Black-Box Model Risk in Finance

Abstract

Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and quantified. In this sub-chapter, we will focus on a well studied application of machine learning techniques, to pricing and hedging of financial options. Our aim will be to highlight the various sources of risk that the introduction of machine learning emphasises or de-emphasises, and the possible risk mitigation and management strategies that are available.

Keywords: Neural Networks, derivative pricing, hedging, model risk, data cleaning, quantitative finance, data-driven models, market generators, uncertainty, reinforcement learning, sensitivity, adversarial attacks, robustness, expert design

JEL Classification: D81, C6