arXiv:2607.00856v1 Announce Type: cross Abstract: In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications. At the same time, explainability remains critical in finance, a domain characterized by high stakes and strict regulatory requirements. Although numerous methods have been proposed to explain black box machine learning models, the majority of these approaches are designed for general purpose tasks and do not incorporate domain specific knowledge. In this work, we study the explainability of financial textual data mod
The rapid adoption of large language models in finance necessitates a concurrent focus on explainability due to the high-stakes and regulated nature of the industry.
Improving the explainability of AI in finance is crucial for regulatory compliance, risk management, and building trust in automated financial decision-making.
The focus is shifting from general-purpose AI explainability to domain-specific, context-aware methods tailored for financial language, enhancing transparency and utility.
- · Financial institutions adopting AI
- · Regulatory bodies
- · AI explainability researchers
- · Financial AI developers
- · Black box AI solutions in finance
- · Companies ignoring explainability requirements
Increased confidence and broader adoption of AI-driven tools in finance due to enhanced transparency.
New regulatory frameworks and industry standards specifically for explainable AI in financial applications.
The development of a competitive advantage for financial firms that successfully integrate and operationalize explainable AI methods.
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Read at arXiv cs.LG