Award Date

December 2018

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science

First Committee Member

Fatma Nasoz

Second Committee Member

Laxmi Gewali

Third Committee Member

Justin Zhan

Fourth Committee Member

Mehmet Erdem

Number of Pages

66

Abstract

Credit card fraud is an ever-growing problem in today’s financial market. There has been a rapid increase in the rate of fraudulent activities in recent years causing a substantial financial loss to many organizations, companies, and government agencies. The numbers are expected to increase in the future, because of which, many researchers in this field have focused on detecting fraudulent behaviors early using advanced machine learning techniques. However, the credit card fraud detection is not a straightforward task mainly because of two reasons: (i) the fraudulent behaviors usually differ for each attempt and (ii) the dataset is highly imbalanced, i.e., the frequency of majority samples (genuine cases) outnumbers the minority samples (fraudulent cases).

When providing input data of a highly unbalanced class distribution to the predictive model, the model tends to be biased towards the majority samples. As a result, it tends to misrepresent a fraudulent transaction as a genuine transaction. To tackle this problem, data-level approach, where different resampling methods such as undersampling, oversampling, and hybrid strategies, have been implemented along with an algorithmic approach where ensemble models such as bagging and boosting have been applied to a highly skewed dataset containing 284807 transactions. Out of these transactions, only 492 transactions are labeled as fraudulent. Predictive models such as logistic regression, random forest, and XGBoost in combination with different resampling techniques have been applied to predict if a transaction is fraudulent or genuine. The performance of the model is evaluated based on recall, precision, f1-score, precision-recall (PR) curve, and receiver operating characteristics (ROC) curve. The experimental results showed that random forest in combination with a hybrid resampling approach of Synthetic Minority Over-sampling Technique (SMOTE) and Tomek Links removal performed better than other models.

Keywords

credit card fraud detection; machine learning techniques

Disciplines

Computer Sciences

File Format

pdf

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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