Machine Learning Methods for Analysis Fraud Credit Card Transaction
Megasari Gusandra Saragih1, Jacky Chin, Rianti Setyawasih2, Phong Thanh Nguyen3, K. Shankar4
1Megasari Gusandra Saragih, Universitas Pembangunan Panca Budi, Medan, Indonesia.
2Jacky Chin, Mercu Buana University, Indonesia. Rianti Setyawasih, Universitas Islam Bekasi, Indonesia.
3Phong Thanh Nguyen, Department of Project Management, Ho Chi Minh City Open University, Vietnam.
4K.Shankar, Department of Computer Applications, Alagappa University, India.
Manuscript received on 18 August 2019 | Revised Manuscript received on 29 August 2019 | Manuscript Published on 06 September 2019 | PP: 870-874 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11640886S19/19©BEIESP | DOI: 10.35940/ijeat.F1164.0886S19
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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: The use of online banking and credit card is increasing day by day. As the usage of credit/debit card or netbanking is increasing, the possibility of many fraud activities is also increasing. There are many incidents are happened in presently where because of lack of knowledge the credit card users are sharing their personal details, card details and one time password to a unknown fake call. And the result will be fraud happened with the account. Fraud is the problem that it is very difficult to trace the fraud person if he made call from a fake identity sim or call made by some internet services. So in this research some supervised methodologies and algorithms are used to detect fraud which gives approximate accurate results. The illegal or fraud activities put very negative impact on the business and customers loose trust on the company. It also affects the revenue and turnover of the company. In this research isolation forest algorithm is applied for classification to detect the fraud activities and the data sets are collected from the professional survey organizations.
Keywords: Credit Debit Card Fraud Detection, Machine Learning Algorithms, Forest Algorithm, Classification Algorithm.
Scope of the Article: Machine Learning