• Sheetal
• K.L Bansal

Now-a-Days credit card fraud is growing significantly with the development of new technology. Because of super highways of communication. Credit card costs consumers, financial institution and banks billions of bucks each year. Because credit card becomes the most popular mode of payment for both online and offline. Fraudster continuously tries to commit illegal actions. Therefore, fraud detection systems have become necessary for banks and financial institution to reduce their losses. The most commonly practiced techniques for credit card fraud detection are such as Hidden Markov Model, Genetic algorithm, Naïve Bayes, Neural Network etc. The aim of this paper is to offer a relative survey of different data mining tools and techniques to find out the best technique for credit card fraud detection. First part of the paper introduces the credit card fraud detection and their types. Next part discusses literature survey. The third part talks about the data mining techniques and their advantages and disadvantages and then the last part is the comparison and their conclusion. The Result of this comparison indicates that the classification technique and ensemble classification technique give the best performance with high accuracy rate.

Data Mining, Credit card Fraud, Types of fraud, Classification.

"Review of credit card fraud detection using data mining techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.533-540, December-2018, Available :http://www.jetir.org/papers/JETIR1812170.pdf

"Review of credit card fraud detection using data mining techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp533-540, December-2018, Available at : http://www.jetir.org/papers/JETIR1812170.pdf