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A Model Framework to Estimate the Fraud Probability of Acquiring Merchants

Abstract 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... (more)
Created Date 2015
Contributor Zhou, Ye (Author) / Chen, Hong (Advisor) / Gu, Bin (Advisor) / Chao, Xiuli (Committee member) / Arizona State University (Publisher)
Subject Business / Banking / fraud detection / fraudulent transactions / merchant level / transaction level
Type Doctoral Dissertation
Extent 69 pages
Language Chinese
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Business Administration 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis