Explaining AI in Finance: Past, Present, Prospects

TL;DR

This paper reviews the evolution of AI in finance, emphasizing the importance of Explainable AI (XAI) for interpretability, trust, and regulatory compliance, with simulations showing XAI's positive impact on decision-making.

Contribution

It highlights the progression of AI methods in finance, compares interpretability techniques like Shapley values, and demonstrates XAI's role in fostering trust and responsibility in financial decisions.

Findings

XAI methods like Shapley values offer better interpretability than linear regression.

Simulations show XAI increases trust in AI-driven financial decisions.

Further XAI research is needed to meet upcoming EU regulations.

Abstract

This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI's evolution from early statistical methods to sophisticated machine learning, highlighting XAI's role in popular financial applications. The paper underscores the superior interpretability of methods like Shapley values compared to traditional linear regression in complex financial scenarios. It emphasizes the necessity of further XAI research, given forthcoming EU regulations. The paper demonstrates, through simulations, that XAI enhances trust in AI systems, fostering more responsible decision-making within finance.