Credit card default prediction using ML and DL techniques

Fazal Wahab · Imran Khan · Sneha Sabada

14 citations0/0 files storedInternet of Things and Cyber-Physical Systems

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

Abstract

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost . The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %.

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References

2020Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods 2019Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan 2021Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach 2018Predicting credit card delinquencies: An application of deep neural networks 2018Credit Card Default Prediction using Machine Learning Techniques 2021Research on Default Prediction for Credit Card Users Based on XGBoost-LSTM Model 2021Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network 2019Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

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