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This chapter explores the use of machine learning (ML) models to detect corporate fraud, focusing on financial statement analysis. It covers ML models that screen quantitative data, textual content, and entire financial reports, as well as those that incorporate external data sources for a more holistic approach. The chapter discusses the benefits and limitations of each method and highlights real-world applications and research in this area. It also emphasizes the potential of ML models to revolutionize fraud detection by analyzing vast amounts of data and uncovering hidden patterns that traditional methods may miss.

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Machine Learning Models to Screen Financial Statements for Fraud

  • 章节

  • 发表日期：

    2025年

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  • Jesper Sørensen

摘要

This chapter explores the use of machine learning (ML) models to detect corporate fraud, focusing on financial statement analysis. It covers ML models that screen quantitative data, textual content, and entire financial reports, as well as those that incorporate external data sources for a more holistic approach. The chapter discusses the benefits and limitations of each method and highlights real-world applications and research in this area. It also emphasizes the potential of ML models to revolutionize fraud detection by analyzing vast amounts of data and uncovering hidden patterns that traditional methods may miss.