The Effect of Best First Search Optimization on Credit Card Fraudulent Transaction Detection
Sapan Sahu1, Shikha Agrawal2, Raju Baraskar3
1Sapna Sahu, Department of Computer Science, University of Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya , Bhopal, India.
2Dr shikha agarawal, Department of Computer Science, University of Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya , Bhopal, India.
3Dr. Raju Baraskar, Department of Computer Science, University of Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya , Bhopal, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1939-1946 | Volume-8 Issue-12, October 2019. | Retrieval Number: L28941081219/2019©BEIESP | DOI: 10.35940/ijitee.L2894.1081219
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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: In the digital world, Recently growth of online shopping site for purchasing clothes, electronic items, glossary etc and online transaction for transfer money is increasing day by day . At the same time, criminals have become able to doing fault and earning money through wrong ways .that’s why fraud grows. With the development of Machine Learning in the field of Computer Science and Engineering, its application in the different domain also in fields like Medical, Marketing, Telecommunication, finance, etc. The reason for the popularity of Machine Learning in these domains is due to its high accuracy prediction. That’s why over many years, machine learning has been used in fraud detection. With the advancement of technology in online transactions, fraud is the greatest issue for businesses and has become difficult to recognize than the traditional form of this crime. Historically, the area of Fraud Detection is interrelated to Data Mining & Text Mining. Due to the sudden growth of fraud whose outcome is loss of trillions of rupees worldwide every year, various modern techniques in detecting fraud were proposed that are progressed without interruption and applied to many business fields. Bank frauds worth ₹2.05 trillion happened in the last 11 years, among which there were overall 53,334 fraud issues in the usage of RBI data. The principle purpose behind this write up is to review different methods in identifying frauds corresponding to the unusualness in the transactions. The supervised and unsupervised machine learning algorithms will be used to identify fraud and the best first search optimization will be analyzed to compare both results, i.e., before and after optimization.
Keywords: Machine Learning, Fraud Detection, Supervised Learning Algorithm, Unsupervised Algorithm, Best First Search
Scope of the Article: Machine Learning