This paper presents experimental results of cluster analysis using self organising neural networks for identifying failing banks. The paper first describes major reasons and likelihoods of bank failures. Then it demonstrates an application of a self-organising neural network and presents results of the study. Findings of the paper demonstrate that a self-organising neural network is a powerful tool for identifying potentially failing banks. Finally, the paper discusses some of the limitations of cluster analysis related to understanding of the exact meaning of each cluster
This article provides evidence that machine learning methods are suitable for reliably predicting t...
Summarization: This paper presents a comprehensive review of 196 studies which employ operational re...
In this study, neural network models are introduced and employed for the classification of failed no...
This paper presents experimental results of cluster analysis using self organising neural networks f...
In this thesis, we present cluster-based classification methodology as a process of identifying bank...
Summary The current paper aims to predict bank insolvency before the bankruptcy using neural network...
We propose in this study to test the relevance of the use of an early warning system (EWS) for banki...
This Paper introduces a neural-net approach to perform discriminant analysis in business research. A...
One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This ...
Customer's Clustering is an instrument for considering the needs which were not allowed to be expres...
Bank failure prediction is an important study for regulators in the banking industry because the fai...
In this research, a neural network based clustering model is successfully applied to predict bankrup...
The study of machine learning has helped create and refine many types of predictive models. These mo...
International audienceThis research compares the accuracy of two approaches: traditional statistical...
Corporate failure is one of the most popular prediction problems because early identification of at-...
This article provides evidence that machine learning methods are suitable for reliably predicting t...
Summarization: This paper presents a comprehensive review of 196 studies which employ operational re...
In this study, neural network models are introduced and employed for the classification of failed no...
This paper presents experimental results of cluster analysis using self organising neural networks f...
In this thesis, we present cluster-based classification methodology as a process of identifying bank...
Summary The current paper aims to predict bank insolvency before the bankruptcy using neural network...
We propose in this study to test the relevance of the use of an early warning system (EWS) for banki...
This Paper introduces a neural-net approach to perform discriminant analysis in business research. A...
One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This ...
Customer's Clustering is an instrument for considering the needs which were not allowed to be expres...
Bank failure prediction is an important study for regulators in the banking industry because the fai...
In this research, a neural network based clustering model is successfully applied to predict bankrup...
The study of machine learning has helped create and refine many types of predictive models. These mo...
International audienceThis research compares the accuracy of two approaches: traditional statistical...
Corporate failure is one of the most popular prediction problems because early identification of at-...
This article provides evidence that machine learning methods are suitable for reliably predicting t...
Summarization: This paper presents a comprehensive review of 196 studies which employ operational re...
In this study, neural network models are introduced and employed for the classification of failed no...