Final Exam Project Layout

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Mississippi College *

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640

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Information Systems

Date

Apr 3, 2024

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docx

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Uploaded by mmakol

Running Head: Final Exam Project Layout UNIVERSITY OF THE POTOMAC DACS640:7: Online Data Integration, Warehousing, Provenance, and Analysis Dr. Daryl R. Brydie Megha Makol, Quoc Bao Tran, Cassidy Ballard, Roshni Patel

Final Exam Project Layout Abstract The goal of this project is to detect fraudulent credit card transactions using machine learning techniques to prevent fraudsters from using customers' accounts in an unauthorized manner. Credit card fraud is increasing rapidly around the world, which is why action should be taken to stop fraudsters. Putting a limit on those actions would benefit customers because their money would be recovered and retrieved back into their accounts, and they would not be charged for items or services that they did not purchase, which is the main goal of the project. The goal of this project is to detect fraudulent credit card transactions using machine learning techniques to prevent fraudsters from using customers' accounts in an unauthorized manner. Credit card fraud is increasing rapidly around the world, which is why action should be taken to stop fraudsters. Putting a limit on those actions would benefit customers because their money would be recovered and retrieved back into their accounts, and they would not be charged for items or services that they did not purchase, which is the main goal of the project. The detection of fraudulent transactions will be accomplished using three machine-learning techniques: KNN, SVM, and Logistic Regression.

Final Exam Project Layout Introduction With more people using credit cards in their daily lives, credit card companies must take extra precautions to ensure the security and safety of their customers. The global credit card market revenue reached an impressive $152 billion in 2022, growing at a 9.1% annual rate, highlighting the massive scale of credit card transactions worldwide. The number of credit card holders worldwide has steadily increased to 1.25 billion in 2023, representing a 2.79% annual growth rate from 1.1 billion in 2018. (Caporal). Credit card holders in the United States number 166 million, while credit card holders in Canada number 36 million. Through the first half of 2023, 559,000 cases of identity theft were reported. With 440,666 reports, credit card fraud was the most common type of identity theft in 2022. In the first half of 2023, 219,713 credit card fraud reports were filed. The fastest-growing type of identity theft is synthetic fraud. There are two types of credit card fraud. The first is when an identity thief opens a credit card account in your name; reports of this fraudulent behavior increased 48% from 2019 to 2020. The second type is when an identity thief uses an existing account that you created, usually by stealing credit card information; reports on this type of fraud increased 9% from 2019 to 2020. These statistics piqued our interest because the numbers have been steadily increasing over the years, motivating us to try to solve the problem analytically by employing various machine learning methods to detect credit card fraudulent transactions within many transactions. (Radage) Project Goals The primary goal of this project is to detect credit card fraudulent transactions, as it is critical to identify fraudulent transactions so that customers are not charged for products they did not purchase. The detection of fraudulent credit card transactions will be performed using multiple ML techniques, and then a comparison will be made between the outcomes and results of each

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