Credit Card Default Prediction using Machine Learning Techniques

Yashna Sayjadah · Mohamed Hashem · Faiz Abdullah Alotaibi · Khairl Azhar Kasmiran

42 citations0/0 files storedInternational Conference on Advances in Computing, Communication & Automation (ICACCA)

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

Abstract

Credit risk plays a major role in the banking industry business. Banks' main activities involve granting loan, credit card, investment, mortgage, and others. Credit card has been one of the most booming financial services by banks over the past years. However, with the growing number of credit card users, banks have been facing an escalating credit card default rate. As such data analytics can provide solutions to tackle the current phenomenon and management credit risks. This paper provides a performance evaluation of credit card default prediction. Thus, logistic regression, rpart decision tree, and random forest are used to test the variable in predicting credit default and random forest proved to have the higher accuracy and area under the curve. This result shows that random forest best describe which factors should be considered with an accuracy of 82 % and an Area under Curve of 77 % when assessing the credit risk of credit card customers.

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References

2017A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring 2014Defining architecture components of the Big Data Ecosystem 2011Using data mining to improve assessment of credit worthiness via credit scoring models 2009Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey 2013Forecasting and stress testing credit card default using dynamic models

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2023Predicting Credit Card Defaults with Machine Learning Algorithm Using Customer Database 2024Credit card default prediction using ML and DL techniques 2024Interpretable Machine Learning Models for Credit Risk Assessment

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