Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach
Problem
Financial report fraud among Chinese listed companies is growing in number, complexity, and concealment, requiring faster detection using only structured financial report data.
Why it matters
Fraud undermines capital market supervision and audit efficiency.
What the AI did
The study compared two single-classifier and five ensemble machine learning algorithms on 28 sets of raw financial report data from Chinese listed companies (1998–2016) to build a fraud identification model.
Results
Ensemble learning algorithms generally outperformed single classification algorithms; the stacking algorithm performed best (no numeric metrics stated).
Impact
Provides direct evidence that ensemble learning improves fraud detection for Chinese listed companies.
Record
| Venue | Sustainability |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Yasheng Chen, Zhuojun Wu |
| Citations | 54 (OpenAlex, at import time) |
| References | 43 |
| Model or method | Ensemble learning algorithms; stacking algorithm performed best |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/su15010105 |
| Open access | gold |
| Keywords | financial fraud, machine learning, ensemble learning, stacking, listed companies, China |
Importance rating
real but narrower advance
Abstract describes financial report fraud as enormous in number, complex, and hidden, undermining capital market supervision and audit efficiency for Chinese listed companies.
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