• Kunal Inglunkar
• Shubham Bhagwat
• Vedant Deshpande

The rapid growth in E-Commerce industry has led to an exponential increase in the use of credit cards for online purchases and consequently they have been surging in the fraud related to it. Nowadays, the development of technology is rapidly increasing, including the credit card fraud. The credit card fraud (CCF) is one of the problems our banking system is facing today. Fraudsters used many methods to attack the customer. The growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Conventional method of identification based on possession of pin and password are not all together reliable. Higher acceptability and convenience of credit card for purchases have not only given personal comfort to customers but also attracted a large number of attackers. In recent years, for banks has become very difficult for detecting the fraud in credit card system. Machine learning plays a vital role for detecting the credit card fraud in the transactions. For predicting these transactions banks make use of various machine learning methodologies, past data has been collected and new features are been used for enhancing the predictive power. The performance of fraud detecting in credit card transactions is greatly affected by the sampling approach on data-set, selection of variables and detection techniques used. The three techniques are applied for the dataset and work is implemented in python language. The performance of the techniques is evaluated for different variables based on sensitivity, specificity, accuracy and error rate. The comparative results will show which Methodologies are efficient.

"Credit Card Fraud Detection using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 11, page no.d66-d69, November-2021, Available :http://www.jetir.org/papers/JETIR2111310.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.8, Issue 11, page no. ppd66-d69, November-2021, Available at : http://www.jetir.org/papers/JETIR2111310.pdf