A Hybrid Model On Data Clustering and Computational Intelligence for Bank Crisis Classification and Prediction
The prediction of bank failures is an important academic topic of which many have used artificial intelligence methods to build an early warning system for this purpose. The objective of this paper is to enhance the accuracy in predicting bank failures by proposing two hybrid models. In this paper, two hybrid models are developed by integrating a K-means cluster method to integrate K-means with a Back-Propagation Neural Network (BPN) and a Support Vector Machines (SVM) technique for financial data classification. Datasets from the website of Federal Reserve Bank of Chicago are employed for benchmark test. Initially a K-mean clustering method is applied to preprocess the dataset thus a more homogeneous data within each cluster will be attainted.