An application of element oriented analysis based credit scoring

Authors:
Yihao Zhang;Mehmet A. Orgun;Rohan Baxter;Weiqiang Lin
Affiliations:
Department of Computing, Macquarie University Sydney, NSW, Australia;Department of Computing, Macquarie University Sydney, NSW, Australia;Australian Taxation Office, Canberra, ACT, Australia;Australian Taxation Office, Canberra, ACT, Australia
Venue:
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Year:
2010

Citing 4
Cited 0

Neural network credit scoring models

Computers and Operations Research - Neural networks in business
Credit rating analysis with support vector machines and neural networks: a market comparative study

Decision Support Systems - Special issue: Data mining for financial decision making
Mining Multidimensional Data through Element Oriented Analysis

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Measuring classifier performance: a coherent alternative to the area under the ROC curve

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an application of an Element Oriented Analysis (EOA) credit scoring model used as a classifier for assessing the bad risk records. The model building methodology we used is the Element Oriented Analysis. The objectives in this study are: 1) to develop a stratified model based on EOA to classify the risk for the Brazilian credit card data; 2) to investigate if this model is a satisfactory classifier for this application; 3) to compare the characteristics of our model to the conventional credit scoring models in this specific domain. Classifier performance is measured using the Area under Receiver Operating Characteristic curve (AUC) and overall error rate in out-of-sample tests.