Machine Learning Applications for Accounting Disclosure and Fraud Detection

Book Description The prediction of the valuation of the quality of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the actual financial performance of the business activity.
Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify quality characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.

Content

Chapter 1. Corporate Governance as a Tool for Fraud Mitigation
Chapter 2. Corporate Sector Fraud: Challenges and Safety
Chapter 3. Corporate Governance: Introduction, Roles, Codes of Corporate Governance
Chapter 4. Fraud Governance and Good Practices Against Fraud
Chapter 5. Theoretical Analysis of Creative Accounting: Fraud in Financial Statements
Chapter 6. Operational Risk Framework and Fraud Management: A Contemporary Approach
Chapter 7. Current Trends in Investment Analysis
Chapter 8. A Study on Various Applications of Data Mining and Supervised Learning Techniques in Business Fraud Detection
Chapter 9. Detection and Prevention of Fraud in the Digital Era
Chapter 10. Downside Risk Premium: A Comparative Analysis
Chapter 11. Impact of Corporate Fraud on Foreign Direct Investment? Evidence From China
Chapter 12. Outsourcing of Internal Audit Services Instead of Traditional Internal Audit Units: A Literature Review on Transition From In-House to Outsourcing
Chapter 13. Machine Learning Techniques and Risk Management: Application to the Banking Sector During Crisis
Chapter 14. Application of Adaptive Neurofuzzy Control in the Field of Credit Insurance
Chapter 15. Prediction of Corporate Failures for Small and Medium-Sized Enterprises in Europe: A Comparison of Statistical and Machine Learning Approaches