Bayesian Variable Selection with Shrinkage Priors and Generative Adversarial Networks for Fraud Detection

Author

• Amina Issoufou Anaroua, University of Central FloridaFollow

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