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In banking business, loan default payments of individual customers are counted as risks that result in the loss of the business. Thus, some assessment mechanisms are needed to assess the risks of individual customers who apply for personal loan products. This paper presents an investigation of machine learning techniques to predict loan default payments based on individual customers information backgrounds. The paper emphasis on the ensemble techniques that mostly used in banking business. Besides the ensemble prediction models, the principal component analysis is also used for further investigation. The experimental results showed that all prediction models provided acceptable prediction of non-defaulting payment class, but provided unacceptable prediction of default payment class. That is because the imbalance nature of the data and the features used are not specific enough for the prediction models to classify the minor class from the major class. This paper acts as an initial study of the credit default payment analysis.
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