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  • Photo du rédacteurHélène Dufour

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

The era of big data revolutionizes operational management in enterprises, amplifying the challenges for auditors in managing vast corporate information and escalating fraud risks. This study explores machine learning's role in identifying financial fraud, constructing models based on fraud triangle theory and empirical data. The model, particularly LightGBM, achieves a 73.21% accuracy, showcasing its effectiveness in predicting fraud risks in publicly traded companies.


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