• Satheeshkumar S
• Haritha R
• Karthika B
• Madhu Prabha M
• Priyadarshini S

Traditional fraud detection techniques, which are often manual, are inefficient, time-consuming, and costly. As a result, methods that use AI and ML have been implemented to improve fraud detection procedures. Despite attempts to reduce it, financial fraud continues to be a major problem in many industries, including healthcare, banking, and insurance. As a result, methods that use AI and ML have been implemented to improve fraud detection procedures. This study examines the application of ML algorithms for credit card fraud detection using a dataset consisting of 284,807 transactions made by European cardholders in 2013, out of which 492 were fraudulent. Preprocessing steps, including Label Encoding, SMOTE for handling class imbalance, and PCA for feature reduction, were applied to the dataset. On the training dataset have applied ML based classification models like DT, SVM, and ANNs were employed to evaluate their performance. The models were assessed using accuracy, precision, and recall as key metrics. The ANN model emerged as the best-performing model, achieving 98.41%precision, 98.69%accuracy, and 98.98%recall, outperforming both Decision Trees and SVM. This study highlights the effectiveness of ML models, particularly ANNs, in improving financial fraud detection.

Financial Fraud, Machine Learning, Credit Card Transaction Dataset, Detection.

"HYBRID DEEP LEARNING APPROACH FOR CREDIT CARD FRAUD DETECTION USING SMOTE AND NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.e320-e331, May-2025, Available :http://www.jetir.org/papers/JETIR2505506.pdf

"HYBRID DEEP LEARNING APPROACH FOR CREDIT CARD FRAUD DETECTION USING SMOTE AND NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppe320-e331, May-2025, Available at : http://www.jetir.org/papers/JETIR2505506.pdf