• Paila Sai Vinay
• Kocherlla Santosh Bhardwaj
• Mogalipuri Jatin Kumar
• Kommula Ashish
• Mrs.Y.Uma Sudha

Credit card fraud is a growing concern in digital transactions, leading to financial losses and security breaches. Traditional fraud detection systems struggle to keep up with evolving fraudulent activities. This project proposes a hybrid machine learning approach that integrates supervised and unsupervised learning techniques to enhance fraud detection accuracy. By an analyzing real-time transaction data and user behavior patterns, the system employs an ensemble of decision trees, neural networks, and anomaly detection algorithms to detect fraudulent activities while minimizing false positives.

Credit card fraud detection, hybrid machine learning, supervised learning, unsupervised learning, anomaly det ection, real-time payment processing, decision trees, neural networks, ensemble learning, feature engineering, data preprocessing, fraud pattern recognition.

"HYBRID MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION IN REAL TIME PAYMENT PROCESSING SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.f121-f125, March-2025, Available :http://www.jetir.org/papers/JETIR2503540.pdf

"HYBRID MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION IN REAL TIME PAYMENT PROCESSING SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppf121-f125, March-2025, Available at : http://www.jetir.org/papers/JETIR2503540.pdf