Research Article
Predicting Credit Card Defaults with Machine Learning Algorithm Using Customer Database
@INPROCEEDINGS{10.1007/978-3-031-35078-8_22, author={Anushka and Nidhi Agarwal and Devendra K. Tayal and Vrinda Abrol and Deepakshi and Yashica Garg and Anjali Jha}, title={Predicting Credit Card Defaults with Machine Learning Algorithm Using Customer Database}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Credit Card Defaulter Taiwanese Bank Decision Tree Random Forest Regressor Prediction Model Credit Cards Data Model}, doi={10.1007/978-3-031-35078-8_22} }- Anushka
Nidhi Agarwal
Devendra K. Tayal
Vrinda Abrol
Deepakshi
Yashica Garg
Anjali Jha
Year: 2023
Predicting Credit Card Defaults with Machine Learning Algorithm Using Customer Database
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_22
Abstract
In the banking sector, credit risk is a significant factor. Banking’s main activities include granting loans, credit cards, investments, mortgages, etc. Credit cards are one of the fastest growing financial services offered by banks in recent years. However, as the number of credit card users increases, banks are facing rising credit card failure rates. Therefore, data analytics can provide solutions to address current phenomena and manage credit risk. This document provides a performance evaluation of credit card default prediction. In this work, a prediction model for credit card defaulters was developed utilising a variety of unconnected decision trees. It helps speculate if someone might be a defaulter and helps the bank decide the credit limit for customers.
