• Palak Shukla
• Vijay Shankar Mishra

The proliferation of digital transactions has led to an increase in credit card fraud, a critical challenge in the financial sector that necessitates sophisticated detection mechanisms. Machine Learning (ML) and Deep Learning (DL) frameworks have emerged as powerful tools in combating such fraudulent activities. These technologies enable the identification of fraudulent transactions by learning from vast amounts of transactional data, detecting patterns and anomalies that might indicate fraud. By leveraging algorithms that can adapt and improve over time, ML and DL models provide an evolving defense against the constantly changing tactics of fraudsters. The integration of these frameworks into fraud detection systems has proven to be highly effective, significantly reducing the rate of successful frauds and securing the integrity of the credit card transaction ecosystem. This abstract encapsulates the essence of using ML and DL for credit card fraud detection, highlighting their role in enhancing security measures in the financial industry.

Machine Learning, Credit Card Fraud, Financial Implications, XGBoost, Anomaly Detection.

"Credit Card Fraud Detection using Machine Learning and Deep Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e214-e218, November-2023, Available :http://www.jetir.org/papers/JETIR2311432.pdf

"Credit Card Fraud Detection using Machine Learning and Deep Learning Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe214-e218, November-2023, Available at : http://www.jetir.org/papers/JETIR2311432.pdf