Chinese Corporate Fraud Risk Assessment with Machine Learning

Qingyang Lu, Chuyan Fu, Kailiang Nan, Yichu Fang, Jialue Xu, Jinyao Liu, Anthony Graham Bellotti, Boon Giin Lee

Research output: Journal PublicationArticlepeer-review


Corporate fraud is becoming a vital concern of Chinese financial industry in recent years. However, manual investigation of certain suspicious companies could result in mass consumption of resources. Aside from financial variables which were widely used in previous research, this paper has included novel features to assess Chinese corporate fraud, such as transfer of shares and Institutional Shareholdings, and these features are proven to be effective. We also present an efficient and accurate framework for corporate fraud detection that could be used as a fraud risk early warning system for financial institutions and regulatory authorities. Five machine learning methods have been implemented in the experiment and are compared using six metrics. Among these methods, XGboost has relatively higher performance overall in comparison to other models. Feature analysis has also been performed to analyze the influences of different groups of features in different models. This result illustrates that the models would achieve the best performance when all features are included, while financial condition has the most important impact among these groups. In addition, since the dataset being used is public, the result is simple for other researchers to reproduce and improve.

Original languageEnglish
Article number200294
JournalIntelligent Systems with Applications
Publication statusPublished - Nov 2023


  • Automatic financial audit
  • Chinese corporate fraud
  • Fraud early warning
  • Machine learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence


Dive into the research topics of 'Chinese Corporate Fraud Risk Assessment with Machine Learning'. Together they form a unique fingerprint.

Cite this