Fraud Detection Using Ml And Data Science For Online Transaction

Abstract

newlineIn today s rapidly evolving digital world, the widespread use of digital wallets and online payment platforms has revolutionized how financial transactions are carried out. While these platforms offer users speed, convenience, and accessibility, they have also become prime targets for fraudulent activities. Cybercriminals continually develop advanced techniques to exploit vulnerabilities in digital systems, putting both consumers and financial institutions at risk. In response to this growing concern, this study presents an intelligent, hybrid approach that integrates deep learning, machine learning (ML), and data science techniques for real-time fraud detection in digital transactions. The proposed model combines the strengths of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), enhanced by data-driven strategies such as feature engineering, anomaly detection, and ensemble learning. CNNs are highly effective in identifying spatial patterns and relationships within transaction datasets, while RNNs especially Long Short-Term Memory (LSTM) networks excel at capturing temporal dependencies and sequential behavior patterns. By combining these capabilities, the hybrid CNN-RNN model is able to detect complex and previously unseen fraud patterns in real time. Additionally, the methodology incorporates traditional supervised and unsupervised ML techniques, such as decision trees, support vector machines, clustering algorithms, and statistical models like Benford s Law, to support comprehensive transaction analysis. The model is trained and evaluated on large, diverse datasets, simulating various real-world scenarios and transaction volumes. Extensive experiments demonstrate that the hybrid model significantly outperforms conventional standalone models in terms of accuracy, precision, recall, and false positive rates. What makes this framework especially practical is its ability to operate efficiently even in high volume environments, processing transactions in milliseconds without co