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Abstract
Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.
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Affiliation(s)
- ADNAN KHASHMAN
- Intelligent Systems Research Group (ISRG), Near East University, Lefkosa, Mersin 10, Turkey
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A probabilistic neural network for earthquake magnitude prediction. Neural Netw 2009; 22:1018-24. [DOI: 10.1016/j.neunet.2009.05.003] [Citation(s) in RCA: 220] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2008] [Revised: 04/29/2009] [Accepted: 05/13/2009] [Indexed: 11/22/2022]
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