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A Model Framework to Estimate the Fr...

Zhou, Ye.

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A Model Framework to Estimate the Fraud Probability of Acquiring Merchants.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Model Framework to Estimate the Fraud Probability of Acquiring Merchants./
Author:	Zhou, Ye.
Description:	69 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
Contained By:	Dissertation Abstracts International76-09A(E).
Subject:	Business administration. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3701468
ISBN:	9781321721270

A Model Framework to Estimate the Fraud Probability of Acquiring Merchants.
Zhou, Ye.

A Model Framework to Estimate the Fraud Probability of Acquiring Merchants. - 69 p.

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.

Thesis (Ph.D.)--Arizona State University, 2015.

Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis -- the first focuses on fraud detection at the store level, and the second focuses on fraud detection at the merchant level by aggregating store level data to the merchant level for merchants with multiple stores. My purpose is to put the model into business operations, helping to identify fraudulent merchants at the time of transactions and thus mitigate the risk exposure of the payment acquiring businesses. The model developed in this study is distinct from existing fraud detection models in three important aspects. First, it predicts the probability of fraud at the merchant level, as opposed to at the transaction level or by the cardholders. Second, it is developed by applying machine learning algorithms and logistical regressions to all the transaction level and merchant level variables collected from real business operations, rather than relying on the experiences and analytical abilities of business experts as in the development of traditional expert systems. Third, instead of using a small sample, I develop and test the model using a huge sample that consists of over 600,000 merchants and 10 million transactions per month. I conclude this study with a discussion of the model's possible applications in practice as well as its implications for future research.

ISBN: 9781321721270Subjects--Topical Terms:

3168311
Business administration.

A Model Framework to Estimate the Fraud Probability of Acquiring Merchants.
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100 1 $a Zhou, Ye. $3 1278889
245 1 2 $a A Model Framework to Estimate the Fraud Probability of Acquiring Merchants.
300 $a 69 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
500 $a Advisers: Hong Chen; Bin Gu.
502 $a Thesis (Ph.D.)--Arizona State University, 2015.
520 $a Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis -- the first focuses on fraud detection at the store level, and the second focuses on fraud detection at the merchant level by aggregating store level data to the merchant level for merchants with multiple stores. My purpose is to put the model into business operations, helping to identify fraudulent merchants at the time of transactions and thus mitigate the risk exposure of the payment acquiring businesses. The model developed in this study is distinct from existing fraud detection models in three important aspects. First, it predicts the probability of fraud at the merchant level, as opposed to at the transaction level or by the cardholders. Second, it is developed by applying machine learning algorithms and logistical regressions to all the transaction level and merchant level variables collected from real business operations, rather than relying on the experiences and analytical abilities of business experts as in the development of traditional expert systems. Third, instead of using a small sample, I develop and test the model using a huge sample that consists of over 600,000 merchants and 10 million transactions per month. I conclude this study with a discussion of the model's possible applications in practice as well as its implications for future research.
590 $a School code: 0010.
650 4 $a Business administration. $3 3168311
650 4 $a Banking. $2 bicssc $3 1557594
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710 2 $a Arizona State University. $b Business Administration. $3 1679662
773 0 $t Dissertation Abstracts International $g 76-09A(E).
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791 $a Ph.D.
792 $a 2015
793 $a Chinese
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3701468