Intelligent Financial Warning Model Using Fuzzy Neural Network and Case-Based Reasoning

Publication Type:
Conference Proceeding
IEEE Symposium on Computational Intelligence for Financial Engineering & Economics, 2011, pp. 9 - 15
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2010001759OK.pdf1.3 MBAdobe PDF
Creating an applicable and precise financial early warning model is highly desirable for decision makers and regulators in the financial industry. Although Business Failure Prediction (BFP) especially banks has been extensively a researched area since late 1960s, the next critical step which is the decision making support scheme has been ignored. This paper presents a novel model for financial warning which combines a fuzzy inference system with the learning ability of neural network as a Fuzzy Neural Network (FNN) to predict organizational financial status and also applies reasoning capability of Fuzzy Case-Based Reasoning (FCBR) to support decision makers measuring appropriate solutions. The proposed financial warning model generates an adaptive fuzzy rule base to predict financial status of target case and then if it is predicted to fail, the FCBR is used to find similar survived cases. Finally according similar cases and a fuzzy rule base, the model provides financial decisions to change particular features as company goals in upcoming year to avoid future financial distress.
Please use this identifier to cite or link to this item: