---
title: Predicting Corporate Fraud with Machine Learning
url: https://www.ml-quant.com/papers/repec/kap-jbuset-v-186-y-2023-i-1-d-10-1007-s10551-022-05120-2/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:kap:jbuset:v:186:y:2023:i:1:d:10.1007_s10551-022-05120-2
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10551-022-05120-2%3Bh%3Drepec%3Akap%3Ajbuset%3Av%3A186%3Ay%3A2023%3Ai%3A1%3Ad%3A10.1007_s10551-022-05120-2
featured: 2023-08-02
citations: unknown
topic: ML & AI Methods
---


# Predicting Corporate Fraud with Machine Learning

The study applies a machine learning model using the GONE framework to predict corporate fraud in China, revealing that the Random Forest model is superior and that exposure variables are vital for accurate prediction.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10551-022-05120-2%3Bh%3Drepec%3Akap%3Ajbuset%3Av%3A186%3Ay%3A2023%3Ai%3A1%3Ad%3A10.1007_s10551-022-05120-2
- Identifier: RePEc:kap:jbuset:v:186:y:2023:i:1:d:10.1007_s10551-022-05120-2
- Released: 2023-08-02
- First featured: Quant Letter No. 10 (2023-08-02): https://www.ml-quant.com/issues/2023-08-02/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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