Artificial intelligence for knowledge graphs of cryptocurrency anti-money laundering in fintech

Abstract

Cryptocurrency anti-money laundering has become an important research topic in recent years. Legal empirical research combined with AI technology has received considerable attention. How to construct a knowledge graph of cryptocurrency anti-money laundering in a small sample of international cases and judgments on the prevention and control of cryptocurrency money laundering has become an essential issue for a better understanding of the relationship between the crime patterns and emerging financial technologies. In this study, we proposed artificial intelligence meta-learning with a few-shot learning model to construct a cryptocurrency anti-money laundering knowledge graph. The research method of this study aims at the abuse of electronic payment tools and cryptocurrency in various crimes by analyzing the causes and background, the amount of money, the type of crime, and the growth trend in recent years. The contribution of this study is that the proposed AI cryptocurrency anti-money laundering knowledge graphs in fintech can be applied to the content analysis and question-and-answer system of legal documents.