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International Business Management

Year: 2020  |  Volume: 14  |  Issue: 2  |  Page No.: 61 - 64

Comparison of Data Mining Techniques for the Predictive Accuracy of Credit Risk of Leasing Customers in Sri Lanka

H.A.P.L. Perera


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.