• Dr.Sivadi Balakrishna
• P.Ganga Bhavani
• Chennakesavula Sudharani
• Vullam Tanuja
• Kunkalagunta Usharani , Annabathina Kavitha

The financial sector has been faced with a serious problem of credit card fraud that has led to huge economic losses and caused mistrust among consumers[1-8].Earlier, conventional methods of detecting fraud have proved insufficient in countering the current complex scams owing to their heavy reliance on manual review and rule-based systems[9-16].Therefore, our aim in this paper is to evaluate the extent by which various machine learning algorithms can be used to improve the accuracy and efficiency of detecting credit card frauds[18-26]. Logistic Regression, Decision Trees, Random Forests and Neural Networks were some of them[27].Several steps on data preprocessing including missing values handling, normalization and feature selection were carried out for quality purposes. To offer a complete comparison, models had been tested using performance metrics such as accuracy; precision; recall; F1-score; and ROC-AUC[28-32]. The authors found that Random Forest algorithm significantly performed better than other traditional methods[33]. Moreover, evidence presented demonstrates how different algorithms vary between trade-offs including complexity, interpretability and computational efficiency[34-41].These results highlight the potential of ML for effective fraud detection approaches this research contributes to the existing body of knowledge by presenting a detailed comparison of machine learning techniques in the context of credit card fraud detection and highlighting areas for future improvement and research[42-49]. The paper concludes with recommendations for integrating these models into real-world fraud detection systems, emphasizing the need for continuous adaptation to evolving fraud patterns[50].

Credit Card Fraud Detection, Machine Learning, Anomaly Detection, Supervised Learning, Unsupervised Learning, Neural Networks, Feature Engineering, Handling Imbalanced Data, Predictive Analytics, Real-time Detection

"CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.234-242, October-2024, Available :http://www.jetir.org/papers/JETIRGN06027.pdf

"CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp234-242, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06027.pdf