Leveraging Financial Social Media Data for Corporate Fraud Detection

Abstract

Corporate fraud can lead to significant financial losses and cause immeasurable damage to investor confidence and the overall economy. Detection of such frauds is a time-consuming and challenging task. Traditionally, researchers have been relying on financial data and/or textual content from financial statements to detect corporate fraud. Guided by systemic functional linguistics (SFL) theory, we propose an analytic framework that taps into unstructured data from financial social media platforms to assess the risk of corporate fraud. We assemble a unique data set including 64 fraudulent firms and a matched sample of 64 nonfraudulent firms, as well as the social media data prior to the firm’s alleged fraud violation in Accounting and Auditing Enforcement Releases (AAERs). Our framework automatically extracts signals such as sentiment features, emotion features, topic features, lexical features, and social network features, which are then fed into machine learning classifiers for fraud detection. We evaluate and compare the performance of our algorithm against baseline approaches using only financial ratios and language-based features respectively. We further validate the robustness of our algorithm by detecting leaked information and rumors, testing the algorithm on a new data set, and conducting an applicability check. Our results demonstrate the value of financial social media data and serve as a proof of concept of using such data to complement traditional fraud detection methods.

Key words and phrases:

• corporate fraud
• financial social media
• fraud detection
• social media platform
• systemic functional linguistics theory
• text analytics

Supplemental File

Supplemental data for this article can be accessed on the publisher’s website at DOI: https://doi.org/10.1080/07421222.2018.1451954

Additional information

Funding

This work was supported by the National Science-Technology Support Plan of China (2015BAK18B02), a development grant from Shenzhen Science, Technology and Innovation Commission (JCYJ20160229165300897), and a Hong Kong GRF grant (193213).

Notes on contributors

Wei Dong

Wei Dong ([email protected]) is a Ph.D. candidate in management science and engineering at the School of Management, University of Science and Technology of China. He is in a joint doctoral program with City University of Hong Kong. His research interests include social media, text mining, and business intelligence. He has published in European Journal of Operational Research.

Shaoyi Liao

Shaoyi Liao ([email protected]) is a professor in the Department of Information Systems, City University of Hong Kong. He obtained his Ph.D. in information systems from the Aix-Marseille University, France. His research is focused on artificial intelligence, business intelligence, and social media analytics. He has published in MIS Quarterly, INFORMS Journal on Computing, Decision Support Systems, and ACM Transactions on Management Information Systems, among others.

Zhongju Zhang

Zhongju Zhang ([email protected]; corresponding author) is codirector of the Actionable Analytics Lab and an associate professor of information systems at the W. P. Carey School of Business, Arizona State University. His research focuses on how information technology and data analytics impact consumer behavior and decision making, create business value, and transform business models. His work has appeared in the leading academic journals including Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Production and Operations Management, INFORMS Journal on Computing, and others. He has won numerous research and teaching awards.