A Review on Credit Card Default Modelling using Data Science

Ayush Jyala; Dishank Bhandari; Harsh Nautiyal

Title:
A Review on Credit Card Default Modelling using Data Science

Authors:
Harsh Nautiyal | Ayush Jyala | Dishank Bhandari

Cite This Article :

Harsh Nautiyal | Ayush Jyala | Dishank Bhandari "A Review on Credit Card Default Modelling using Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advances in Engineering, Science and Technology – 2021, May 2021, pp.22-28, URL: https://www.ijtsrd.com/papers/ijtsrd42461.pdf

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Abstract :
In the last few years, credit card issuers have become one of the major consumer lending products in the U.S. as well as several other developed nations of the world, representing roughly 30% of total consumer lending (USD 3.6 tn in 2016). Credit cards issued by banks hold the majority of the market share with approximately 70% of the total outstanding balance. Bank’s credit card charge offs have stabilized after the financial crisis to around 3% of the outstanding total balance. However, there are still differences in the credit card charge off levels between different competitors.

Keywords :
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Publication Details:

Unique Identification Number : IJTSRD42461

Published In : Special Issue | International Conference on Advances in Engineering, Science and Technology – 2021, May 2021

Page Number(s) : 22-28

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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