• Tejas Hande
• Prakash Navale
• Vinayak Sarode
• Sagar Bholankar
• Ankita Ghodke

Now a day’s online payment gaining popularity because of easy and convenience use of e commerce. It became very easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the results of immense quantity of e-commerce use, there is a vast increment in credit card fraud also. Machine Learning has been successfully applied to finance databases to automate analysis of huge volumes of complex data. Machine Learning has also played a salient role in the detection of credit card fraud in online transactions. Fraud detection in credit card is a big problem, it becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviors change frequently and secondly due to reason that master card fraud knowledge sets are extremely skew. This paper research and checks the performance of Random Forest on highly skewed credit card fraud data. Data set of credit card transactions is sourced from European cardholders containing 1 lakh transactions. These techniques are applied on the raw and pre processed data. The performance of the techniques is evaluated based on accuracy, sensitivity, and specificity, precision.

Data Analysis, Fraud in Credit Card, Decision Tree, Random Forest, Machine Learning, Security

"Credit Card Fraud Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 10, page no.276-279, October 2019, Available :http://www.jetir.org/papers/JETIR1907L91.pdf

"Credit Card Fraud Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 10, page no. pp276-279, October 2019, Available at : http://www.jetir.org/papers/JETIR1907L91.pdf