ISSN 2394-5125
 

Research Article 


MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS

M.Shanmuga sundari, RudraKalyan Nayak.

Abstract
Dynamic bank fraud exercises occur now and produce high loss in current market. Hackers and criminals can utilize
various advancements, for example, phishing and trojan to take the information from others' mastercards. Subsequently,
it can recognize extortion in time. One strategy is to utilize the historical information including typical exchanges and
misrepresentation ones to acquire ordinary/extortion conduct highlights dependent on data mining procedures and
afterward find the fraud and normal transactions. Anomaly detection is the best procedure material to numerous
businesses, for example, banking and money related parts, social insurance, protection, government offices. There has
been an extraordinary increment lately, pushing fraud detection more significant than any time in recent world. A huge
dollars are lost to fraud (misrepresentation) consistently. The methods for detecting fraud/ anomaly had to develop as
well as to provide more efficient protection. The most effecive and recognized technology that has been implemented for
fraud/ anomaly detection is data mining. In data mining, ranom forest(RF) and support vector machine (SVM) are most
efficient algorithms. This paper shows the accuracy comparison of fraud detection using RF and SVM techniques.

Key words: Credit Card Data, Ranking Anomaly Detection, Support Vector Machine, Random Forest Classifier, Rating and Review.


 
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Pubmed Style

M.Shanmuga sundari, RudraKalyan Nayak. MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS. JCR. 2020; 7(9): 2384-2390. doi:10.31838/jcr.07.09.387


Web Style

M.Shanmuga sundari, RudraKalyan Nayak. MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS. http://www.jcreview.com/?mno=107462 [Access: April 18, 2021]. doi:10.31838/jcr.07.09.387


AMA (American Medical Association) Style

M.Shanmuga sundari, RudraKalyan Nayak. MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS. JCR. 2020; 7(9): 2384-2390. doi:10.31838/jcr.07.09.387



Vancouver/ICMJE Style

M.Shanmuga sundari, RudraKalyan Nayak. MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS. JCR. (2020), [cited April 18, 2021]; 7(9): 2384-2390. doi:10.31838/jcr.07.09.387



Harvard Style

M.Shanmuga sundari, RudraKalyan Nayak (2020) MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS. JCR, 7 (9), 2384-2390. doi:10.31838/jcr.07.09.387



Turabian Style

M.Shanmuga sundari, RudraKalyan Nayak. 2020. MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS. Journal of Critical Reviews, 7 (9), 2384-2390. doi:10.31838/jcr.07.09.387



Chicago Style

M.Shanmuga sundari, RudraKalyan Nayak. "MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS." Journal of Critical Reviews 7 (2020), 2384-2390. doi:10.31838/jcr.07.09.387



MLA (The Modern Language Association) Style

M.Shanmuga sundari, RudraKalyan Nayak. "MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS." Journal of Critical Reviews 7.9 (2020), 2384-2390. Print. doi:10.31838/jcr.07.09.387



APA (American Psychological Association) Style

M.Shanmuga sundari, RudraKalyan Nayak (2020) MASTER CARD ANOMALY DETECTION USING RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS. Journal of Critical Reviews, 7 (9), 2384-2390. doi:10.31838/jcr.07.09.387