Rodriguez, A.E. (2024): Tools for Ferreting-out Fraud: a Book Review of Mark Nigrini's Forensic Analytics.
Preview |
PDF
MPRA_paper_121861.pdf Download (119kB) | Preview |
Abstract
Book review of
MARK J. NIGRINI, Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations, John Wiley & Sons (Hoboken, NJ: Wiley, 2020, ISBN: 978-0-470- 89046-2, 463 pages, $95.00).
Item Type: | MPRA Paper |
---|---|
Original Title: | Tools for Ferreting-out Fraud: a Book Review of Mark Nigrini's Forensic Analytics |
Language: | English |
Keywords: | Fraud; Forensic Analytics; Book Review; forensic accounting; forensic economics |
Subjects: | K - Law and Economics > K1 - Basic Areas of Law > K13 - Tort Law and Product Liability ; Forensic Economics K - Law and Economics > K4 - Legal Procedure, the Legal System, and Illegal Behavior > K41 - Litigation Process K - Law and Economics > K4 - Legal Procedure, the Legal System, and Illegal Behavior > K42 - Illegal Behavior and the Enforcement of Law |
Item ID: | 121861 |
Depositing User: | A.E. Rodriguez |
Date Deposited: | 01 Oct 2024 13:25 |
Last Modified: | 01 Oct 2024 13:25 |
References: | Association of Certified Fraud Examiners. (2024). Occupational Fraud 2024: A Report to the Nations. Austin: Association of Certified Fraud Examiners. Retrieved from https://www.acfe.com/- /media/files/acfe/pdfs/rttn/2024/2024-report-to-the-nations.pdf Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection. Hoboken: John Wiley & Sons. Bloomberg, J. (2018, 09 16). Don't Trust Artificial Intelligence? Time To Open The AI 'Black Box'. Forbes. Retrieved from https://www.forbes.com/sites/jasonbloomberg/2018/09/16 /dont-trust-artificial-intelligence-time-to-open-the-ai-black-box/#29a5bbd33b4a Chiu, P.-C., Teoh, S. H., Zhang, Y., & Huang, X. (2023, August). Using Google Searches of Firm Products to Detect Revenue Management. Accounting, Organizations and Society, 109. doi:doi.org/10.1016/j.aos.2023.101457 Debener, J., Heinke, V., & Kriebel, J. (2023). Detecting insurance Fraud Using Supervised and Unsupervised Machine Learning. Journal of Risk and Insurance, 90, 743-768. doi:10.1111/jori.12427 Johnson, J. M., & Khoshgoftaar, T. M. (2019). Medicare Fraud Detection Using Neural Networks. Journal of Big Data, 6, 1- 35. doi:doi.org/10.1186/s40537-019-0225-0 King, J., & van Vuuern, G. W. (2016). Flagging Potential Fraudulent Investment Activity. Journal of Financial Crime, 23(4), 882- 901. doi:doi.org/10.1108/JFC-09-2015-0051 Li, P., & Byrnes, P. E. (2012). Book Reviews. Journal of Information Systems, 46(1), 207-212. doi:10.2308/isys-10247 Simonsohn, U. (2019, May 25). Data Colada. Retrieved from Number-Bunching: A New Tool for Forensic Data Analysis: https://datacolada.org/77 Weaver, C., McGinty, T., Mathews, A. W., & Maremont, M. (2024, July 8). Insurers Pocketed $50 Billion From Medicare for Diseases No Doctor Treated. The Wall Street Journal. Retrieved from https://www.wsj.com/health/healthcare/medicare-health-insurance-diagnosis-payments-b4d99a5d?mod=hp_lead_pos7 Yong, E. (2018, January 17). A Popular Algorithm is No Better at Predicting Crimes Than Random People. The Atlantic. Retrieved from https://www.theatlantic.com/technology/archive/2018/01/ equivant-compas-algorithm/550646 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/121861 |