The integration of advanced technology into everyday practices is not just a luxury, it's a necessity. This article, the third in our series, sheds light on the simple yet powerful applications of Large Language Models (LLMs) in treasury and risk management. In a sector where time is as valuable as the information handled, LLMs like GPT-4 are revolutionising how we manage and process information.
The Challenge of Information Overload
Professionals in treasury and risk management are constantly in need of up-to-date information on regulatory policies, economic commentary, and industry opinions. While the internet is a treasure trove of such data, sifting through it to find relevant information is a daunting and time-consuming task. The hours spent in this endeavour are often at the expense of more critical, strategic work.
The Solution: LLMs in Action
The capabilities of LLMs extend beyond mere information retrieval. To demonstrate this, I employed GPT-4 to research the Bank of England's publications on using large language models and AI in financial services. The model efficiently located the latest feedback statement, FS2/23, and provided a concise summary. Further probing into the summary revealed in-depth insights, especially regarding "live" regulatory guidance.
Three noteworthy points:
Rapid Information Retrieval: GPT-4 can swiftly navigate the web to find the exact information you need.
Summarisation Skills: It can condense extensive data into digestible summaries.
Deep Dive Capability: You can delve deeper into these summaries to uncover more nuanced details as required.
Practical Workplace Application
The potential of GPT-4 in treasury and risk management is immense. It lends itself exceptionally well to:
Developing high-quality content for management reports, committee briefings, policies, and procedures.
Providing updates on economic indicators and Monetary Policy Committee (MPC) meetings, thereby explaining the state of the economy in layman's terms.
Assisting senior management with relevant and timely information.
Professionals in this field, often pressed for time, will find LLMs invaluable assistants, capable of undertaking various tasks to expedite their workflow.
Human-AI Collaboration
It's crucial to note that, as of now, LLMs like GPT-4 cannot entirely replace human intervention. A collaborative approach, where humans guide and refine the output of these models, is necessary. This synergy allows for the completion of individual tasks that cumulatively result in comprehensive and sophisticated outputs, such as policy documents on risk management or committee briefing papers.
Looking Ahead
The dialogue below shows the conversation I had and as LLMs continue to evolve, we can anticipate a future where they handle more foundational writing and preparatory tasks, thus granting professionals more time for in-depth discussions and considerations of the topics at hand. This shift will undoubtedly enhance our approach to risk management and decision-making.
Conclusion
In conclusion, LLMs like GPT-4 are not just tools; they are partners in the quest for efficient and effective treasury and risk management. They enable professionals to focus on what truly matters – using their expertise to make informed decisions that shape the financial landscape thereby relieving us of the lower-level repetitive tasks that currently take up so much time.
Here's the conversation:
Me: “Please provide the most recent article from the Bank of England this year concerning the use of LLMs and AI in financial services”.
GPT-4: “The most recent article from the Bank of England this year concerning the use of Large Language Models (LLMs) and AI in financial services is titled "FS2/23 – Artificial Intelligence and Machine Learning," published in October 2023. This document is a feedback statement summarizing responses to a discussion paper (DP5/22) published in October 2022, aimed at understanding the implications of AI on the prudential and conduct supervision of financial firms.
The feedback statement indicates that the discussion paper received 54 responses from various stakeholders, including banks and industry bodies. Key points raised by the respondents include scepticism about the usefulness of a regulatory definition of AI, suggestions for 'live' regulatory guidance that adapts to rapidly changing AI capabilities, the need for ongoing industry engagement, and calls for more coordinated regulation concerning AI, particularly regarding data regulation, consumer outcomes, third-party models and data, and the complexity of AI systems. There's an emphasis on principles-based approaches, consumer protection, managing risks of bias and discrimination, and the challenges of integrating AI in financial services, including cybersecurity risks and the potential for AI misuse.”
Me: “please give me a summary of this document”
GPT-4: “The Bank of England's feedback statement "FS2/23 – Artificial Intelligence and Machine Learning" summarizes responses to the 2022 discussion paper on AI in UK financial services. Key points include:
Skepticism about a regulatory definition of AI, with a preference for principles-based approaches.
The need for 'live' regulatory guidance due to rapidly evolving AI capabilities.
Calls for ongoing industry engagement and better regulatory coordination, especially in data regulation.
Emphasis on consumer protection, addressing risks of bias and discrimination.
Concerns about integrating AI in financial services, focusing on cybersecurity risks and potential misuse of AI.
The document reflects diverse industry perspectives on adopting AI in financial services, focusing on regulation, risk management, and the evolving nature of technology.”
Me: “please expand on 'live' regulatory guidance”
GPT4: "Live regulatory guidance, as mentioned in the Bank of England's feedback statement, refers to a dynamic and adaptable approach to regulation that keeps pace with the rapidly evolving capabilities of AI technologies. Instead of static rules, this approach involves regularly updating guidance and best practices to reflect the latest developments in AI. This method allows regulators to respond promptly to technological advancements and emerging risks, ensuring that regulatory frameworks remain relevant and effective in a fast-changing landscape. This approach is particularly pertinent for AI, where the speed of innovation can quickly outpace traditional regulatory mechanisms.”