A Comparative Study of Classification Techniques for Fraud Detection

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Er. Monika, Er. Amarpreet Kaur


There is large volume of data generated each day and the handling such large volume of data is very cumbersome. The generated data is stored in huge databases which can be retrieved as per the user. There are large sized repositories and databases generated in which the data can be stored. However, the retrieval of important data from such large databases is a major concern. There are numerous tools presented which can help in extracting useful information from the databases as per the requirement of users. The mechanism through which the data can be stored and extracted efficiently as per the requirement is known as data mining. This review paper studied about the classification techniques on the basis of different types of algorithms like Decision tree, Na�ve bayes, Rule based, K-NN(K Nearest Neighbour), Artificial Neural Network. It describe the uses of various classification algorithm for develop a predictive model which is useful in different fields like Software fault prediction , credit card fraud analytics, and intrusion detection, medical and so on with respect to accuracy during the past few years.

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How to Cite
, E. M. E. A. K. (2018). A Comparative Study of Classification Techniques for Fraud Detection. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(5), 19–23. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1638