Topic Modeling in Fintech Regulations

Abstract

This paper analyzes South Korea’s fintech policy landscape from 2000 to 2023, employ- ing topic modeling techniques such as Non-negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA). We identify key topics and investigate the trends and shifts of recurring topics by examining 67 fintech-related policy documents issued by the national gov- ernment. Three dominant topics are institutional support for the fintech sector, strengthening regulation for safe and secure provision of fintech services, and fintech regulatory reform for economic growth. Keywords and their weight distribution provides useful insights into the core elements of each theme, illuminating policy priorities and objectives. Our findings suggest that the evolution of key fintech policy themes are connected to political shifts and significant trig- ger events. By leveraging Intertopic Distance Maps, we examine the relative dominance and commonality of topics. The results show the evidence of the interplay of policy goals to sup- port financial innovation and to ensure financial stability.