• Shubham Mungase
• Abhay Tikande
• Surendra Bora
• Pandurang Zanwar
• Apashabi Pathan

Nowaday’s online payment gaining popularity because of easy and convenience use of ecommerce. It became very easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount 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 Learninghas 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 credit card fraud data sets are highly skewed. This paper research and checks the performance of Random Forest on highly skewed credit card fraud data. Dataset 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, random forest, Machine Learning, Security.

"Credit Card Fraud Detection using Machine Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.355-360, June-2020, Available :http://www.jetir.org/papers/JETIR2006391.pdf

"Credit Card Fraud Detection using Machine Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp355-360, June-2020, Available at : http://www.jetir.org/papers/JETIR2006391.pdf