic classification , 2007, Math. Comput. Model..

[41]  Shuchuan Lo,et al.  Web service quality control based on text mining using support vector machine , 2008, Expert Syst. Appl..

[42]  Javier Bajo,et al.  Multi-agent neural business control system , 2010, Inf. Sci..

[43]  Juan M. Corchado,et al.  Autonomous Internal Control System for Small to Medium Firms , 2005, ICCBR.

[44]  Lucila Ohno-Machado,et al.  A neural network-based similarity index for clustering DNA microarray data , 2003, Comput. Biol. Medicine.

[45]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[46]  Adnan Khashman,et al.  A Neural Network Model for Credit Risk Evaluation , 2009, Int. J. Neural Syst..

[47]  Qing-Song Xu,et al.  Support vector machines and its applications in chemistry , 2009 .

[48]  Marc G. Genton,et al.  On the discretization of nonparametric isotropic covariogram estimators , 2004, Stat. Comput..

[49]  Juan M. Corchado,et al.  A Reasoning Model for CBR_BDI Agents Using an Adaptable Fuzzy Inference System , 2003, CAEPIA.

[50]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[51]  Hui Li,et al.  Gaussian case-based reasoning for business failure prediction with empirical data in China , 2009, Inf. Sci..

[52]  Chuang Lin,et al.  On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction , 2010, Expert Syst. Appl..

[53]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[54]  Hui Li,et al.  Ranking-order case-based reasoning for financial distress prediction , 2008, Knowl. Based Syst..

[55]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[56]  Andrew D. Bailey,et al.  Risk assessment in internal auditing: a neural network approach , 1999 .

[57]  Juan M. Corchado,et al.  Constructing deliberative agents with case‐based reasoning technology , 2003, Int. J. Intell. Syst..

[58]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[59]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[60]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[61]  Jing Lu,et al.  Creating ensembles of classifiers via fuzzy clustering and deflection , 2010, Fuzzy Sets Syst..

[62]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[63]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.