U4TY
Thành viên tích cực
Authors: KHASHMAN, ADNAN
Source: International Journal of Neural Systems; Aug2009, Vol. 19 Issue 4, p285-294, 10p, 1 Diagram, 6 Charts, 2 Graphs
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. [ABSTRACT FROM AUTHOR]
Source: International Journal of Neural Systems; Aug2009, Vol. 19 Issue 4, p285-294, 10p, 1 Diagram, 6 Charts, 2 Graphs
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. [ABSTRACT FROM AUTHOR]
