Fine Tuning for Risk Mgmt.
From Lehman Brothers to US Debt Ceiling: Leveraging AI and Fine-Tuned LLMs for Risk Management
28 May 2024
We explore how Fine-Tuning Large Language Models (LLMs) can revolutionize risk management in the Capital Markets industry, particularly during crisis events like the Lehman Brothers default, Euro Sovereign Debt Crisis, Brexit, and US Debt Ceiling debates.
Discover how Fine-Tuning LLMs to access Risk System APIs can generate custom risk reports in real-time using natural language queries. Learn how this innovative approach can help risk managers navigate tail events by automating risk data retrieval, exploring what-if scenarios, and providing scalable risk reporting solutions.
We also delve into a Proof of concept using CodeLlama and Manim, demonstrating how Fine-Tuning can be applied to generate complex visuals and animations. Stay tuned for a deep dive into building Fine-Tuning datasets and testing and validating Fine-Tuned LLMs for risk management.
We'll also introduce you to LoRA (Low-Rank Adaptation), a powerful method for adapting pre-trained language models that can dramatically speed up traditional Fine-Tuning approaches.
Don't miss out on this opportunity to learn how AI is transforming the Capital Markets industry and how you can leverage Fine-Tuned LLMs to enhance risk management in your organization.
Keywords: Fine-Tuning LLMs, Risk Management, Crisis Events, API, Custom Risk Reports, Manim, CodeLlama, Capital Markets, LoRA, AI.
#FineTuningLLMs #RiskManagement #CrisisEvents #CapitalMarkets #AI #CustomRiskReports #Manim #CodeLlama #LoRA
Video and Transcript
https://www.youtube.com/watch?v=WoittT72pgA
This is Richard Walker from Lucidate. Welcome back to our series on fine-tuning LLMs. In our first video, we explored how large language models or LLMs are not just for natural language processing but can also be used as app development frameworks. We discovered that with clever prompting, you can get a computer to do amazing things without writing a single line of code.
In our second video, we got a bit more technical and delved into how to run Hugging Face LLMs locally on your own machine or remotely on Hugging Face GPUs. Now, in this third installment, we're going to focus on fine-tuning. While there are many possible applications of fine-tuning, we'll get very specific in this video and look at a particular application of fine-tuning that has the potential to transform financial risk management in the face of crisis longtail events. We're going to look at teaching an LLM a risk management system's API or application programming interface.
Imagine this: a tail event hits the market like the Lehman Brothers default, the Euro debt crisis, Brexit, or the US debt ceiling. Suddenly, your risk management team needs to generate custom non-standard reports ASAP to navigate the turbulent waters. But the current process of building these reports is time-consuming and inefficient, leaving your firm exposed to significant risk. What if there was a way to generate these custom risk reports in real-time using natural language queries? That's where fine-tuning an LLM to access your risk systems API comes in. In this video, we'll explore how this innovative approach can revolutionize risk management during crisis events, providing your firm with the speed, efficiency, and adaptability it needs to weather any storm. So buckle up, grab a coffee, and let's dive into the world of fine-tuning LLMs for risk management.
Let's first talk about the challenges capital markets firms face during tail events. These are the low probability, high impact events that can shake the foundations of the financial world. Think about the Lehman Brothers default in 2008, which triggered the global financial crisis, or the Euro sovereign debt crisis in 2011, which threatened the stability of the entire European Union. More recently, we've seen uncertainty surrounding Brexit and the US debt ceiling. During these critical moments, risk management teams are under immense pressure to quickly assess the potential impact on their firm's portfolio. They need to generate non-standard risk reports that provide a clear picture of the firm's exposure, and they need to do it fast.
But here's the problem: the current process of building these custom reports is often time-consuming and inefficient. It typically involves manually gathering data from various sources, writing complex SQL queries, and then formatting the results into a presentable report. This process can take hours, if not days, depending on the complexity of the request and the availability of the necessary data. In the meantime, the market is moving at a breakneck speed, and every minute counts. Delays in generating these critical risk reports can leave firms exposed to significant losses as they're unable to make informed hedging decisions quickly enough to mitigate risk.
And it's not just the speed of report generation during these tail risk events. Risk managers often need to explore multiple what-if scenarios to assess the potential impact of various market movements. With the current manual process, running these scenarios can be tedious and time-consuming, further delaying the decision-making process. So how can we solve this problem? An answer lies in fine-tuning LLMs to access and invoke risk system APIs.
In the next section, we'll explore how this innovative approach can revolutionize risk management during crisis events. Here's a schematic of a risk system: we have our user interface with a series of inputs like sliders and drop-down lists for inputs and a series of outputs like tables and charts to display results back to the user. Behind the scenes, the UI talks to a series of different objects here, represented by different shapes. These will configure the system, receive information from databases, conduct risk analysis, and construct reports in the form of tables, graphs, and charts. The methods of these objects are effectively the system's API or application programming interface. These objects are typically written in high-level programming languages like Python, C++, or Java.
Well, guess what? LLMs are already pretty good at writing code in these languages. By fine-tuning an LLM on your API, in effect, giving it a bunch of natural language instructions written in plain English and the corresponding API calls, it will learn to build new reports on the fly. Sure, you can still keep the user interface for all your regular reports, but for these one-off tail event crisis reports, you can send your natural language request to the fine-tuned LLM and get the information you need.
So here's how it works: by fine-tuning an LLM on your risk system's API, you can essentially teach the model to understand and interact with your firm's specific risk data and reporting capabilities. This fine-tuned model can then act as a natural language interface to your risk system, allowing users to generate custom reports using simple plain English queries. Imagine during the US debt ceiling crisis being able to simply ask the fine-tuned LLM something like, "Give me a list of our top 10 counterparty parties measured by holdings of short-dated US treasuries. For each party, give me the net exposure and how the exposure and collateral value could change under extreme but plausible scenarios." The model would then translate this natural language query into the appropriate API calls, retrieve the necessary data, perform the required calculations, and present the results in a clear, well-formatted report all in a matter of seconds.
This approach offers several key benefits. First and foremost, it dramatically speeds up the process of generating these vital custom risk reports. Instead of spending hours or days manually gathering the data and writing SQL queries, risk managers can get the answers they need in near real-time simply by asking the right questions. Second, it makes the process much more efficient by automating the data retrieval and calculation steps. Fine-tuned LLMs can free up risk management teams to focus on higher-level analysis and decision-making. This is particularly valuable during crisis events when every minute counts and resources are often stretched thin.
Third, it allows for much greater flexibility and adaptability. With a fine-tuned LLM, risk managers can easily explore multiple what-if scenarios by simply modifying their natural language queries. This allows them to quickly assess the potential impact of various market movements and make informed decisions based on the most up-to-date information. Finally, AI never sleeps. This approach is highly scalable. Once an LLM is fine-tuned on your risk system's API, it can handle a virtually unlimited number of queries simultaneously without any degradation in performance. This means that your firm can generate as many custom risk reports as needed without having to worry about overwhelming your risk management or IT resources.
Of course, this all sounds great in theory, but how do we know it actually works in practice? In the next section, we'll look at a concrete example of how fine-tuning an LLM can be used to generate custom visuals and animations, a proof of concept that demonstrates the real-world potential of this approach.
So let me share with you a non-identical but similar problem that I've been working on. As many of you know, I use a tool called Manim to generate the animations and visuals for these Lucid explainer videos. Manim is an incredibly powerful Python library that allows you to create complex mathematical animations using Python code. It's been a game-changer for me in terms of creating engaging, informative content. But here's the thing: while Manim is great, it does require writing Python code to generate the animations, and as someone who spends a lot of time working on these videos, I often find myself thinking, "Wouldn't it be great if I could just provide the brief in natural language and have an LLM write the Manim classes for me?"
Well, that's exactly what I've done. I've fine-tuned a model to write Manim classes in Python using natural language instructions. For example, I can say something like, "Create an animation that creates a blue sphere and then a green cuboid with red edges. Move the sphere through the cuboid and then rotate the scene around the vertical axis." The fine-tuned model will then generate the necessary Manim Python code to create the specific animation.
To do this, I fine-tuned a version of Code LLaMA. Now, this is a great foundation as Code LLaMA is already pretty good at writing some fundamental Manim scripts, but by fine-tuning, I was able to take it to the next level. Code LLaMA, like all of the LLaMA models, has a JSON lines format for fine-tuning. Here's an example on your screen. In the fifth video in this series of six, we'll take a closer look at this format and also show how I was able to use my existing Manim classes and Manim scripts to help me come up with this file for fine-tuning and validation of the model.
This is a powerful proof of concept that demonstrates the feasibility of fine-tuning LLMs to access APIs and generate complex outputs based on natural language inputs.
Just like how I've fine-tuned a model to write Manim classes, you can fine-tune an LLM to access your risk system's API and generate custom risk reports. This process is remarkably similar. You provide the model with a clear natural language instruction, and it translates that into the appropriate API calls and data manipulations to produce the desired output, whether it's a Manim animation or a risk report. The underlying principle is the same.
Of course, fine-tuning an LLM is not a trivial task. It requires a well-curated dataset, careful model selection and training, and rigorous testing to ensure that the model is producing accurate, reliable results. In the final two videos of this series, we'll dive deeper into these topics, exploring how to build a fine-tuned dataset and test the fine-tuned model against baselines.
In video five, we'll take a deep dive into the process of building a JSON lines fine-tune dataset. We'll explore the key considerations when curating your data, including data quality, diversity, and relevance. I'll share some of the lessons I've learned from building my own fine-tune datasets, including the one I used for the Manim example. Then, in video six, we'll focus on testing and validating your fine-tuned model. We'll discuss the importance of establishing baseline performance metrics and comparing your fine-tuned model against these baselines. I'll walk you through the process of setting up a rigorous testing framework to ensure that your model is producing accurate, reliable results. By the end of these two videos, you'll have a clear roadmap for fine-tuning and testing an LLM to meet your specific business needs, whether it's generating custom risk reports, creating engaging visuals, or any other application that you can imagine.
But before we wrap up this video, I want to encourage you to stay tuned for the next installment in our series, where we'll be exploring another exciting technique called LoRA or Low Rank Adaptation. LoRA is a powerful method for adapting pre-trained language models to specific tasks, and it has some unique advantages for dramatically speeding up traditional fine-tuning approaches. Trust me, you won't want to miss it.
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Well, that's all for now. Thank you so much for watching and for joining me on this journey into the world of fine-tuning LLMs. I hope you're as excited as I am about the potential of this technology to revolutionize risk management and countless other applications in capital markets. Remember, the future belongs to those who embrace innovation and are willing to push the boundaries of what's possible. So keep learning, keep experimenting, and most importantly, keep innovating. Until next time, this is Richard Walker from Lucid signing off. Take care, and I'll see you in the next video.
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