title: A neural network model for credit risk evaluation.
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.
authors.affiliation: Intelligent Systems Research Group, Near East University, Lefkosa, Mersin 10, Turkey. khashman@ieee.org
authors.forename: Adnan
authors.initials: A
authors.lastname: Khashman
mesh_terms: D000465:Algorithms; D005376:Financial Management; D006801:Humans; D016247:Information Storage and Retrieval; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012309:Risk-Taking