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Decision Models for Application of M...

Bled, Philippe.

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Decision Models for Application of Machine Learning Methods for Fraud Detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Decision Models for Application of Machine Learning Methods for Fraud Detection./
Author:	Bled, Philippe.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	57 p.
Notes:	Source: Masters Abstracts International, Volume: 80-10.
Contained By:	Masters Abstracts International80-10.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13859270
ISBN:	9781392086858

Decision Models for Application of Machine Learning Methods for Fraud Detection.
Bled, Philippe.

Decision Models for Application of Machine Learning Methods for Fraud Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 57 p.

Source: Masters Abstracts International, Volume: 80-10.

Thesis (M.S.)--The University of Tulsa, 2019.

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

Fraudulent transactions are a major expense for businesses and a hassle for customers. The development of machine learning and artificial neural networks can provide an improved solution to the problem of fraud. This thesis proposes economically informed models for tuning a binary classifier in order to minimize the expected cost of dealing with false positives and negatives. It constructs simulated dataset for fraudulent transactions at a retailer, then evaluates the performance of the proposed decision models. The decision models are bench-marked against established classification methods. The thesis demonstrates that the total cost incurred by fraudulent transactions can be significantly reduced by accounting for cost in the decision making process.

ISBN: 9781392086858Subjects--Topical Terms:

1669109
Applied Mathematics.
Subjects--Index Terms:

Decision

Decision Models for Application of Machine Learning Methods for Fraud Detection.
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100 1 $a Bled, Philippe. $3 3551516
245 1 0 $a Decision Models for Application of Machine Learning Methods for Fraud Detection.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 57 p.
500 $a Source: Masters Abstracts International, Volume: 80-10.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Moore, Tyler.
502 $a Thesis (M.S.)--The University of Tulsa, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Fraudulent transactions are a major expense for businesses and a hassle for customers. The development of machine learning and artificial neural networks can provide an improved solution to the problem of fraud. This thesis proposes economically informed models for tuning a binary classifier in order to minimize the expected cost of dealing with false positives and negatives. It constructs simulated dataset for fraudulent transactions at a retailer, then evaluates the performance of the proposed decision models. The decision models are bench-marked against established classification methods. The thesis demonstrates that the total cost incurred by fraudulent transactions can be significantly reduced by accounting for cost in the decision making process.
590 $a School code: 0236.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Mathematics. $3 515831
650 4 $a Artificial intelligence. $3 516317
653 $a Decision
653 $a Fraud
653 $a Learning
653 $a Machine
653 $a Networks
653 $a Neural
690 $a 0364
690 $a 0405
690 $a 0800
710 2 $a The University of Tulsa. $b Mathematics. $3 3547574
773 0 $t Masters Abstracts International $g 80-10.
790 $a 0236
791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13859270