Machine Learning Applications for Accounting Disclosure and Fraud Detection

Financial statements can contain more than numbers: patterns in disclosure may offer clues about reporting quality, corporate risk, or possible fraud. This edited research volume brings machine learning into that investigative landscape, alongside the governance and audit practices that help organizations respond to misconduct.

Across 15 chapters, the contributors consider accounting disclosure and fraud from several angles. Machine learning, data mining, and supervised learning sit alongside discussions of creative accounting, corporate governance, internal auditing, operational risk, and fraud prevention—making this a broad reference for readers interested in how analytical methods meet real-world financial oversight.

Where machine learning meets financial reporting

The collection explores the use of machine-learning models to assess characteristics of corporate accounting disclosure and support the identification of fraud-related patterns. Its coverage also includes data-mining and supervised-learning applications, as well as research on predicting corporate failure using statistical and machine-learning approaches.

Fraud is also a governance question

Several chapters look beyond detection tools to the conditions surrounding misconduct: corporate governance, fraud policy, creative accounting, internal controls, and operational risk management. Together, these perspectives place analytical techniques within the wider work of understanding and addressing fraudulent behavior.

A wider view of business risk

The volume also reaches into investment analysis, banking-sector risk management, credit insurance, and corporate failure among European small and medium-sized enterprises. This range gives readers a view of how quantitative methods intersect with related questions in finance and organizational decision-making.

For research and professional reference

Accountants, auditors, forensic and financial professionals, business analysts, researchers, and students may find the collection relevant to their work or studies. It is an academic edited volume, with chapters that approach the subject from different disciplinary and methodological perspectives.