ABSTRACT. The FinTech industry has exhibited very high growth levels since the Global Economic and Financial Crisis of 2008. The sector growth has been accelerated because of the disruption caused by COVID-19 and that derived in the global health crisis, a crisis with significant implications for global economic stability. To examine the risk profile of FinTech firms, the CRISP-DM methodology was followed to aid in the implementation of clustering and classification algorithms, combined with time series regression models. This research paper offers insights on financial risk assessment by combining machine learning techniques and traditional econometric modeling to acknowledge challenges associated with the analysis of time series in the financial context and framed in the US FinTech sector. The main findings revealed a lack of significant differences between the FinTech and Non-FinTech firms in the US stock market. The results were surprising as the FinTech sector’s speed of development and fast changes in financial innovation have led to the emergence of significant risks that do not seem to be captured by the examined market and firm-specific data sets. The research outcomes point to a substantial vacuum on the regulatory framework at both national and international levels to ensure efficient FinTech governance and adequate industry development amid very ambitious growing prospects.
JEL codes: D12; D22; D91; L66; E71

Keywords: FinTech; Non-FinTech firms; emerging risk; data science; financial econometrics; systemic risk; regulation

How to cite: Morales, L., Gray, G., and Rajmil, D. (2022). “Emerging Risks in the FinTech Industry – Insights from Data Science and Financial Econometrics Analysis,” Economics, Management, and Financial Markets 17(2): 9–36. doi: 10.22381/emfm17220221.

Received 3 January 2022 • Received in revised form 7 June 2022
Accepted 18 June 2022 • Available online 27 June 2022