Open AccessIdentification of Default Payments of Credit Card Clients using Boosting Techniques
Author(s) -
S. Sathya Bama*,
A. K. Maheshwaran,
S. Kishore Kumar,
Kamal Kumar,
M. Yogeshwaran
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8897.038620
Subject(s) - computer science , credit card , loan , boosting (machine learning) , payment , probability of default , categorization , identification (biology) , credit rating , gradient boosting , credit analysis , credit risk , machine learning , process (computing) , artificial intelligence , finance , credit reference , business , random forest , world wide web , botany , biology , operating system
Understanding the history of clients will act as a valuable screening method for banks by providing information that can categorize clients as defaulters on a loan. Customer credit rating is a grade process where the consumer is categorized by the grade. Credit scoring model used to ascertain credit risk from new and existing customer. Credit rating is an assessment used to measure the creditworthiness of the customer. For the huge customers related dataset we can use various classification techniques used in the field of data mining. The main idea is by analyzing the customer data and by combining machine-learning algorithm to identify the default credit card user. Default is a keyword, used for predicting the customer who cant repay the amount on time. Predicting future credit default accounts in advance is highly tedious task. Modern statistical techniques are usually unable to manage huge data. The proposed work focus mainly on ensemble learning and other artificial intelligence technique.