COMPARATIVE ANALYSIS OF CNN FOR FINANCIAL FRAUD DETECTION ON TABULAR TRANSACTION DATA
Financial Fraud threatens global financial stability gravely. Fraud comes with important economic losses and has negative effects on public trust. Traditional rule-based systems and older methods of machine learning cannot detect these patterns of fraud, especially when facing complex or continuously changing fraud patterns in the era of high-speed and high-volume transactions. This research proposes a deep learning technique for the detection of financial fraud by using Convolutional Neural Networks on reshaped tabular transaction data. The proposed CNN model utilizes spatial feature extraction by reprocessing 1D financial records into 2D matrices. This method aims in recognizing local relationships among features as indicative of fraudulent activity. Preprocessing steps for this proposed system include class balancing, scaling of features, and one-hot encoding. It is tested on a highly imbalanced credit card fraud dataset from Kaggle. Then, the classification accuracy was 99.23%. An accuracy of 98.6%, a recall of 97.9%, an F1-score of 98.25%, and an AUC-ROC of 0.992 were also reported. These results indicate that the CNN model can capture the hidden patterns of fraud well when compared to traditional methods. Furthermore, they are less dependent on manual feature engineering and adaptive to evolving fraud strategies.