Mustafa, Ablay2024-06-072024-06-072024-05-01Mustafa, A. (2024). Enhancing Credit Card Fraud Detection: A Multifaceted Approach Using Machine Learning and Adaptation to Non-Gaussian Distributions. Nazarbayev University School of Sciences and Humanitieshttp://nur.nu.edu.kz/handle/123456789/7798This paper addresses the urgent issue of credit card fraud by using existing techniques for fraud detection, including Linear Regression, Support Vector Machines (SVM), Random Forests, Statistical Analysis, and Behavioral Analytics. My goal, as an undergraduate student, is to replicate and extend the findings presented in the ”Data mining for credit card fraud: A comparative study” paper. To implement Linear Regression, I will model the relationship between variables to identify potential fraud indicators. Support Vector Ma- chines (SVM) will involve classifying transactions into normal and fraudulent categories based on distinct patterns. Random Forests will be employed to construct an ensemble of decision trees, enhancing accuracy in detecting anomalous transactions. For Statistical Analysis, I will utilize techniques like hypothesis testing and probability distributions to analyze transaction data for irregularities. Behavioral Analytics will involve studying user behavior over time, identifying deviations from typical patterns as potential fraud signals. Considering the assumption of Gaussian distribution in existing research, I aim to expand these techniques to datasets with non-Gaussian distributions. This involves adapting algorithms to handle different statistical properties, ensuring robustness across diverse datasets. By doing so, I intend to address a gap in current literature and enhance the applicability of credit card fraud detection methods in real-world scenarios.enAttribution-NonCommercial-ShareAlike 3.0 United StatesType of access: Open AccessCapstone Project