Cumberlands Credit Card Fraud Detection using Data Mining Techniques Paper

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University of the Cumberlands

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Title: Credit Card Fraud Detection using Data mining techniques


Write a 7-8-page APA formatted paper on a business problem(Credit Card Fraud Detection using Data mining techniques) that requires data mining, why the problem is interesting, the general approach you plan to take, what kind of data you plan to use, and finally how you plan to get the data. You should describe your problem, approach, dataset, data analysis, evaluation, discussion, references, and so on, in sufficient details, and you need to show supporting evidence in tables and/or figures. You need to provide captions for all tables and figures.

Your paper should include an abstract and a conclusion and a reference page with 3-5 references.



Please try to use below references:

SamanehSorournejad, Z. Zojaji, R. E. Atani and A. H. Monadjemi, "A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective," CoRR, pp. 1-26, November 2016.

G.Suresh and R. Raj, " International Journal of Data Mining Techniques and Applications, vol. 7, no. 1, pp. 21-24, June 2018.

F. N. Ogwueleka, Journal of Engineering Science and Technology, vol. 6, no. 3, pp. 311-322, 2011.

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Explanation & Answer

Attached.

FRAUD DETECTION

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Credit Card Fraud Detection using Data mining techniques

Students’ Name:
Institutional Affiliation:
Course:
Professors’ Name:
Date:

FRAUD DETECTION

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Abstract

Credit card fraud is increasing at a faster rate with the advancement of technology and the
worldwide means of communication, which results in the loss of billions of money yearly to
fraudsters. Therefore, banks and financial institutions are trying to come up with new rules and
means to avoid illegal actions. Therefore, banks and institutions that issue credit cards should
essentially have credit card fraud detection systems to minimize the losses caused by fraudsters.
The common methods used to detect fraud are data mining techniques, including Neural
networks, Support Vector Machines, and Decision trees. This paper presents a survey of the two
techniques decision trees and Neural Networks as fraud detection mechanisms and evaluates
each method using the design criteria.
Keywords: Fraud detection, data mining, techniques, credit card.

FRAUD DETECTION

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Introduction
Presently, banks and other financial institutions in the world encourage the use of credit
cards for financial availability. Therefore they issue out credit cards to consumers for easy
payment of products and services purchased. This provides several benefits for clients who are
unable to access cheques or solid money. There are many credit card users worldwide, but even
the providers cannot tell whether its owner is using the credit card. Many times, people use other
people’s credit cards without authorization or authentication, which is termed as credit card
fraud. This ethical issue has become common in today’s world and has resulted in huge sunk
costs in banks. Credit card fraud also affects online buyers since they do most of their payments
using credit cards. In the year 2011, the US reported over 3.4 dollars of financial loss due to
online fraud; therefore, the issue has been a concern (Ogwueleka, 2011). Credit card detection
refers to how banks can identify faster any fraud activity amidst several legitimate transactions.
Fraud detection mechanisms are increasingly being developed to cope with the new fraud
techniques across the globe. However, developing an accurate and efficient detection of fraud
transactions is challenging, which makes the development of efficient techniques essential.
The advanced technology in the information technology sector has led to the generation
of large amounts of data from several databases in various business sectors (Ogwueleka, 2011).
Timely information on fraud activities is essential for the banking industry, and the huge
databases can be used to extract valuable information. Data mining is the process where various
analysis tools are used to find patterns and relationships in the available data for effective
prediction. Data mining has been consistently used to obtain useful information from huge
...


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