A Method for Identifying Financial Transaction System Fraud Using Hybrid Data Mining

As a consequence of the exponential growth of online financial transactions and the consequent increase in the danger of fraudulent conduct, the identification of fraudulent activity has emerged as a significant concern for financial institutions. For traditional fraud detection algorithms, one of the most prevalent challenges is dealing with massive amounts of transaction data that are multi-dimensional and skewed. The purpose of this research is to provide a technique that utilizes hybrid data mining methodologies in order to effectively identify fraudulent information in financial transaction systems. Some of the data mining methods that are used by the recommended approach include anomaly detection, clustering, and classification. These techniques are utilized to improve detection accuracy while simultaneously reducing the number of false positives. Researchers use a variety of supervised and unsupervised machine learning techniques, including as neural networks, decision trees, and support vector machines, in order to identify both frequent and atypical types of fraudulent activity. Preprocessing the data and selecting features are two steps that are taken in order to enhance the effectiveness and performance of the model. According to the data, the hybrid technique is more robust, accurate, exact, and recall-friendly than singlemodel tactics. This is the case when compared to conventional strategies. Through the provision of an effective and scalable solution for real-time fraud detection, the technique that has been proposed assists financial institutions in lowering losses and enhancing the security of transactions.