Exploring Transfer Learning Techniques in GANs for Fintech Applications

Authors

Adarsh Naidu

Abstract

Generative Adversarial Networks (GANs) have emerged as a crucial tool in financial technology (fintech) applications, such as fraud detection, risk evaluation, and synthetic data creation. However, training GANs for specific financial tasks is often constrained by limited labeled datasets and high computational expenses.Our investigation reveals that applying transfer learning approaches to GANs significantly enhances their performance in fintech applications, particularly when adapting pre-trained models across different financial domains. We analyze three transfer learning strategies—feature extraction, fine-tuning, and domain adaptation—on real-world financial datasets. Our results indicate that transfer learning reduces training duration by up to 40% and enhances model accuracy by 5–10% compared to baseline GANs trained from scratch, as evaluated by the Area Under the ROC Curve (AUC-ROC). Notably, a GAN pre-trained on credit card fraud data reached an AUC-ROC of 0.91 when applied to insurance fraud detection, surpassing conventional methods. These findings highlight the efficiency of transfer learning in facilitating the deployment of GANs in fintech, particularly in environments where labeled data is scarce. This research contributes to cross-domain innovations in financial data analytics.

Keywords

Exploring Transfer Learning Techniques in GANs for Fintech Applications. Adarsh Naidu. 2023. IJIRCT, Volume 9, Issue 4. Pages 1-6. https://www.ijirct.org/viewPaper.php?paperId=2503056