a Record control number 000269527600005

a ISSN 0129-0657

a Original cataloging agency NEU GrandLibrary

a Language code of text/sound track or separate title eng

a Classification number QA76

b Item number .N48 2009

a Personal name Khashman, Adnan,

h Medium Electronic Document

a Title A Neural Network Model For Credit Risk Evaluation.

c Statement of responsibility, etc Adnan Khashman.

c Date of publication, distribution, etc 2009.

a Place of publication, distribution, etc Singapore :

b Name of publisher, distributor, etc World Scientific Publ Co,

a Summary, etc 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.

a Topical term or geographic name as entry element Computer science

a Topical term or geographic name as entry element Near East University Article

a Topical term or geographic name as entry element Yakın Doğu Üniversitesi Makale

a Topical term or geographic name as entry element Artificial intelligence

a Topical term or geographic name as entry element Credit scoring and evaluation

a Topical term or geographic name as entry element Personal bank loan

a Main entry heading Journal Of Information science and Engineering

g Relationship information AUG 2009, Vol 19 Issue 4, p285-294

x International Standard Serial Number 01290657