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Unsupervised Machine Learning to Create Rule-Based Wire Fraud Detection.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Unsupervised Machine Learning to Create Rule-Based Wire Fraud Detection./
作者:	Barricklow, Austin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	33 p.
附註:	Source: Masters Abstracts International, Volume: 82-12.
Contained By:	Masters Abstracts International82-12.
標題:	Law enforcement. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28497198
ISBN:	9798505540763

Unsupervised Machine Learning to Create Rule-Based Wire Fraud Detection.
Barricklow, Austin.

Unsupervised Machine Learning to Create Rule-Based Wire Fraud Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 33 p.

Source: Masters Abstracts International, Volume: 82-12.

Thesis (M.S.)--Utica College, 2021.

This item must not be sold to any third party vendors.

The increase in the number of international wires requires a more efficient way of reviewing activity than the previous reactive approach. Waiting for victims of fraud to report activity before it is investigated often leads to the funds being withdrawn before any type of enforcement action can be taken. The number of transactions occurring each day makes it nearly impossible for even the most well-equipped team of investigators to review all activity. Therefore, there is a need to triage activity and review only the most concerning transactions. This is often accomplished by setting up a rule-based program that flags transactions for further review. If these rules are set up based on anecdotal information, it can introduce bias into the program and diminish the quality of services a financial institution can provide. However, if a rule-based program is built with insight gained from the measurable and repeatable output of a machine learning model, financial institutions can more accurately apply rules and have better success in preventing suspicious activity. This report describes methodologies used to develop an unsupervised machine learning classification model that clusters similar transactions and identifies those that do not fit into clusters, suggesting they are anomalies. The data includes thousands of international wire transactions including a few that stood out from the rest. Wire transaction rules were recommended based on similar characteristics of the anomalies. The first rule holds wires sent to a jurisdiction with a risk rating greater than 6 in an amount greater than $4,000,000.00 for further review.

ISBN: 9798505540763Subjects--Topical Terms:

607408
Law enforcement.
Subjects--Index Terms:

Basel Institute

Unsupervised Machine Learning to Create Rule-Based Wire Fraud Detection.
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