CREDIT CARD FRAUD DETECTION SYSTEM USING ADVANCED BIDIRECTIONAL GATED RECURRENT UNIT

Credit card fraud refers to the physical loss of credit card or loss of sensible credit card data. Several machine-learning algorithms can be applied for detection of false credit card activities. Financial fraud is an ever-growing threat, with real results in the business activity. Machine learning had performed an essential role in the discovery of credit card fraud in online transactions. The performance of fraud detection in credit card transactions is influenced by the sampling method on the dataset, collection of variables and detection technique(s) applied. Consequently, applications of detecting credit card frauds are increasing for high-value banks and financial institutions on demand. False activities can happen in many ways and can place into several categories. Financial fraud, such as money laundering, is a severe process of crime that makes illegitimately obtained funds go to terrorism or other criminal activity. The primary issue when it happens to represent fraud detection as a classification difficulty comes from the reality that in real-world data, the majority of transactions are not false. This variety of unauthorised action requires complex networks of business and financial transactions, which perform it challenging to detect fraud entities and find the characteristics of fraud. In this paper, the class imbalance problem is handled by finding legal or fraud transaction using advanced bidirectional Gated recurrent unit (ABiGRU) based machine learning algorithm. Also, suggesting advanced frequent pattern mining algorithm. It can leverage both network data and function data for the detection of financial fraud and very opportunity presented using the best machine learning paradigm. The experimental results illustrate that the proposed scheme provides better accuracy compared with the previous algorithms..