Explaining AI in Finance: Past, Present, Prospects

Explainability methods like Shapley values outperform traditional approaches in complex financial scenarios, offering superior interpretability compared to conventional linear regression, which is particularly important given the complexity of modern AI financial systems.

XAI directly enhances trust and responsible decision-making: The paper's simulations demonstrate that explainable AI systems foster greater trust among users, leading to more responsible decision-making within financial institutions—a critical finding for organizations implementing AI systems.

Regulatory pressure is driving XAI adoption: Forthcoming EU regulations are creating urgency around XAI research and implementation, suggesting that explainability is becoming not just a best practice but a compliance requirement for financial institutions using AI.

The evolution from simple to complex AI requires new transparency tools: As AI in finance has progressed from early statistical methods to sophisticated machine learning, traditional interpretability approaches have become insufficient, necessitating advanced XAI techniques to maintain accountability and understanding.

Understanding "why" AI makes financial decisions is essential for responsible deployment: The paper's emphasis on XAI's necessity underscores that financial institutions cannot responsibly deploy AI systems without being able to explain their decisions to stakeholders, regulators, and affected parties.