Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services
Photo by Kindel Media on Pexels
Swiss finance institutions are increasingly adopting large language models (LLMs) to enhance customer services and operations. However, this trend introduc
Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services
Swiss finance institutions are increasingly adopting large language models (LLMs) to enhance customer services and operations. However, this trend introduces new risks, including operational, regulatory, and security threats. To mitigate these risks, a novel risk-adjusted harm scoring framework is proposed for automated red teaming in the BFSI sector. This framework aims to evaluate LLM security failures in a domain-specific manner, accounting for the unique challenges and regulatory requirements of the Swiss financial industry. By applying this framework, Swiss banks and financial institutions can better assess and manage the risks associated with LLM adoption.
Source
Original Article: Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services
Published: March 11, 2026
Author: Fabrizio Dimino
This article was automatically aggregated from ArXiv Computational Finance for informational purposes. Summary written by AI.
References
Transparency Notice: This article may contain AI-assisted content. All citations link to verified sources. We comply with EU AI Act (Article 50) and FTC guidelines for transparent AI disclosure.
Original Source
This article is based on Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services (ArXiv Computational Finance)
