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Machine-learning methods for credit card fraud detection

Author: Sarah E Woolston
Publisher: [Long Beach, California] : California State University, Long Beach, 2017.
Dissertation: M.S. California State University, Long Beach 2017
Series: California State University, Long Beach.; Master's thesis collection, Department of Mathematics and Statistics.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
Abstract: 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
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Details

Genre/Form: Academic theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Sarah E Woolston
ISBN: 9780355219098 0355219093
OCLC Number: 1010733879
Description: 1 online resource (vii, 93 pages) : illustrations (some color)
Series Title: California State University, Long Beach.; Master's thesis collection, Department of Mathematics and Statistics.
Responsibility: by Sarah E. Woolston.

Abstract:

Abstract: 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.

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.

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