Khashman Adnan
Intelligent Systems Research Group, Near East University, Lefkosa, Mersin 10, Turkey.
Int J Neural Syst. 2009 Aug;19(4):285-94. doi: 10.1142/S0129065709002014.
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.
信用评分是信用风险评估中的关键分析技术之一，而信用风险评估一直是金融风险管理领域的一个活跃研究方向。本文提出了一种信用风险评估系统，该系统使用基于反向传播学习算法的神经网络模型。我们使用七种学习方案和来自澳大利亚信用审批数据集的真实世界信用申请来训练和实现神经网络，以决定是否批准或拒绝信用申请。文中提供了不同学习方案下系统性能的比较，此外，我们还比较了两个神经网络的性能；一个具有一层隐藏层，另一个具有两层隐藏层，均遵循理想学习方案。实验结果表明，神经网络可有效地用于信用申请的自动处理。
