Zero-Revelation RegTech: Detecting Risk through Linguistic Analysis of Corporate Emails and News

Abstract

In this paper, we demonstrate how an applied linguistics platform may be used to parse corporate email content and news to assess factors predicting escalating risk or the gradual shifting of other critical characteristics within the firm before they are eventually manifested in observable data and financial outcomes. We find that email content and news articles meaningfully predict increased risk and potential malaise. We also find that other structural characteristics, such as the average email length, are strong predictors of risk and subsequent performance. We present implementations of three spatial analyses of internal corporate communication, i.e., email networks, vocabulary trends, and topic analysis. Overall, we propose a RegTech solution by which to systematically and effectively detect escalating risk or potential malaise without the need to manually read individual employee emails.

Keywords: Fintech, RegTech, Corporate governance, Text mining, Email analysis, Email networks, Mood and net sentiment

JEL Classification: G00, G01, G28, G38