Abstract: Detection of Credit Card Fraud becomes the primitive source of concentration. Investigators would follow a trend of calling a cardholder and invoke the respective discussion with a conclusion labeled as “genuine” or as “fraudulent” to alert the status of the transaction and update the same information to the respective person involved. Examined the effectiveness of the Novel Random Forest with Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost) methods. Both techniques have shown advantages in various facets of fraud detection. The innovative Random Forest algorithm excelled in recognizing anomalies and innovative fraud patterns, while the CNN model showed its capacity to capture complex temporal patterns within transaction data. The fraud detection of credit card transactions is analyzed here using machine learning algorithms like Convolutional Neural Network (CNN), Novel Random Forest (RF) and eXtreme Gradient Boost Algorithm optimized with the sparrow search algorithm. The pretest powers had been carried out with 80% for training and 20% for testing with the Kaggle data set. A statistically significant difference in three algorithms with two-tailed values would be p=0.001(p<0.05) in statistical analysis. In Prediction of Credit Card Fraud, the Novel Random Forest has performed better Credit Card Fraud detection than CNN and XGBoost. The Proposed Novel RF algorithm obtained 82.65%, the XGBoost obtained 77.98% and the CNN algorithm obtained 74.98% accuracy.

Key words: credit card fraud detection, convolutional neural networks, novel random forest, XGBoost, machine learning, sparrow search