Abstract
In today’s digital world, fraud detection has become an important part of financial security. This study explores and compares the performance of different machine learning models in identifying fraudulent transactions using the IEEE-CIS Fraud Detection dataset. Techniques such as Random Forest, Gradient Boosting, Deep Neural Networks, and Logistic Regression were evaluated. The dataset was pre-processed using SMOTE to balance the classes and improve model sensitivity to fraud cases. Each performance of the model was assessed using accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest overall performance with an F1-score of 99.23
Library of Congress Subject Headings
Fraud investigation; Forensic accounting; Big data; Data mining
Publication Date
7-17-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Ioannis Karamitsos
Recommended Citation
Alketbi, Abdulla Matar, "Real-Time Fraud Detection using Big Data" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12248
Campus
RIT Dubai
Plan Codes
PROFST-MS
