GANs for Scenario Analysis and Stress Testing in Financial Institutions

Author(s)	Adarsh Naidu
Country	United States
Abstract	This study investigates the utilization of Generative Adversarial Networks (GANs) in constructing robust and realistic stress-testing scenarios for financial institutions. Stress testing has emerged as a pivotal regulatory necessity and risk management instrument in the wake of the 2008 financial crisis (Allen & Carletti, 2010) [1]. Conventional methods primarily rely on historical data or expert insights, which might not adequately account for novel but plausible crises (Basel Committee on Banking Supervision, 2018) [2]. We introduce a groundbreaking framework that employs GANs to generate a diverse set of realistic stress scenarios, addressing the deficiencies of traditional methodologies. Our empirical findings indicate that GAN-derived scenarios can replicate extreme market conditions while ensuring internal coherence across various economic indicators. This proposed approach fortifies financial institutions by enabling them to anticipate and mitigate a wider range of potential financial disruptions than what historical data alone can provide. Extensive trials using real-world financial datasets reveal that our framework surpasses traditional methods in scenario realism and risk coverage metrics, offering financial entities a more robust tool for assessing systemic vulnerabilities.
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-05-08
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.40681

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