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Credit_card_fraud_detection_using_Random.pdf - Jonnalagadda...

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Jonnalagadda Vaishnave et al.; International Journal of Advance Research, Ideas and Innovations in Technology© 2019, All Rights Reserved Page |1797 ISSN: 2454-132X Impact factor: 4.295 (Volume 5, Issue 2) Available online at:Credit card fraud detection using Random Forest Algorithm Vaishnave Jonnalagadda [email protected] SRM Institute of Science and Technology, Chennai, Tamil NaduPriya Gupta [email protected] SRM Institute of Science and Technology, Chennai, Tamil NaduEesita Sen [email protected] SRM Institute of Science and Technology, Chennai, Tamil NaduABSTRACT This Project is focused on credit card fraud detection in real-world scenarios. Nowadays credit card frauds are drastically increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed project we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions. To come up with the solution one can make use of technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology, it becomes feasible to automate this process and to save some of the intensive amounts of labor that is put into detecting credit card fraud. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a random forest algorithm and decision trees. Using this feasible algorithm, we can analyze the larger data-set and user provided current data-set. Then augment the accuracy of the result data. Proceeded with the application of processing of some of the attributes provided which can find affected fraud detection in viewing the graphical model of data visualization. The performance of the techniques is gauged based on accuracy, sensitivity, and specificity, precision. The results is indicated concerning the best accuracy for Random Forest are unit 98.6% respectively. KeywordsRandom forest algorithm, Criminal transactions, Credit card1. INTRODUCTION Nowadays Credit card usage has been drastically increased across the world, now people believe in going cashless and are completely dependent on online transactions. The credit card has made the digital transaction easier and more accessible. A huge number of dollars of loss are caused every year by the criminal credit card transactions. Fraud is as old as mankind itself and can take an unlimited variety of different forms. The PwC global economic crime survey of 2017 suggests that approximately 48% of organizations experienced economic crime. Therefore, there’s positively a necessity to unravel the matter of credit card

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Term
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Tags
Machine Learning, Credit card, Statistical classification

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