Machine Learning Approaches to Predict Default of Credit Card Clients

Ruilin Liu

11 citations0/0 files storedModern Economy

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

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.

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References

2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

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2021Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach

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