Machine Learning Approaches to Predict Default of Credit Card Clients - Modern Economy

ME > Vol.9 No.11, November 2018

Machine Learning Approaches to Predict Default of Credit Card Clients ()

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DOI: 10.4236/me.2018.911115 1,321 Downloads 4,952 Views Citations

Author(s)

Ruilin Liu

Affiliation(s)

University of Southern California, Los Angeles, CA, USA.

ABSTRACT

This paper compares traditional machine learning models, i.e. Support Vector Machine, k-Nearest Neighbors, Decision Tree and Random Forest, with Feedforward Neural Network and Long Short-Term Memory. We observe that the two neural networks achieve higher accuracies than traditional models. This paper also tries to figure out whether dropout can improve accuracy of neural networks. We observe that for Feedforward Neural Network, applying dropout can lead to better performances in certain cases but worse performances in others. The influence of dropout on LSTM models is small. Therefore, using dropout does not guarantee higher accuracy.

KEYWORDS

Machine Learning, Feedforward Neural Network, Long Short-Term Memory, Dropout

Share and Cite:

Liu, R. (2018) Machine Learning Approaches to Predict Default of Credit Card Clients. Modern Economy, 9, 1828-1838. doi: 10.4236/me.2018.911115.

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