A Systematic Short Review on Intelligent Fraud Detection Approaches in the Banking Sector using Deep Learning and Machine Learning with Future Trends

Technological growth brings various fields into linear advancement, The banking domain is one among these and it deals with financial activities. There are some key enabling strategies such as big data, cloud computing, Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT) are utilized to enhance security in the banking domain. The term "Cyber Security" is very familiar these days because of various threats occurring in the internet society. Digital banking deals with online transactions and the bank offers most of the services through online applications or software. For simplified money transactions, the bank offers debit and credit cards to its users. As the technology increases its features in the banking domain, scammers also increase parallel with equal knowledge. So, protecting the money from fraud is necessary to keep the banking sector secure. So, several fraud detection frameworks utilize advanced technologies like deep and machine structure and it comes with unique merits and some challenges. In this survey, the details of the prior fraud detection techniques using machine and deep learning are discussed. Here, the brief introduction to fraud detection in the banking sector, the existing literature works regarding fraud detection, and the chronological assessment of fraud detection are presented in an elaborate way. In this review, the existing techniques used for banking security are discussed. Moreover, the simulation tools and the performance indices adopted for fraud detection in the banking sector are discussed. Lastly, the research gaps and challenges the given for future research studies.