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Internal Fraud: Prevention and Detec...

Utica College.

Internal Fraud: Prevention and Detection Methods Including Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Internal Fraud: Prevention and Detection Methods Including Machine Learning.
作者:	White, Amelia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2018
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 58-01.
附註:	Adviser: Kyung-Seok Choo.
Contained By:	Masters Abstracts International58-01(E).
標題:	Finance.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10933703
ISBN:	9780438362529

Internal Fraud: Prevention and Detection Methods Including Machine Learning.
White, Amelia.

Internal Fraud: Prevention and Detection Methods Including Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 48 p.

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.S.)--Utica College, 2018.

Internal fraud is one of the greatest financial threats to most organizations. Employees will continue to be a risk in the workplace as long as organizations have the need to hire them. To maintain profitability, risk prevention processes should be implemented in every organization. Internal controls encompass policies, processes, and procedures. Applying effective internal controls within an organization can limit system misuse while preventing internal fraud. When preventative techniques and internal controls are not sufficient to mitigate fraud, detection methods should be used. This capstone will examine types of internal fraud, effective fraud prevention measures, and the most beneficial detection methods in relation to large-scale organizations and multi-operational organizations. Multi-operational organizations are comprised of multifaceted business models, each of which require a unique set of procedures and expertise. One standard fraud detection report or method may not be sufficient to detect malfeasance in its various forms due to a wide array of operating procedures within these companies. Many organizations do not have a designated Fraud Department. Most organizations that employ fraud teams utilize exception-based reporting (EBR) and trend analytics as their fraud detection methods, which can be time-consuming for multi-operational organizations. This research will demonstrate that multi-operational organizations like casinos, cruise ships, theme parks, stadiums, hotels, and similar business structures would greatly benefit from the use of machine learning processes to detect internal fraud. Machine learning processes would run automatically, extract anomalies, predict risk trends, and alert analysts. This allows analysts to utilize their time to investigate potential threats, rather than manually mine data or run various fraud reports.

ISBN: 9780438362529Subjects--Topical Terms:

183252
Finance.

Internal Fraud: Prevention and Detection Methods Including Machine Learning.
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