Effectiveness of Machine and Deep Learning for Blockchain Technology in Fraud Detection and Prevention

Abstract

Blockchain was formerly originated to prevent fraud in digital currency exchanges. Blockchain refers to a collective decentralised ledger that is unaffected by tinkering. It provides the confirmed contributor access to the store, views, and shares the digital information in a situation that is rich in safety, which in turn supports the development of trust, liability, and transparent business associations. Identity theft and fraud safety are endless challenges for everyone in buying and selling. With each novelty in security technology, hackers and fraudsters learn how to outsmart the technology and breach these networks. The first section of the chapter describes the structure of the blockchain, its framework, the pros and cons of combining these technologies, and the role and importance of machine and deep learning algorithms in fraud detection and prevention in the blockchain. The next section focuses on the reported work, highlighting different researchers’ work for fraud detection and prevention using Blockchain technology. The chapter’s final section comprises a comparative analysis based on various performance parameters such as accuracy, the area under the curve, confidence, true negative, false positive, and truly positive for a different type of fraud detection using blockchain technology.