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Application of Machine Learning Models in Detecting Financial Fraud in Publicly Traded Companies

https://doi.org/10.2139/ssrn.4635606
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.ejor.2014.08.016openalex-extracted
Prediction of financial distress: An empirical study of listed Chinese companies using data mining
inferredcites →https://doi.org/10.1093/rfs/hhaa009openalex-extracted
Empirical Asset Pricing via Machine Learning
inferredcites →https://doi.org/10.1111/1911-3846.12134openalex-extracted
The Value of Political Ties Versus Market Credibility: Evidence from Corporate Scandals in China
inferredcites →https://doi.org/10.1111/j.1540-6261.2010.01614.xopenalex-extracted
Who Blows the Whistle on Corporate Fraud?
inferredcites →https://doi.org/10.1111/j.1755-053x.2012.01211.xopenalex-extracted
Financial Misrepresentation and Its Impact on Rivals
inferredcites →https://doi.org/10.2139/ssrn.774364openalex-extracted
inferredcites →https://doi.org/10.2308/accr.2008.83.1.83openalex-extracted
The Contagion Effects of Accounting Restatements

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-81834-9_12openalex-snapshot
Machine Learning Models to Screen Financial Statements for Fraud
inferred← citeshttps://doi.org/10.1016/j.procs.2026.06.347openalex-snapshot
A Deep Hybrid Learning Approach for Fraud Detection in Digital Payment Systems Using Bi-LSTM and Ensemble Boosting
inferred← citeshttps://doi.org/10.4018/979-8-3373-4149-1.ch002openalex-snapshot
Decision Support Systems for Real-Time Fraud Response
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