TY  - JOUR
T1  - Comparison of Data Mining Techniques for the Predictive Accuracy of Credit Risk of Leasing
Customers in Sri Lanka
AU - Perera, H.A.P.L. 
JO  - International Business Management
VL  - 14
IS  - 2
SP  - 61
EP  - 64
PY  - 2020
DA  - 2001/08/19
SN  - 1993-5250
DO  - ibm.2020.61.64
UR  - https://makhillpublications.co/view-article.php?doi=ibm.2020.61.64
KW  - Data mining
KW  -credit risk
KW  -logistic regression
KW  -Naive Bayes
KW  -decision tree
KW  -neural networks
AB  - This research has been conducted to construct
a model which can be used to measure the predictive
accuracy of the credit risk of leasing customers in Sri
Lanka and to compare different data mining techniques
for the purpose of selecting the best and adequate
technique to predict the credit risk. The dataset employed
in this study was obtained from one of the leading
finance/leasing companies in Sri Lanka. Altogether 8235
customers/data instances have been considered for the
analysis under 24 variables. The data set was divided in to
two different datasets, training (60%) and test (40%). The
Waikato environment for knowledge analysis machine
learning software was the major software tool used for the
entire model construction process. Four main data mining
techniques (logistic regression, Naive Bayes, decision
tree-J 48 and neural networks) were used to construct
models and results from each model were obtained and
compared with other techniques. According to the results
of the study, we can conclude that, a model constructed
using the neural network as the best model to predict the
payment accuracy of leasing customers in Sri Lanka.
ER  -