Performance assessment of data mining methods for loan granting decisions: a preliminary study

Authors:
Jozef Zurada;Niki Kunene
Affiliations:
Department of Computer Information Systems, College of Business, University of Louisville, Louisville, KY;Department of Computer Information Systems, College of Business, University of Louisville, Louisville, KY
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

Citing 4
Cited 0

Neural network credit scoring models

Computers and Operations Research - Neural networks in business
Feature Selection for Financial Credit-Risk Evaluation Decisions

INFORMS Journal on Computing
Data Mining

Data Mining
Could Decision Trees Improve the Classification Accuracy and Interpretability of Loan Granting Decisions?

HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

After the greatest financial debacle since the great depression, the need for accurate and systematic assessment of loan granting decisions has never been more important than now. The paper compares the classification accuracy rates of six models: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), k-Nearest Neighbor (kNN), and decision tree (DT) for loan granting decisions. We build models and test their classification accuracy rates on five very versatile data sets drawn from different loan granting decision contexts. The results from computer simulation constitute a fertile ground for interpretation.