Blockchain, Big Data, and Deep Learning-based Fraud Detection System for Credit Card Fraud

Companies in the financial industry are among those who are implementing their operations online as a result of the internet's rapid growth in usage. Because of the enormous economic damages that arise from financial fraud, it is becoming an important concern as financial frauds are becoming more common and sophisticated globally. An economic fraud detection system (FDS) must be able to identify risks such as unusual assaults and unauthorized entry. The last few decades have seen a widespread application of data mining and machine learning (ML) methods to address this problem. These techniques still require improvement, though, to handle big data quickly and recognize unidentified trends in attacks. The importance of data safety and evaluation systems for big data has changed recently due to the enormous volume of data and its continuous growth. Big data is defined as information that is difficult to handle, store, and evaluate using standard software tools and databases. Big databases exhibit considerable quantity, speed, and diversity, necessitating the development of novel methods for handling them. Thus, employing blockchain and a deep learningbased (DL) approach for FDS is suggested in this work for credit card frauds. The objective of this framework is to improve the accuracy of identification in the context of big data in a private-permissioned blockchain network, while also improving the existing detection methods. A present DL algorithm called the Auto-encoder algorithm and a few other ML algorithms are contrasted with the outcomes of the suggested framework's evaluation using a real database of credit card frauds. According to the study findings, the LSTM performed flawlessly, achieving 99.9% accuracy in roughly a minute.