國立虎尾科技大學 |

Machine-Learning Methods for Credit Card Fraud Detection.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine-Learning Methods for Credit Card Fraud Detection./
Author:	Woolston, Sarah E.
Description:	1 online resource (100 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355219098

Machine-Learning Methods for Credit Card Fraud Detection.
Woolston, Sarah E.

Machine-Learning Methods for Credit Card Fraud Detection. - 1 online resource (100 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)

Includes bibliographical references

In order to thwart fraudsters, financial institutions must use current, advanced, customized predictive analytics to protect themselves. Data scientists and statisticians who understand machine learning and statistical methods are in increasingly high-demand and the demand for them is growing each year. Technically, machine learning is a subfield of artificial intelligence whereas statistics is subdivision of mathematics and many believe they only need in depth knowledge of one in order to be a predictive modeler. This fallacy leads to inefficient and/or inaccurate models, and sadly, many industries have not yet realized that the mathematics behind the model is just as important, if not more important, than the computer science needed to implement it. However, some businesses have and this thesis will hopefully help both industry and academia move further along in this direction.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355219098Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Machine-Learning Methods for Credit Card Fraud Detection.
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245 1 0 $a Machine-Learning Methods for Credit Card Fraud Detection.
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300 $a 1 online resource (100 pages)
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500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Adviser: Olga Korosteleva.
502 $a Thesis (M.S.) $c California State University, Long Beach $d 2017.
504 $a Includes bibliographical references
520 $a In order to thwart fraudsters, financial institutions must use current, advanced, customized predictive analytics to protect themselves. Data scientists and statisticians who understand machine learning and statistical methods are in increasingly high-demand and the demand for them is growing each year. Technically, machine learning is a subfield of artificial intelligence whereas statistics is subdivision of mathematics and many believe they only need in depth knowledge of one in order to be a predictive modeler. This fallacy leads to inefficient and/or inaccurate models, and sadly, many industries have not yet realized that the mathematics behind the model is just as important, if not more important, than the computer science needed to implement it. However, some businesses have and this thesis will hopefully help both industry and academia move further along in this direction.
520 $a In this thesis, we explore existing methodologies for fraud detection proposed by academic professionals around the globe and illustrate their accuracy, efficiency and reliability on a large dataset downloaded from a public website. The methods analyzed are hidden Markov models (HMM), convolutional neural networks (CNN), and support vector machines (SVM). For each method, we present the history and motivation, theoretical framework, strengths and weaknesses, and numerical examples done in either R or SAS Enterprise Miner.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Applied mathematics. $3 1069907
650 4 $a Computer science. $3 573171
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a California State University, Long Beach. $b Mathematics and Statistics. $3 1182969
773 0 $t Masters Abstracts International $g 56-06(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10602012 $z click for full text (PQDT)