A Hybrid ML and Data Science Approach to Detect Online Fraud Transaction at Real Time

To safeguard customers and financial institutions, the swift growth of online transactions calls for strong fraud detection systems. To identify online fraud in real time, this study suggests a mixed machine learning (ML) and data science strategy. Through the integration of many data mining methodologies, such as supervised and unsupervised learning algorithms, the research endeavours to detect trends and anomalies suggestive of fraudulent activity. A comprehensive knowledge of transaction behaviours is made possible by the methodology's emphasis on dynamic feature extraction and selection, which makes use of massive datasets made up of transactional records. Using ensemble learning techniques reduces false positives and improves prediction accuracy. Results from experiments reveal that the hybrid model works well, outperforming conventional techniques in terms of processing speed and detection rates. Furthermore, the model's flexibility facilitates its implementation across multiple internet platforms, guaranteeing efficiency and scalability. The results highlight the value of a multidisciplinary strategy in the fight against online fraud, which will ultimately help create more secure online transaction environments. The foundation for future research targeted at improving fraud detection techniques in an increasingly digital economy is laid by this study.