Keywords
Generative Adversarial Networks (GANs); Fraud Detection; Bayesian Model; Variable Selection; Data Imbalance; Synthetic Data Generation
Abstract
This research paper focuses on fraud detection in the financial industry using Generative Adversarial Networks (GANs) in conjunction with Uni and Multi Variate Bayesian Model with Shrinkage Priors (BMSP). The problem addressed is the need for accurate and advanced fraud detection techniques due to the increasing sophistication of fraudulent activities. The methodology involves the implementation of GANs and the application of BMSP for variable selection to generate synthetic fraud samples for fraud detection using the augmented dataset. Experimental results demonstrate the effectiveness of the BMSP GAN approach in detecting fraud with improved performance compared to other methods. The conclusions drawn highlight the potential of GANs and BMSP for enhancing fraud detection capabilities and suggest future research directions for further improvements in the field.
Completion Date
2024
Semester
Spring
Committee Chair
Huang, Hsin-Hsiung
Degree
Master of Science (M.S.)
College
College of Sciences
Department
Statistics and Data Science
Degree Program
Big Data Analytics
Format
application/pdf
Identifier
DP0028295
URL
https://purls.library.ucf.edu/go/DP0028295
Language
English
Rights
In copyright
Release Date
May 2024
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
Campus Location
Orlando (Main) Campus
STARS Citation
Issoufou Anaroua, Amina, "Bayesian Variable Selection with Shrinkage Priors and Generative Adversarial Networks for Fraud Detection" (2024). Graduate Thesis and Dissertation 2023-2024. 126.
https://stars.library.ucf.edu/etd2023/126
Accessibility Status
Meets minimum standards for ETDs/HUTs