An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data
| Autor: | Soner Akkoç |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Information Systems and Management
General Computer Science Neuro-fuzzy Computer science media_common.quotation_subject Credit scoring Management Science and Operations Research Machine learning computer.software_genre Logistic regression Industrial and Manufacturing Engineering Neuro fuzzy Black box ANFIS media_common OR in banking Adaptive neuro fuzzy inference system Variables Artificial neural network Artificial neural networks business.industry Linear discriminant analysis Credit card ComputingMethodologies_PATTERNRECOGNITION Modeling and Simulation Financial crisis Artificial intelligence Data mining business computer |
| Popis: | The number of Non-Performing Loans has increased in recent years, paralleling the current financial crisis, thus increasing the importance of credit scoring models. This study proposes a three stage hybrid Adaptive Neuro Fuzzy Inference System credit scoring model, which is based on statistical techniques and Neuro Fuzzy. The proposed model's performance was compared with conventional and commonly utilized models. The credit scoring models are tested using a 10-fold cross-validation process with the credit card data of an international bank operating in Turkey. Results demonstrate that the proposed model consistently performs better than the Linear Discriminant Analysis, Logistic Regression Analysis, and Artificial Neural Network (ANN) approaches, in terms of average correct classification rate and estimated misclassification cost. As with ANN, the proposed model has learning ability; unlike ANN, the model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in the explanation of why credit applications are rejected. © 2012 Elsevier B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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