S10_Credit_Question

S10_Credit_Question - Reviewing some of the large...

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Session 10, Credit Co. Credit Co. is the financial subsidiary of a large Canadian department store. It’s primary function is to manage the credit cards. It issues new credit cards, cancels cards that are not being paid or that have been stolen, sends statements and processes payments. Credit card charges consist of purchases or returns at the department store, cash advances, payments, interest, or adjustments. Historically, the company has had about one or two percent bad debts due to credit card fraud, which is consistent with the industry averages. Lately, however, this has mushroomed. In the past six months, credit card fraud has spiked to a high of ten percent, resulting in enormous losses for the company (hundreds of thousands of dollars).
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Unformatted text preview: Reviewing some of the large transactions, they are all online transactions (submitted via the internet to the store catalogue), and are either larger than normal for the customers affected, or from different departments. For example, some customers normally buy only clothing and food, and the fraudulent transactions were for music and hardware. Required: For each intelligent system listed in the text [expert system, natural language processing, neural network, fuzzy logic], indicate whether it would or would not be suitable for the company to help combat (or to help identify) credit card fraud. What other types of systems could be usable to prevent or detect credit card fraud? How? Justify your answers....
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This note was uploaded on 04/11/2011 for the course ADMS 2511 taught by Professor Jiu during the Spring '09 term at York University.

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