Announcing the Final Examination of Amina Issoufou Anaroua for the Master of Science in Statistics & Data Science – Data Science Track
This research paper focuses on fraud detection in the financial industry using Generative Adversarial Networks (GANs) in conjunction with univariate 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.
Committee in Charge:
Dr. Hsin-Hsiung Huang, Chair
Dr. Edgard Maboudou
Dr. Richard Ajayi
Read More