• C.S.Abhinay
• Aravind.M
• D.S.Prathamesh
• K.Naveen Kumar
• Dr. Sheetal Kundra

The increase in digital financial transactions has led to a rise in fraudulent activities. This project explores the development of a deep learning-based model for credit card fraud detection. The proposed system integrates ensemble learning techniques such as Gradient Boosting, Random Forest, and Logistic Regression. Additionally, Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) networks were implemented to capture sequential dependencies in transaction data. Synthetic Minority Over-sampling Technique (SMOTE) and under-sampling methods were used to balance the dataset. The proposed model, trained using TensorFlow and Keras, achieves an accuracy of 99.59%, surpassing traditional fraud detection techniques. This research highlights the effectiveness of deep learning in financial security applications.

Ensemble learning ,Gradient Boosting , Random Forest ,Logistic Regression ,Bidirectional Long Short-Term Memory (BiLSTM) ,Bidirectional Gated Recurrent Unit (BiGRU)

"Identification Of Credit Card Frauds Using Machine Learning and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.h40-h46, April-2025, Available :http://www.jetir.org/papers/JETIR2504722.pdf

"Identification Of Credit Card Frauds Using Machine Learning and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. pph40-h46, April-2025, Available at : http://www.jetir.org/papers/JETIR2504722.pdf