• SAI TEJASRI YERRAMSETTI
• Prathima Gamini
• Gayathri Devi Darapu
• Vamsi Kaladhar Pentakoti
• Vegesna Prudhvi Raju

In today’s socio-economic scenario, people rely heavily on credit cards. Moreover, credit cards are a requisite financial tool that enables its holders to make assets. It is true that credit cards, as a new method of payment, have become socially amenable to the masses. But nowadays, improvement in technology directs to growth in illegal activities. During credit card transactions many fraudsters can breach security and make fraudulent transactions to withdraw or transfer funds from one’s account or e-wallets. In this paper, three Boosting algorithms such as CatBoost, XGBoost and Stochastic gradient boosting algorithms are applied for the identification of frauds achieved using credit cards. Boosting assists to obtain an accurate result. For CatBoost algorithm, the evaluation of metric parameters namely precision, recall and confusion matrix is the best when compared to XGBoost and SGB boosting algorithms for the classification of fraudulent or non-fraudulent transactions.

Machine learning; Credit card fraudulent transactions; Boosting algorithms.

"Detection of Credit Card Fraudulent Transactions using Boosting Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 2, page no.2031-2036, February-2021, Available :http://www.jetir.org/papers/JETIR2102248.pdf

"Detection of Credit Card Fraudulent Transactions using Boosting Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 2, page no. pp2031-2036, February-2021, Available at : http://www.jetir.org/papers/JETIR2102248.pdf