Consumer Credit Risk Analysis using Data Mining Clustering and Business Intelligence Solutions
Subhash Babu Bathala1, Muthuluru Nagendra2
1Mr. Subhash Babu Bathala, Research Scholar, Department of Computer Science and Technology, Sri Krishnadevaraya University, Ananthapur, Andhra Pradesh, India.
2Dr. Muthuluru Nagendra, Professor, Department of Computer Science and Technology, Sri Krishnadevaraya University, Ananthapur, Andhra Pradesh, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 27, 2020. | Manuscript published on March 10, 2020. | PP: 1349-1358 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2122039520/2020©BEIES | DOI: 10.35940/ijitee.E2122.039520
Open Access | Ethics and Policies | Cite | Mendeley
© 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: In the recent years, the scale of online transaction has increased considerably. Subsequently, this has also increased the number of fraud cases, causing billions of dollars losses each year worldwide. Therefore, it has become mandatory to implement mechanisms that are able to assist in fraud detection. In this work, the use of Ensemble Genetic Algorithm is proposed to identify frauds in electronic transactions, more specifically in online credit card operations. A case study, using the dataset containing transactions made by credit cards in September 2013 by European cardholders, is used. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The presented algorithm achieves good performance in fraud detection as compared to the other machine learning algorithms. The results show that the proposed algorithm achieved good classification effectiveness in all tested instances.
Keywords: Data mining, Credit risk analysis, ROC,
Scope of the Article: Data Mining