HYBRID NEURAL INTELLIGENT SYSTEM TO PREDICT BUSINESS FAILURE IN SMALL-TO-MEDIUM-SIZE ENTERPRISES

Lourdes Borrajo · Bruno Baruque · Emilio Corchado · Javier Bajo · Juan M. Corchado

77 citations0/0 files storedInternational Journal of Neural Systems

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Financial Distress and Bankruptcy Prediction Neural Networks and Applications Forecasting Techniques and Applications

Abstract

During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

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References

1985Availability and the Generation of Hypotheses in Analytical Review 1997Scheduling/Cost Optimization and Neural Dynamics Model for Construction 2006Data mining method for listed companies’ financial distress prediction 2001Resource Scheduling Using Neural Dynamics Model of Adeli and Park 2008Ranking-order case-based reasoning for financial distress prediction 2007Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers 2007Financial distress early warning based on group decision making 2008Gaussian case-based reasoning for business failure prediction with empirical data in China

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2013Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble With Time Window in Corporate Financial Risk Prediction 2011Predicting Business Failure Using an RSF‐based Case‐Based Reasoning Ensemble Forecasting Method 2012Forecasting business failure using two‐stage ensemble of multivariate discriminant analysis and logistic regression 2021Explanatory Factors of Business Failure: Literature Review and Global Trends

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