My thoughts on Generative Artificial Intelligence (genAI) in market risk management

My thoughts on Generative Artificial Intelligence (genAI) in market risk management

While there are numerous applications for genAI based on LLM models, market risk management is not particularly suited for it. The sector, especially risk control functions, faces several challenges:

• Opacity of LLM Models: In market risk management, figures need to be audited and validated. LLM models are often considered a black box, making it extremely difficult to explain their outputs. This lack of transparency poses a significant challenge for institutions trying to validate and explain model decisions to regulators and stakeholders. Ensuring these models are robust, stable, and understandable is a major hurdle.
• Potential for Major Financial Losses: The occurrence of errors in genAI models can lead to significant financial losses. The high stakes involved in market risk management mean that even small mistakes can have severe financial repercussions, making it a risky proposition to rely on genAI.
• Data Sensitivity and Privacy: Market risk management often involves handling highly sensitive and confidential data, including models, positions, and margins. Using an online chatbot or API could potentially expose this sensitive information, leading to privacy breaches and data leaks, which are unacceptable in such a critical sector.
• Regulatory Compliance: The financial industry is one of the most heavily regulated sectors. Institutions must comply with strict guidelines from regulators like the ECB and the FED regarding the computation of various metrics. Any system, including those using genAI, must adhere to existing financial regulations concerning risk management, reporting, and transparency.
• Integration with Existing Systems: Legacy systems in financial institutions are often deeply entrenched and highly complex. Integrating new genAI solutions with these existing systems can be challenging, requiring significant effort and resources to ensure compatibility and seamless operation.
• Talent and Expertise: There is a high demand for professionals who possess both AI technology expertise and a deep understanding of financial risk management. The shortage of skilled professionals who can develop and implement genAI solutions tailored to the specific needs of the financial industry is a significant barrier to adoption.