Exploring AI-Driven Approaches to Enhance Blockchain Forensics in Cryptocurrency Fraud Detection

Abstract

In this paper, the author would elaborate on how Artificial Intelligence (AI) and machine learning (ML) may be utilized to improve blockchain forensics in detecting cryptocurrency frauds. Although blockchain provides a safe platform to carry out transactions using cryptocurrencies, fraudster transactions, including double-spending and money laundering, are major demerits. Scalability and efficiency are two downsides of the conventional approaches to blockchain forensics. According to the paper, AI-based (supervised and unsupervised) machine learning models may be applied to process blockchain information and better identify suspicious transactions. Their outcomes have revealed that the AI models including the decision trees, neural networks, and support vector machines are efficient in identifying complex fraud patterns compared to the traditional approaches. The paper has clarified that both AI and ML solutions will find their applicability in making the blockchain more secure and countering the intelligence of cryptocurrency frauds that is so far on the rising trend.