Identification of Default Payments of Credit Card Clients using Boosting Techniques
S. Sathya Bama1, A. Maheshwaran2, S. Kishore Kumar3, K. RaghulKumar4, M. Yogeshwaran5
1S. SathyaBama, Assistant professor at Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
2A. Maheshwaran, Department of Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
3S. KishoreKumar, Department of Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
4K. RaghulKumar, Department of Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
5M. Yogeshwaran, Department of Computer Science & Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
Manuscript received on February 27, 2020. | Revised Manuscript received on March 14, 2020. | Manuscript published on March 30, 2020. | PP: 4990-4994 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8897038620/2020©BEIESP | DOI: 10.35940/ijrte.F8897.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Customers, Classification Techniques, Credit Card, Ensemble methods.
Scope of the Article: Classification.