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Hedging mortgages with swaps is a common practice for financial institutions looking to manage interest rate risks associated with their mortgage portfolios. When hedging mortgages, two primary strategies emerge: cash flow hedging and basis point value (BPV) hedging. These strategies are influenced by the accounting method employed, with accrual accounting favouring cash flow hedging and mark-to-market accounting favouring basis point value hedging. It is important to understand how these approaches impact the hedge ratios of a mortgage book. Cash Flow Hedging and Accrual Accounting: Cash flow hedging involves aligning the cash flows from the mortgage portfolio with the cash flows on the swap thereby hedging interest rate risk as fixed rate interest is converted to variable rate. Accrual accounting, which focuses on recognizing revenue and expenses when they are earned or incurred, is typically used to account for cash flow hedges. Under accrual accounting, the objective is to hedge the future cash flows of the mortgage portfolio, ensuring that the income generated from the portfolio remains relatively stable despite fluctuations in interest rates. This approach allows financial institutions to match the interest income received from the mortgage portfolio with the interest payments made on the swap contract. The hedge ratio for cash flow hedging is determined based on the projected future cash flows of the mortgage portfolio and the swap contract. It aims to offset changes in interest rates by ensuring that the interest income and interest expense remain closely aligned. This approach provides a level of predictability and stability in cash flow generation, thereby reducing the impact of interest rate movements on the financial institution's earnings. Basis Point Value (BPV) Hedging and Mark-to-Market Accounting: Basis point value hedging focuses on managing the market value of the mortgage portfolio relative to changes in interest rates. This approach is often employed when mark-to-market accounting is used, which involves valuing assets and liabilities based on their current market prices. With basis point value hedging, the hedge ratio is determined based on the BPV of the mortgage portfolio and the swap contract. The BPV measures the sensitivity of the mortgage portfolio's value to changes in interest rates. By actively adjusting the hedge ratio, financial institutions aim to offset the impact of interest rate fluctuations on the market value of the mortgage portfolio. Mark-to-market accounting provides a real-time view of the market value of the mortgage portfolio and the swap contract. This approach enables financial institutions to assess the effectiveness of their hedge positions and make adjustments as necessary. By managing the risk through basis point value hedging, financial institutions can protect the market value of their mortgage portfolios and mitigate potential losses arising from interest rate movements. The choice between cash flow hedging and basis point value hedging for hedging mortgages with swaps depends on the accounting method used and the specific objectives of the financial institution. Accrual accounting favours cash flow hedging, which emphasizes stable cash flow generation, while mark-to-market accounting favours basis point value hedging, which focuses on managing market value and potential losses. Financial institutions must carefully evaluate their risk management objectives, regulatory requirements, and accounting practices when determining the most suitable hedging strategy for their mortgage portfolios. By understanding the nuances and implications of cash flow hedging and basis point value hedging, institutions can effectively manage interest rate risks and optimize their overall portfolio performance. Perspective The first time I encountered a variation on this risk was in the early 1990s, while working at a bank, I faced an unusual situation while hedging fixed-rate bonds with swaps. The challenge emerged because we were hedging bonds issued by Brazil and Mexico, which traded on a higher yield curve due to their credit risk. The bank's system recommended a hedge ratio of $10m bond to $7m swap, deviating from the conventional 1:1 ratio that aligned cash flows on an equal basis. This unexpected result gave two different but potentially correct hedge ratios depending on the purpose of the hedge. A 1:1 ratio was appropriate for a buy and hold, accrual investment and a 1:0.7 ratio was suited to a traded mark-to-market situation. It should be noted that this latter dynamic hedge would need constant rebalancing as a result of changing interest rates. At first sight, this may appear a million miles from mortgage hedging but the challenges encountered in hedging these bonds bore similarities to those faced in mortgage hedging, requiring financial professionals to manage interest rate risks and balance cash flows or market values effectively. This is brought home by a more recent encounter with mortgage hedging. In this situation, the mortgages had uncertain maturities, although they were predominantly long-dated. The bank, under regulatory pressure, was required to mark-to-market (MTM) the mortgages using a yield curve that reflected the relevant credit risk. Similarly, the swaps were also marked-to-market. Despite employing cash flow hedging with a 1:1 basis for the swaps to hedge the mortgages, significant profit and loss fluctuations occurred. As interest rates fell, the basis point values of the swaps increased at a faster pace than the mortgages due to differences in convexity. Since the bank paid a fixed rate on the swaps, it experienced increasing losses as interest rates declined. This situation presented a dilemma: should the bank shift from managing the risk through cash flow hedging to basis point value hedging? This dilemma consumed considerable management time as they grappled with finding the optimal solution. Ultimately, a compromise was reached, acknowledging the imperfect nature of the situation. The chosen hedge ratio fell between the two extreme scenarios. This compromise recognized that hedging is not a black and white issue. Real-world factors such as regulation, accounting practices, capital requirements, and management perceptions all play significant roles in shaping the approach to hedging. By understanding the nuances and trade-offs involved, financial professionals can navigate the complexities of hedging in a more informed manner. Balancing the objectives of risk mitigation, accounting requirements, and capital optimization is key to achieving successful hedging outcomes in the real world.

In this article, I explore the capabilities of GPT-4 in assessing a firm's interest rate risk. My objective is to provide a practical perspective on how advanced models like this can be applied in real-world scenarios. The insights garnered along the way not only shed light on future trajectories but also highlight potential pitfalls to be wary of. The Starting Point I've put together a straightforward spreadsheet that illustrates the typical interest rate exposure a small bank or building society might encounter. This often takes the form of long-dated fixed-rate assets funded by shorter-dated floating-rate liabilities. In such a setup, profits materialize if interest rates fall, while losses ensue if they rise. Such exposure is typically managed using various techniques. One of the most basic is the "gap report," which relies on a basis point value or delta calculation. For this analysis, we'll keep things uncomplicated. I believe a simpler approach allows us to better comprehend how the model can assist us. The initial step involves asking GPT-4 to reformat the information, ensuring it's presented in a manner the model can easily interpret and engage with. GPT 4: Next, I prompt GPT-4 to perform a DV01 analysis. It's important to note that I provided no additional cues, purely because I wanted to gauge its intuitive understanding of my request. Impressively, GPT-4 not only grasped the essence of what I intended but also laid out a set of assumptions it would be operating under. GPT 4: I ask it to continue. GPT 4: GPT-4 not only presents the DV01 calculation but also articulates its results in plain English, bringing to light some potential limitations of such an analysis. However, an unexpected discrepancy arises. Based on my calculations, I anticipated a risk figure around £106,000, but GPT-4 returns an estimate of £138,000. This raises the question: is there an error in our methods? A closer examination reveals methodological differences. GPT-4 employs a textbook duration approach, whereas I utilize a more hands-on discount function method. This latter technique involves adjusting the yield curve by a basis point. To determine if this variance in methods is the root of our discrepancy, I prompt GPT-4 to emulate my approach and provide its results. GPT 4: Our results are now closely aligned, though slight differences persist. Without diving too deep, these variances likely stem from GPT-4's preferred approach versus mine. Admittedly, this can sound rather technical, but there's an essential lesson to be drawn from it. GPT-4 has adopted a methodology that, while not incorrect, might diverge from prevalent industry practices. It has behaved akin to a diligent academic. This highlights a need to watch out for such outputs: they're not wrong, just potentially misaligned with expectations. Once such disparities are identified, GPT-4 can be directed to adjust its response in the context of specific industry practices, which it does commendably. In a novice's hands, GPT-4's output might be accepted without scrutiny, but an experienced practitioner can ensure the proper alignment. This raises a contemplation: given such a straightforward example, could LLMs potentially replace the conventional software tools used for risk management? It's a question I aim to delve into in the future, but the winds of change seem to be stirring. Having conducted some technical analysis, let's pivot to an area where GPT-4 truly shines: writing. Could it draft a memo to my supervisor elucidating the risk we've discussed? Let's find out. GPT 4: As anticipated, the memo is succinct and zeroes in on the primary exposures, setting a clear agenda for subsequent actions. Impressively, this was achieved in a single attempt by GPT-4. The memo effectively highlights the main concerns, suggests potential resolutions, and points to further analyses that might be beneficial. To provide some perspective, the quality and depth of the memo are such that I would expect it from someone with several years of experience in the field. What are the implications? The ability to rapidly produce high-quality documents undoubtedly boosts productivity. This efficiency implies we can either accomplish more with our current workforce or, in the future, require fewer personnel for the tasks we handle today. Many treasurers and risk managers—no offence intended—aren't always the most adept at drafting reports. Many would welcome a tool that both expedites and elevates the writing process. In fact, with GPT-4's assistance, one might be tempted to entrust the role of a risk analyst to an eager new graduate. Yet, I'd advise caution, especially recalling our earlier hiccup with the calculation method. While GPT-4 can certainly elevate the capabilities of less experienced individuals, it doesn't replace the nuanced understanding and seasoned judgment that veterans bring. It's a balance that organizations must thoughtfully consider as they move forward. Considering the complexities, it might be beneficial for management to grasp the nuances between the two risk calculation methodologies. To this end, I decided to test GPT-4's adeptness in elucidating the distinctions between the two approaches. GPT 4: While the output from GPT-4 provides a solid foundation, it may not be immediately ready for distribution. Some fine-tuning would be necessary, but the overarching essence and direction are promising. With more precise prompts, the quality of the output could be enhanced swiftly. To further illustrate GPT-4's capabilities, I tasked it with crafting a high-level paper. This paper would explain the rationale for shifting from one risk methodology to another—a task treasurers and risk managers often confront. It becomes evident that GPT-4 offers an efficient and streamlined approach to draft content tailored for diverse audiences. GPT 4 It’s short and to the point but not entirely what I was expecting as it’s now brought in issues (like non-parallel shifts) that I wasn’t anticipating. But it was a one-shot request that could easily be refined by further prompting. Key Takeaways from the Exercise: Capability and Application: GPT-4 can craft risk reports reminiscent of real-world standards. While this exercise isn't exhaustive proof of handling a real-life balance sheet, there's a strong indication that, given the right input and guidance, GPT-4 can produce results comparable to traditional systems. Understanding Computer-Generated Output: Accepting computer-generated information unquestioningly can lead to pitfalls. GPT-4's edge lies not just in generating numerical data but in framing it within coherent, natural language narratives. This offers remarkable benefits but requires users to approach with experience and discernment, ensuring they don't fall into potential traps. GPT-4's Writing Prowess: As touched upon in prior sections, GPT-4 excels in articulation. It can swiftly render intricate explanations in chosen formats. This proficiency, especially in saving time and elevating report quality, presents a compelling case for adopting the enterprise version of the model. Considering the time saved and the enhanced clarity in reports, the cost of GPT-4 can be quickly justified. Future Outlook: GPT-4 and LLMs are still in their nascent stages, with the latter being accessible for under a year. Yet, the transformative potential of these models is evident. They herald a shift in risk evaluation and reporting. Compared to the rigid algorithms of traditional systems, LLMs' ability to process and query data in natural language, especially given banks' vast unstructured data, signals an imminent paradigm shift. This evolution might unfold more rapidly than anticipated, perhaps even outpacing the transformative journey of the internet.

In today's rapidly evolving technological landscape, large language models (LLMs) stand as a testament to the strides we've made in artificial intelligence. However, harnessing their full potential in the professional realm remains a challenge. While tech companies excel in creating these advanced models, their grasp of the nuances of specific business sectors often falls short. To truly capitalize on the impending AI revolution, it's imperative to discern how LLMs can be integrated effectively within our workplaces to enhance both the quantity and quality of output. As insiders in our respective industries, we possess unique insights that place us in an advantageous position to experiment with AI applications. Rather than waiting for the perfect solution, it's often more rewarding to dive in headfirst, exploring first-hand the benefits and limitations of these models. In this article, I aim to shed light on one specific use case: managing basis risk. For those unfamiliar, basis risk, in its most basic sense, refers to the discrepancy when one pays and receives interest rates based on differing reference indices. It's a multifaceted risk, but for the sake of clarity, I've distilled its essence. Delving into how an LLM, specifically GPT-4, can aid in analyzing this risk has yielded some enlightening findings. Facing Initial Hurdles My exploration began with a straightforward approach: uploading a spreadsheet for the model to interpret. However, this method immediately ran into some snags. Surprisingly, GPT-4 struggled to comprehend the data I provided. This was unexpected, especially given the rave reviews I'd encountered regarding its data analysis capabilities. Switching to the default mode—bypassing the advanced settings—yielded better results. GPT-4 seemed more adept at evaluating the data in this configuration. Interestingly, I observed that spreadsheets, especially those with statistical data, don't mesh well with GPT's advanced data analysis. The model often requires significant prompting and guidance to navigate them effectively. Undeterred, I ventured further by using the advanced data analysis feature. I prompted GPT-4 to convert the spreadsheet into a format it found best to use and thereby facilitate a better conversation about the findings. The outcome? A vast improvement, which I'll delve into in the subsequent sections. GPT4: Diving Deeper into Risk Analysis I set the model on a task to gauge the risk in terms of delta value. Specifically, I wanted to understand the implications if the interest basis shifted by a mere 0.01%. GPT-4 accurately pinpointed the net basis risks, producing a potential profit and loss figure that could arise from such a change. Beyond just the numbers, the model went on to elucidate the constraints and limitations inherent to its analysis, providing a better understanding. GPT4: I then posed another challenge: a scenario analysis. I proposed a situation where the base rate decreased by 0.5%, while Libor increased by 0.25%. Without missing a beat, GPT-4 crunched the numbers, laying out the potential repercussions on profitability. Just as before, it highlighted certain caveats to consider in the analysis. GPT4: However, beyond the raw numbers and scenarios, understanding the 'why' is equally crucial. I asked the model to decipher the reasons behind such disparities in interest rates. Our exchange offered insights into the economic dynamics that might sway a balance sheet, rounding out a holistic picture. GPT4: Exploring Hedging Strategies with GPT-4 Our conversation progressed to potential strategies for hedging the identified risk. While GPT-4 offered suggestions, they leaned more towards the generic, lacking the specificity I had hoped for. It felt as if the model was, metaphorically, hedging its own bets, casting a wide net rather than zeroing in on a direct solution. Maybe I should have probed more incisively, pressing for a more exact answer. It's an avenue I plan to explore more deeply in future discussions on hedging techniques. GPT4: One common approach to managing basis risk is adjusting the margin on financial products. For instance, one might consider reducing savings rates while raising borrowing rates. However, this strategy is far from straightforward, carrying its own set of advantages and pitfalls. When I prompted GPT-4 to unpack these, it responded, touching on several pivotal concerns. GPT4: To cap off our session, I asked the model to draft a memo for the board. The objective was to succinctly capture the risks we had dissected and propose potential measures to mitigate those exposures. While GPT-4's initial draft was competent, it would undoubtedly benefit from some fine-tuning. Here’s the memo. GPT4: Key Takeaways using GPT-4 in this context From this deeper dive into the capabilities of GPT-4, several insights emerged: Embracing the Future: Any organization yet to experiment with GPT, even in a sandbox environment, is potentially missing out. The trajectory of GPT's impact remains somewhat nebulous, but its utility in swiftly identifying, analyzing, and elucidating risks is undeniable. By rapidly generating documents and explanations that would conventionally drain resources, GPT offers a glimpse into the future of business operations. Balancing Expectations with Reality: Banking on AI to have all the solutions right out of the box will lead to disappointment. The quality of GPT's output can oscillate - sometimes it's stellar, other times less so. It underscores the importance of leveraging your own expertise to sift through the results. Adeptness in Economic Analysis: GPT-4 consistently shines in its ability to dissect economic scenarios and their potential ripple effects on businesses. This isn't surprising, given its training on a wealth of textual data. Versatile Documentation: GPT's proficiency isn't limited to drafting memos for board members. It can adeptly tailor content to resonate with diverse audiences, presenting a promising avenue for customizing internal communications. In essence, while GPT-4 heralds a new frontier in AI-driven solutions learning how to apply it will be the big challenge going forward.

After a bit of practice, I decided to put it to work on something practical and asked it to generate a series of reverse stress scenarios suitable for a building society. The results were good, but there was nothing particularly new. I then asked it to be more adventurous, and one of the things it came back with was the business impact of ChatGPT - you can see the result below. What surprised me was the ability for it to be creative and quickly produce something that's useful. If I wanted to, I could drill down into every aspect of what it has written. What's clear is that AI is coming for knowledge work, and the change may be relatively fast. It's hard not to think that things will look very different within five years, so this case study is something that decision-makers need to consider. Case Study: Building Society X - Reverse Stress Test on Automation Impact: ChatGPT and Workforce Displacement Background: Building Society X, a reputable building society with a focus on providing personalized financial services, recognizes the potential impact of AI and automation on its workforce. The top team, consisting of the CRO, CEO, and Chairman, acknowledges the need to conduct a reverse stress test to assess the society's vulnerability to job displacement caused by the implementation of ChatGPT, an advanced AI-powered chatbot. The society aims to navigate this transition while ensuring the well-being of its employees and maintaining a high level of customer service. Objective: The objective of this reverse stress test is to evaluate Building Society X's vulnerability to job displacement resulting from the introduction of ChatGPT. The test aims to identify potential risks arising from automation, assess the impact on the workforce, and develop strategies to mitigate the potential adverse effects on employees and customer service. Scenario: The reverse stress test focuses on a hypothetical scenario where Building Society X implements ChatGPT to enhance customer service efficiency and streamline operations. ChatGPT is an AI-powered chatbot capable of handling customer inquiries, providing financial advice, and performing various transactional tasks. The scenario assumes a significant adoption of ChatGPT, resulting in the potential displacement of a substantial number of employees across customer service, administrative, and operational roles. Process: Scenario Development: The top team, in collaboration with relevant stakeholders, develops the specific parameters of the scenario, considering the potential scope and scale of ChatGPT's implementation. They examine industry trends, technological advancements, and customer preferences to ensure the scenario reflects a plausible future state for the building society. Risk Identification: The team conducts a comprehensive assessment to identify potential risks associated with workforce displacement. They consider the impact on employees whose roles may become redundant or significantly altered due to automation. They also assess the potential effects on employee morale, customer satisfaction, and the society's reputation as a customer-centric organization. Impact Analysis: The team evaluates the potential impact of job displacement on Building Society X's workforce, including aspects such as employee retention, skills gaps, and workforce morale. They assess the potential consequences on employee engagement, productivity, and the ability to deliver personalized financial services. Additionally, they analyze the potential impact on customer satisfaction and loyalty, considering the changing dynamics of human-AI interactions. Workforce Transition Strategies: The team explores various strategies to mitigate the adverse effects of workforce displacement and ensure a smooth transition. They identify opportunities for reskilling and upskilling affected employees to prepare them for new roles that leverage their existing expertise while incorporating technology. Additionally, they consider initiatives to enhance employee engagement, such as fostering a culture of continuous learning, promoting collaboration, and providing support for career development. Customer Experience Optimization: Recognizing the importance of maintaining exceptional customer service, the team devises strategies to optimize the customer experience in a hybrid human-AI environment. They evaluate how ChatGPT can enhance service efficiency, provide seamless customer interactions, and ensure that customers receive personalized assistance when needed. The team explores ways to balance the advantages of automation with the value of human interaction, emphasizing the society's commitment to customer-centricity. Change Management and Communication: The team formulates a comprehensive change management plan to effectively communicate the adoption of ChatGPT and address potential employee concerns. They establish transparent channels of communication to keep employees informed about the automation implementation, the rationale behind it, and the support available for them during the transition. The plan emphasizes open dialogue, employee feedback mechanisms, and proactive measures to mitigate any negative impact on the workforce. Conclusion: By conducting a reverse stress test on the impact of ChatGPT and workforce displacement, Building Society X's top team gains valuable insights into potential risks and vulnerabilities.

AI is coming for knowledge work, the problem is that we don't know how it will affect us and the businesses we work in. It has the potential to rapidly alter well-paid white-collar jobs, and that's something we have little choice over. However, we can experiment with the technology and learn new ways to do things. The speed at which these changes are happening means that they will affect strategic decisions, and boards will need to respond accordingly. One of the major issues is the disconnection between those who understand AI and those who make strategic decisions. This has the potential to lead to some very unfortunate consequences, not just for the business but for society. Any rapid adoption of AI that leads to higher unemployment will be socially disruptive, but that's not all. Many employees find fulfilment in their work, and if you automate the creative and interesting aspects of what they do, you risk adversely affecting morale, reducing engagement, and creating a weaker culture. But this doesn't mean ignoring what's going on. AI can increase productivity and creativity, and if it can be successfully applied to your business, market competitors will threaten your long-term viability if you don't utilise it. I know I struggle with AI because it has seemingly come from nowhere, and it has just been let loose on the public, largely as a result of having a natural language interface (just like ChatGPT). I don't think I'm alone in discovering that it's a whole lot better at doing things than expected, and as every day passes, I am more impressed. If we assume that we are all starting from a low base, how do you build up the mental muscle that allows you to understand enough to make sensible strategic decisions? Part of the answer is surely experimentation. If you try ChatGPT you will find it's straightforward, and you can learn some practical skills just by varying the prompt. Taking this a step further, you could build a safe space where you could collectively work with colleagues to improve executive AI fluency. This is not the same as coding; it's about developing a mental mindset that gives you insight into what AI can do for your business. Practical suggestions could include taking a document and repurposing it, asking it to explain some data trends, and building a new advertising campaign but don’t use proprietary data until you are clear about where it goes. It doesn't take a lot of imagination to see that just about every part of a board's information deck could, in the future, be AI-generated and nuanced for the reader, who would have the ability to summarise and drill down as required. But that's only the start. In the industry I'm familiar with, financial services, there's a huge amount of proprietary data held across a multitude of systems. That data is frequently in documents, contracts, and spreadsheets, making it very hard to access. There's also a deep history of legacy data that resides somewhere, and all of this is held together with tape and string. Now, with AI this data becomes extremely powerful. It can help you price and manage risk like it's never been done before. Underwriting credit and insurance risk immediately springs to mind. There's also a separate business case that takes data and sells access to it through an interface like a chatbot. This may sound fanciful, but it's something that's already being done by firms like Bloomberg. These two business models won't suit everyone, but there is a third way: to pick the low-hanging fruit and implement AI as and when it becomes commercially available from third parties and embedded in systems and applications that you already use. Examples include using chatbots for customers and implementing fraud detection systems to identify irregular patterns of behaviour. How you go about this depends on whether you can make smaller incremental changes leading to increased efficiency or if you are pushed into much larger, rapid changes to respond to competitors. There are also limitations to what can be achieved. In a regulated industry, you need to be aware of what data you can use and how you can use it. If you get this wrong, there are serious financial and reputational risks; therefore, banking cannot be as entrepreneurial as many lightly regulated businesses. Furthermore, regardless of the path you take, the results are only as good as the information used in the model, and we know that a lot of banking data has been subject to manual intervention which embeds errors. It also seems evident that if decision-makers can't explain how a model is trained, how it works, and what its limitations are, the regulator is not going to be happy. This is particularly important when considering GDPR and the principles of treating customers fairly, as well as the application of complex risk management techniques. There's no doubt that enterprising employees are already using AI in their personal workspaces, and for better or worse, it will leak into your business. Providing them with guidance on what is acceptable is a good place to start. Overall, AI experimentation and understanding are essential in navigating the changing landscape of knowledge work. By embracing the potential of AI, businesses can increase productivity, creativity, and strategic decision-making. However, it is crucial to consider the potential consequences, such as job displacement and employee morale, and find ways to mitigate them. With proper experimentation, collaboration, and a thoughtful approach, AI can become a powerful tool to drive growth and success in the business world.

It seems that everyone is utilising artificial intelligence. However, the narrative often contrasts sharply when I engage in personal conversations. For instance, I recently learned about a bank investing millions in developing models. Yet, when talking with its employees, many claim they aren't using or even aware of such tools. It's astonishing to see such a sophisticated strategy that overlooks the basics. Large Language Models (LLMs) like GPT-4 are democratising AI, granting anyone the potential to enhance their tasks — the only requirement is the willingness to embrace it. My work habits have changed. I used to search online or ask colleagues for answers. Now, I often turn to Large Language Models (LLMs) like GPT-4. This change comes from trying them out and seeing their efficiency. They save time and often give better results. So, I wonder, why aren't more businesses using tools like this? Inertia: Human nature tends to favour the familiar. We often gravitate towards tried-and-tested methods, which can prevent us from fully understanding or appreciating the capabilities of new innovations. Time Constraints: Our existing commitments often demand immediate attention, leaving little energy or inclination to venture into the unfamiliar territory of new tools and technologies at the end of the day. Turf Wars: Technology adoption can often be perceived as an IT-centric endeavour. This perception can lead to extensive processes like planning, development, testing, roll-out, and sign-offs. Solutions that bypass these stages can introduce inter-departmental friction. Access Restrictions: Most workplaces have strict protocols around software installations and gaining permission often involves multiple layers of approval, making spontaneous adoption challenging. Security Concerns: Data is a valuable asset. The threat of breaches or leaks can deter companies from trying new tools. Unauthorized access can benefit competitors and prove costly and damaging to a company's reputation, especially for regulated entities. Misinformation Risks: LLMs, though powerful, can sometimes generate inaccurate or misleading narratives. Associating your name or brand with such content can have unintended consequences. Even with these challenges, it's important to note that progress has its ups and downs. With the right planning and approach, we can overcome these obstacles and pave the way for new ideas and growth. Many businesses already rely on third-party services for data management, so outsourcing is not a novel concept. There's an underlying principle of mutual interest at play. Service providers are just as invested in preserving the integrity of the data they manage. Any breach, misuse, or unauthorised copying of client data would jeopardise their own business model and reputation. Do LLMs occasionally generate incorrect, biased, or misleading outputs? Certainly. But this doesn't render them unreliable. We have safeguards in place, one of which is human oversight. Just as we review spreadsheet results for accuracy, we can and should scrutinise LLM outputs to ensure they align with our expectations. Blindly accepting any tool's output without questioning or validation can lead to issues down the road. It's disheartening to see many businesses overlooking the potential of AI. In doing so, they also disadvantage their employees. As AI becomes increasingly integrated into industries, professionals unfamiliar with its capabilities risk being outpaced by those who can use it. Deploying these models can yield immediate benefits, and the investment of time and effort is minimal. A structured approach, spearheaded by a board-empowered project sponsor, can streamline the integration process. Here's a simple plan: Sign Up: Register for GPT-4 or its enterprise version. Empower the Workforce: Distribute access among your employees and encourage them to experiment. Feedback Mechanism: Set up a centralised system where users can document their experiences, uses, and feedback. This will provide invaluable insights into the model's real-world applications. Training: Although intuitive, a brief demonstration on how to effectively prompt the model can save hours in the long run. Review & Adapt: After a few weeks, review the innovative applications your team has discovered. The financial incentives alone, such as the time saved in drafting documents, make this endeavour worthwhile. The proposition is straightforward and cost-effective. Why not take the leap? Getting Started with GPT-4 While GPT-4 doesn't come with a manual, here are some steps: Registration: Opt for the paid version, priced at $20/month for individual access Switch it on: Navigate to settings and grant yourself access Experiment: Use the prompt window to test various queries. Toggle Features: Explore different modes to familiarise yourself with the model's capabilities. From here, it's all about learning through experience.

In previous articles, I've discussed some of the economic benefits of using LLMs, as well as the problems concerning hallucinations and how simple steps can mitigate their effects. After learning about the applications of LLMs in treasury and risk management, you might want to try them yourself. However, if you work for a bank or building society, it may not be that straightforward. Many firms have prohibited the use of these models. Let's explore what's causing this and whether there are steps we can take to move forward. (The following discussion concerns the use of LLMs in treasury and risk management. This means that the model's use predominantly applies to information used internally and, in the business, not externally with customers). Challenges in Implementing LLMs Why have some firms not allowed their employees to use GPT-4, or such, at work? This list is not exhaustive, but you may recognise some of these challenges in your own business. Let’s look at three issues of concern: Risk Management: This includes legal issues, data security, privacy, and the risk of misinformation from hallucinations. It's about the apprehension of potential negative consequences of using LLMs, including legal complications, data breaches, and regulatory non-compliance. In sensitive businesses like financial institutions, these risks require careful appraisal. At no point can you say the risk is nil and that's what frightens people, how can you quantify what you are getting yourself into? You can't it's a matter of using judgment - something we do all the time in financial markets. Technological Understanding and Trust: A lack of knowledge and a gap in understanding of what LLMs are capable of, their potential benefits, and limitations. This lack of familiarity or mistrust results in a wait-and-see approach, with decision-makers hesitant to adopt new technology until it is widely accepted within their peer group. Organisational Culture and Resistance to Change: This is related to operational inertia and a reluctance to adopt new technology, especially when integrating LLMs into established workflows. Many traditional firms resist change, not just because of a lack of immediate perceived benefits but also because it suits the short-term interests of those involved. Practical Steps The economic argument for using Large Language Models (LLMs) is compelling. Even the simplest application of these models to improve workflow can boost efficiency by at least 10%, leading to savings in headcount and costs. If you are experiencing resistance, three practical steps may help: Understand where the blockage is: Identify the concern. Who is raising it? And, what do they need to know to become comfortable? Address individual use cases: For example, in treasury, if you want to use an LLM to prepare for ALCO meetings, put forward a case. Show how an LLM can repurpose something you write for a different audience - as I've discussed in a previous article. Form an AI committee for collaborative decision-making: Initiating an AI committee within the firm brings together diverse viewpoints. This committee will serve as a platform to assess AI's varied applications and implications in the business and is a path towards drafting an AI policy – something regulated industries need to take seriously. Need for Policy The catalyst likely to trigger change is whether your peer group starts using the technology. There's no doubt that adoption will accelerate once this happens. Starting now and putting things in writing helps to think through the unique circumstances affecting your firm. As these technologies evolve, expertise is key to enhancing the workflow and efficiency and maintaining a competitive market position. Security will be high on the agenda, including the potential for accessing LLMs through an Application Programming Interface (API) and ensuring that sensitive data is not used for training by the LLM provider. In collaboration with the IT department, the committee will need to assess encryption methods. These methods are essential for securing data transmitted over the Internet, particularly when accessing LLMs through APIs. Furthermore, the committee will need to oversee data storage, especially with external cloud service providers like Amazon, Microsoft, and Google, as well as LLM providers such as OpenAI and Anthropic. There is also a need to be aware that data models (like LLMs) could exhibit bias. Could this translate into active decision-making without being aware? Ironically, firms that currently don't allow LLMs to be used can't stop employees from using these models to solve work problems at home. By not addressing the issues, the business has, by default, turned a blind eye to what employees are doing. That's what I call a risk.

Large Language Models (LLMs) in Treasury and Risk LLMs, or Large Language Models, are advanced artificial intelligence systems designed to understand, generate, and interact with human language, mimicking natural human communication. They are a subset of machine learning models within the field of natural language processing (NLP). In previous articles, I have discussed their use in various tasks, including: Streamlining Communication Writing for the recipient Undertaking research Stress and scenario creation Preparing for committees & analysing risk Adding narrative to presentations and record-keeping These tasks, which we regularly undertake, are time-consuming and, in some cases, repetitive. LLMs excel here, thereby freeing up our time and thought, allowing us to concentrate more on what is important and productive. Even if no further progress were made in developing these models, their impact on treasury and risk management, and indeed the whole firm, would be significant. We are only just learning how to use them in business. Key Findings from the UK Department for Education Report In November of last year, the UK Department for Education published a report titled "The Impact of AI on UK Jobs and Training." This report outlines AI and LLMs' influence on the UK labour market. The key findings are: Occupational Exposure: Professional occupations, especially in finance, law, business management, and teaching, are more exposed to AI. Large language models are particularly relevant in these fields. Sectoral Impact: The finance and insurance sector is most exposed to AI, followed by the information and communication, professional services, property, public administration, defence, and education sectors. Geographic Variations: Workers in London and the South East have the highest AI exposure, reflecting the concentration of professional occupations. The North East has the least exposure, but overall geographic variation is smaller than that observed across occupations or industries. Qualifications and Training: Employees with advanced qualifications, especially in accounting, finance, and economics, are typically in jobs more exposed to AI. Conversely, those with qualifications in building, construction, and transportation operations are least exposed. The analysis emphasises that AI is likely to augment rather than replace many jobs, with varying degrees of exposure across different occupations, sectors, and geographic regions. Upon first reading, I was surprised at the level of impact AI could have on us. However, after reflection, LLMs allow us to produce more output from the same resources or the same output from fewer resources. The implications are profound in terms of the workplace and more widely society. A Ridiculously High Return on Investment My own experience with LLMs has been more of an experiment in what they can do, particularly in the field I am familiar with. A simple conclusion I have come to is that the $20 subscription to GPT-4 is probably the best investment a bank or building society can make for its employees in Treasury and Risk. At first glance, this may seem extravagant, but a closer look makes it hard to ignore. One of the highest costs for financial firms is employing staff. With this in mind, let's look at things further. I've made some broad assumptions, so what follows is an estimate, a conservative one at that, because I have only considered the use of LLMs for document preparation. I’ve assumed an hourly rate for a skilled person in this area of £40/hour (based on a 46-week year, working five days a week, eight hours a day, totalling 1,840 hours per year, with a remuneration package of £75,000). Let’s also assume that 10 hours a week are spent on writing and processing documents, a common scenario given the workload for internal meetings and management reporting. From my experience, GPT-4 can easily halve the time taken for this, saving 5 hours per week. Over the year, this amounts to 230 saved hours with a value of approximately £9,200 or 12% of the salary bill. Even if you deduct the 20-25 hours needed to get proficient in using LLMs in your business (a process that can be significantly accelerated with the appropriate training). The first-year cost saving is over £8,000 or 10%. A More Positive Outlook You can now see why the report from the Department of Education highlights the implications of AI on banking and finance, especially in the southeast of England. While this may be true, I see a more positive impact on the workplace. Colleagues working in treasury and risk are constantly under pressure to deliver and things fall behind. The solution, hiring more staff, can be tricky, and many firms have headcount limits. Spending a small amount on LLMs will go a long way to alleviate some of these problems without breaking any headcount restrictions. It's also that much easier to sign up for an LLM than to go through the process of hiring.

What they are and what causes them Hallucination is a term used to explain the phenomenon of a Large Language Model (LLM) producing incorrect outputs. LLMs, like GPT-4, are trained on vast datasets consisting of text from the internet. They learn to predict the next word in a sequence, making them powerful tools for generating human-like text. However, this strength also leads to a key vulnerability known as 'hallucination,' where the model generates plausible but factually incorrect or misleading information. This often stems from the model's reliance on patterns in the data it was trained on, rather than an understanding of factual truth. Hallucinations are caused by: Overgeneralization: LLMs sometimes overgeneralise from their training data, leading to outputs that are too broad or generic, and at times, inaccurate. Data Quality: The training data may contain inaccuracies, biases, or outdated information, which the model can inadvertently learn and replicate. Context Limitation: LLMs have a limited context window (the number of words they can consider at one time), which can result in missing or misunderstanding key parts of an input query, leading to incorrect outputs. Lack of Real-World Understanding: Unlike humans, LLMs don’t have real-world experience or common-sense reasoning. They operate purely based on statistical patterns in the data they’ve seen. Not entirely detrimental In the context of treasury and risk, hallucinations are seen as detrimental because they produce unexpected and potentially erroneous results. However, it's worth asking whether hallucinations are intrinsic to the nature of predictive text-type models, which are essentially statistical models that generate output based on gathered data. We can prompt this in various ways to stimulate the output, and I believe the process of hallucinations is almost a natural part of LLMs. In many respects, we can benefit from this when considering risk management. Many of the reports we run, whether linear or scenario-based, are generated by our ingrained biases, our thoughts on how the world works, and regulatory norms. A savvy risk manager should consider using these models to explore some of the more nuanced features of risk in the business by questioning whether the reports currently being generated have any obvious holes or biases within them. Working with LLMs to generate specific stress-type scenarios can help investigate whether there are weaknesses in the business model. To this end, I've specifically addressed this issue in an earlier article in this series titled "Simple Uses for LLMs - Stress and Scenario Creation," which uses examples to create different stress scenarios and mitigants that can arise. When using these LLMs in the context of treasury and risk management, the issue of over-generalisation tends to be more prevalent when you start from scratch and ask the model to generate output without giving much thought to the prompting process, something we have control over. Nevertheless, I have regularly experienced overgeneralisation in the responses from GPT-4, particularly when I've asked broad questions about risk. The data quality issue is pertinent when dealing with treasury-type issues. The general model used by the LLM has been trained from the internet, and you have to be wary of some of the potential outputs. In the case of conducting specific analysis for the business, it is likely that you will be including your data either through an API or in the prompt window. This means that the data needs to be clean; there's nothing new in this – garbage in, garbage out. The issue concerning context limitation is one that I frequently experienced earlier last year. However, the new and updated models are now capable of much longer context windows, and I see this as a less important issue going forward. Experience counts Lack of real-world experience and understanding is a problem for these LLMs. They currently do extremely well in analysing situations, but they often miss the nuances that we see and have gained experience in while working in treasury and risk management. In extreme cases, the output can range from amusing to outright harmful. Let's illustrate this with an example where I've experienced a hallucination using GPT-4, and discuss some of the implications and consequences. When analysing a gap report, GPT-4 focused on liquidity risk management and prioritised it. This was somewhat surprising, and although insightful, it wasn't quite what I had in mind. Without my experience in markets, it would have been difficult to detect the subtle difference between liquidity and interest rate risk, as the two are often closely related but not the same. To overcome the problem, a specific prompt focusing on interest rate risk was necessary. At a later juncture, I asked GPT-4 to provide a delta report. Normally, you would expect a delta report to represent a shift in the yield curve leading to a change in the present value of the portfolio or balance sheet in question. GPT-4, however, provided a report based on the nominal amounts of the balance sheet rather than the risk amounts. A novice might accept this at face value, leading to significant trouble. Following it blindly, your interest rate risk analysis would be entirely wrong, and acting on it could lead to hedging non-existent risks, exacerbating the situation. To add more detail, I asked GPT-4 to provide a swap overlay onto the balance sheet risk for an institution, and the result was incorrect. Following it would be financially risky, exposing you to massive market exposure and blowing your limits out of the water. Relying strictly on the recommendations of GPT-4 could quickly damage your credibility. Despite these issues, the problem of hallucination seems to be far less prevalent than it was 12 months ago when we first started using these models. The current set of models and their training appear to have reduced many of the possibilities for hallucinations. However, when using an LLM, you need to be aware that it can produce outputs that seem wholly correct and plausible when they are indeed false and inaccurate. This can be potentially embarrassing, if not dangerous, for organisations, especially for financial services firms regulated by the PRA or FCA. Credibility at stake When considering the use of LLMs in treasury and risk management, it's important to note that we are typically not customer-facing. The users we work with are usually internal, digesting financial information to make business decisions. Thus, the issue becomes one of credibility. If you create content or work using LLMs and then disseminate that within the organisation, such as in a management meeting, and it is wrong or found to be incorrect, your credibility is at stake. This, I believe, is a major factor in exercising caution when producing output with LLMs. It would be unwise to allow a junior staff member with little experience to use these models for generating content that directly feeds into senior management. Often, they are unable to discern whether the output is correct or whether it is misleading or factually inaccurate. Senior management, provided with incorrect information, would not only be annoyed but would also likely take action. Indeed, such errors could put the organisation at risk, especially if they lead to changed management decisions or result in misleading or inaccurate reporting to regulators. At this stage whatever you produce, you must read and thoroughly understand it before using it in the workplace. Colour coding When using LLMs in treasury and risk management, a clear and open discussion about their use is necessary. Initially, we should question the output until we are entirely comfortable with its accuracy and reliability. For instance, if LLMs are used in generating reports for an ALCO meeting or a board meeting, the text could be colour-coded, or a graph could be annotated to indicate that an LLM was used in the generation. This transparency is helpful as it not only demonstrates the power of LLMs in the business but also draws attention to their usage, prompting caution until we are entirely comfortable with the material. Parallel running I also suggest that it is important to run processes and risk management systems in parallel with LLMs, rather than substituting them, until you are entirely comfortable with what they are generating. This parallel running is a “compare and contrast” exercise and may help highlight either anomalies in what the LLM produces or gaps in the information provided by existing legacy systems. For example, if you were producing a liquidity report, you might ask the LLM to comment on the liquidity situation and add this to the regular information you generate before fully transitioning from one approach to another. Additionally, I wouldn’t overload reporting with LLM-generated material too quickly, as it would be difficult to trace exactly what is being generated and to compare the quality and consistency of the new with the old. Use the three lines of defence Another way to manage the introduction of LLMs in treasury and risk management is to ensure there is independent oversight of these models. We use a three-line-of-defence approach to many aspects of our operations, and this can be valuable in assessing whether an LLM inadvertently exposes us to more risk. There needs to be an open and frank discussion about the use of LLMs in treasury and risk management, including with senior management and risk management. Risk management needs the skills and understanding to assess whether the output generated by the models and particularly how it is used in the firm, is beneficial or could generate harmful situations for the business. This discussion should be constructive, recognising that LLMs offer a significant increase in efficiency, provided there is solid independent oversight of their use. A further area of investigation should look at the use of LLMs in compliance and regulatory reporting. Incorrect reporting in these areas can raise questions about the governance of the firm. In treasury and risk, particular attention should be paid to reporting on liquidity, interest rate risk, market risk, and capital. These are all areas where LLMs can significantly improve efficiency but require full oversight by those in charge. Scope of audit Moreover, the audit function has a responsibility to delve into these models to ensure they do not lead to unintended consequences. For example, do these models reinforce bias in the business? Are we overlooking pipeline risk? Are we risk-seeking with deposits without considering the consequences? Is there a tendency to think that interest rates are always moving in one direction? Internal audit should also examine whether balance sheet and risk data are leaking out of the business via the LLM into the public domain. A themed audit could drill down into the data produced and its use in the firm or reach upwards towards the management suite to assess governance and oversight. I’m also aware that GPT-4 can subtly alter content, giving it a slightly different meaning from what was intended. This too is something to be wary of, and I advise thoroughly checking any output before it is used in the firm. What does this mean for the future? An opportunity to be grasped It means that LLMs currently offer an opportunity to increase the efficiency of those working in treasury and risk, but output from LLMs used in the business, either from a reporting or governance perspective, must be sense-checked to ensure its robustness. As LLMs improve, we will likely grow more reliant on their accuracy and efficacy, similar to the first risk management systems used in treasury 40 years ago. The rapid development of LLMs suggests that within a few years, they will reach "escape velocity," capable of completing tasks at a human level with minimal prompting, significantly reducing the need for human involvement in the process between trades and the C-suite.

In this sixth instalment, we delve into two time-saving applications of LLMs: summarising and repurposing PowerPoint presentations, and efficiently taking minutes in meetings. Whilst the following is general it takes little imagination to use this in the context of Treasury and Risk thereby saving yourself a lot of time and effort. Summarising and Repurposing PowerPoint Presentations My discovery of this application was serendipitous, revealing the untapped potential of GPT-4 when we experiment and explore. Initially, interacting with ChatGPT to discuss an uploaded PowerPoint file required code interpreter and a series of specific prompts. Now, the process has become remarkably user-friendly. Simply upload your PowerPoint presentation into GPT-4, hit the return and it automatically generates a concise summary - no prompting necessary Real-World Example: Imagine having a series of slides for a presentation on large language models. By uploading these into GPT-4, you receive a bullet-point summary almost instantaneously. However, this is just the beginning. Each bullet point can be expanded into a detailed article, providing rich content tailored to your audience. At the end of this article is an example of both summary and content repurposing in action. Taking Minutes in Meetings In today’s digital age, recording meetings, especially committee ones, is commonplace. Whilst I cannot get GPT-4 to transcribe audio files directly, alternative solutions like Descript offer transcription and summarisation services. For instance, uploading an m4a recording to Descript yields a comprehensive and accurate transcript - it's impressive. Zoom meetings also offer another efficient alternative, providing near-instantaneous transcripts. The advantage of these AI-driven summaries lies in their accuracy and objectivity, free from the biases or errors a human note-taker might introduce. Workplace Efficiency As we continue to explore the capabilities of LLMs like GPT-4, their potential becomes increasingly evident. From transforming presentations into diverse content forms to accurately capturing the essence of meetings, these tools are not just about saving time; they are about enhancing the quality of our work and getting information to those in our organisations in a format that is useable and actionable. I also believe they (LLMs) deal with a lot of boring and repetitive tasks that take the fun out of work for those involved and the more you experiment the more you can see their potential. Example of GPT-4 and slide deck, this is the uploaded presentation (note I uploaded a ppt file). Then hit return. GPT-4: The PowerPoint presentation titled "My Experience with LLMs" by William Webster covers various aspects of using Large Language Models (LLMs), including their applications and capabilities. Key points from the presentation include: Introduction to Large Language Models (LLMs): This section explains the basics of Generative Pre-trained Transformers (GPT) and mentions different models from various companies like OpenAI's ChatGPT, Anthropic’s Claude, Google’s Bard, Meta’s LLaMA, and Inflection’s Pi. User Interface and Interaction: It discusses how users interact with GPTs through various interfaces such as text, documents, pictures, and speech. Emphasis is placed on the ease of interaction through Natural Language Processing (NLP), requiring no programming skills. Practical Applications: The presentation highlights several practical uses of LLMs, such as reading and converting text from pictures, calculating costs, generating images with DALL-E, and analyzing CT scans. Evolution of GPT Models: The presentation tracks the development of GPT models, mentioning the release of GPT-3.5 and GPT-4, and their capabilities in creating content and engaging in detailed conversations. Usage Examples in Professional Contexts: Specific use cases in professional settings are outlined, including drafting emails and documents, summarizing information, and crafting prompts for specific outputs. Efficiency and Risk Management: The presentation explores how LLMs can improve efficiency in tasks like preparing board papers, taking minutes, and analyzing financial risks. AI as a Creative Engine: It is emphasized that unlike traditional search engines, LLMs can generate diverse outputs, making them valuable for creative and strategic tasks. AI Policy and Workforce Implications: The presentation underscores the importance of developing an AI policy, training needs, and the impact of AI on jobs and workflows. Ethical Considerations and Data Security: Concerns about data training, privacy, and the need for human oversight in AI processes are mentioned. Resources: The presentation concludes with a list of resources for further exploration, including podcasts, blogs, and websites related to AI and LLMs. The presentation provides a comprehensive overview of the capabilities, applications, and considerations surrounding the use of Large Language Models in various contexts. Me: Write a single helpful article on: User Interface and Accessibility: Focuses on the user-friendly nature of LLMs, allowing interaction through text, images, and voice without needing programming expertise. GPT-4: Harnessing the Power of AI: The User-Friendly Revolution of Large Language Models In the world of artificial intelligence, the emergence of Large Language Models (LLMs) has been nothing short of a revolution. A key factor behind their widespread appeal lies in their exceptionally user-friendly interfaces, making advanced AI accessible to everyone, regardless of their programming skills. This article delves into how LLMs have transformed user interaction through text, images, and voice, effectively democratizing access to cutting-edge technology. The Simplicity of Text Interaction Text-based interaction is the bedrock of LLM interfaces. It's as simple as typing in a query or command in natural language - much like sending a text message. This approach has opened up AI to a much broader audience, allowing users to engage in everything from casual conversations to complex problem-solving without any knowledge of programming languages. Tools like OpenAI's ChatGPT exemplify this, where users can obtain information, write code, or even create content through straightforward text prompts. Visual Interfaces: A Picture is Worth a Thousand Words The integration of visual interfaces in LLMs has taken user experience to a new dimension. Users can now interact with AI through images, making these systems much more intuitive and versatile. For instance, OpenAI's DALL-E can generate detailed images from textual descriptions, showcasing how LLMs can understand and execute visual tasks. This capability is not just a boon for creatives but also enhances the utility of AI in fields like design, education, and marketing. Voice Interaction: Speaking to AI Voice interaction represents a significant leap in making LLMs more accessible and natural to use. By simply speaking to these models, users can engage in a range of activities, from getting real-time assistance to controlling smart devices, all in a hands-free manner. This feature is particularly beneficial for those who find typing challenging or prefer verbal communication. It also paves the way for LLMs to be used in diverse environments, from busy kitchens to moving vehicles. Breaking Down Barriers: AI for All Perhaps the most profound impact of these user-friendly interfaces is the democratization of AI. With LLMs, the need for technical expertise in coding or data science is no longer a prerequisite to harnessing the power of AI. This opens up a world of possibilities for non-technical users, including educators, artists, small business owners, and hobbyists, allowing them to leverage AI for their unique needs. The Future of User Interaction with LLMs Looking ahead, the user interfaces of LLMs are poised to become even more intuitive and integrated. We can expect developments that combine text, visuals, and voice in more seamless and interactive ways, potentially incorporating additional sensory inputs for a more immersive experience. The goal is to create interfaces that are as natural and effortless to use as our own human senses, making AI an unobtrusive yet powerful extension of our daily activities. Conclusion The user-friendly nature of LLMs marks a significant milestone in the field of AI, bringing these powerful tools within reach of a global audience. By simplifying interaction through text, images, and voice, LLMs are not just enhancing productivity and creativity; they are opening doors to a future where the benefits of AI can be enjoyed by everyone, irrespective of their technical background. As we continue to refine these interfaces, the potential of LLMs to enrich and simplify our lives seems limitless.

The fifth instalment of our series exploring simple GPT-4 use cases focuses on its capacity to generate documentation for an ALCO committee. This article delves into a specific scenario involving a small bank or building society, and how GPT-4 can assist in creating insights into interest rate risk. Our approach begins with a basic spreadsheet showing different time buckets, the corresponding assets and liabilities, the net gap position, and two flat yield curves at 5% and 5.01%. Using this data, the discount factors from the yield curves are used to calculate the net profit or loss resulting from a 0.01% interest rate shift, namely this is a simple delta report, a common tool in banking for managing interest rate risk. In this example, we see a bank with long-term assets funded by short-term liabilities, creating both interest rate exposure and potential liquidity risk. Initially, I asked GPT-4 to analyse the spreadsheet. However, the results were unsatisfactory. GPT-4 struggled with the Excel format, producing an incorrect and misleading narrative. Adopting a different strategy, I copied rather than uploaded the relevant section of the spreadsheet directly into GPT-4's prompt window. This action generated an image of the spreadsheet and displayed the numerical data in the chat. After removing the numbers from the chat and leaving only the image, I requested GPT-4 to analyse it. Despite this modification, the process remained challenging due to inconsistencies in the spreadsheet's formatting, such as unclear labelling and the negative numbers being in brackets. After improving the spreadsheet's consistency and including negative numbers rather than brackets, I initiated a new session with GPT-4. This time, the result was more coherent. Progressing further, I requested GPT-4 to create bar charts and relevant commentary for the ALCO committee. Although time-consuming, this exercise yielded a workable method for engaging GPT-4 effectively. From this experience, you can see that GPT-4 can act as a co-pilot but it still needs a lot of interaction so it is not an automatic process. However, if you use it in conjunction with its text generation capability, preparing relevant and useful information is achievable faster. It would not surprise me if, within 12 months, given the improvements in LLMs the process could be automated leading to a very large jump in productivity. Key Points Consistency of data uploaded to the LLM: GPT-4 does not deal well with any slight inconsistency in the data format, for example, if there are blank cells, unaligned rows or columns or bracketed numbers. This contrasts with using text in GPT-4 where it can work with, and make sense of, relatively jumbled ideas, poor syntax and grammar to come up with very good working documents. When LLMs can do this with numbers we will have a step change in the ability to use them in finance. Handling Spreadsheets with GPT-4: GPT-4 exhibits better performance with images of spreadsheets rather than the files themselves, why this is, I don’t know. Is it due to it being a language-based model? Uploading the image without a prompt: this led GPT-4 to analyse what it received before I initiated the prompting, it seemed to slow the model down giving it time to think about the information rather than just trying to complete the prompt and in this limited example the subsequent output was better as a result. Process Efficiency: The procedure of using GPT-4 for such tasks can be initially tedious. However, mastering the process is a valuable skill, promising time savings for future repetitions. The Necessity of Human Oversight: Similar to the early days of spreadsheet usage, there's a risk of assuming AI-generated content is infallible. Vigilance is essential to identify potential errors. GPT-4 can generate plausible but potentially incorrect charts and commentary. Therefore, human validation is crucial to ensure accuracy before using the output in reports. Evolution of LLMs: GPT-4's current capabilities significantly surpass those of its predecessors, suggesting a near future where AI can seamlessly turn data into meaningful analyses and reports. This is where you just upload the data and the charts and written reports are generated automatically for the whole ALCO deck. Benefits of LLMs in Routine Tasks: By handling routine analytical work, AI models like GPT-4 free up human resources to focus on complex decision-making and risk evaluation through collaborative discussions. The dialogue with GPT-4, detailed below, provides insight into the challenges and solutions of integrating LLMs into financial documentation and analysis tasks. ---------------------------------------------------- Me: GPT-4: You've uploaded an image that appears to contain a table with financial data related to asset and liability management, including time buckets, asset and liability amounts, net gap, interest rates (Curve 1 and Curve 2), and net profit and loss (P&L). This data seems to be part of an asset-liability matching or a gap analysis, which is typically used by financial institutions to manage interest rate risk. The table includes different time buckets ranging from "overnight" to "10 years". In each time bucket, the assets and liabilities are compared, and the 'Net gap' row shows the difference between them. Below that, the table shows two different interest rate curves and the net P&L calculated from these rates, which indicates the financial impact of the interest rate changes on the net gap over time. If you need any specific analysis or have questions about the data, feel free to ask! Me: please produce a bar chart of the net gap by time bucket GPT-4: Me: now a bar chart of the P&L by time bucket GPT-4: Me: Now the commentary for an ALCO meeting, this should explain the interest rate risk GPT-4: ALCO Meeting Commentary on Interest Rate Risk Introduction: Our current asset-liability position indicates varying degrees of interest rate risk across different time buckets. The net gap analysis, alongside the interest rate curves, offers a clear view of our exposure. Net Gap Analysis: The net gap distribution shows significant short-term liabilities exceeding assets in the "overnight" to "6-12 months" buckets, indicating a potential liquidity risk if interest rates rise. Conversely, from "1 year" to "10 years," we observe a surplus of assets over liabilities. This longer-term positioning could be beneficial if interest rates decrease but poses a risk if rates increase, as our interest income would be adversely affected while paying out at higher rates on liabilities. Interest Rate Curves and P&L Impact: The first curve (Curve 1) represents a baseline scenario with current interest rates, while the second curve (Curve 2) represents a slight upward shift in rates. The net P&L across time buckets shows that as the maturity extends, the impact of an interest rate rise becomes more pronounced, culminating in significant potential losses, particularly evident in the "2 year" to "5 year" buckets. Short-Term Concerns: In the immediate term, we must address the liquidity risk due to the negative gap. Any unexpected increase in short-term rates could increase our cost of funding significantly, which is not fully offset by corresponding increases in asset yields. Long-Term Strategies: For the longer-term, our current position could benefit from a hedging strategy to mitigate the risk of rising rates. This might include entering into interest rate swaps or options to lock in current rates for future liabilities or to convert fixed-rate assets to floating rates. Conclusion: It is imperative to re-evaluate our asset-liability matching strategy to ensure it aligns with our risk appetite and market expectations. While we have some cushions due to the positive gaps in longer maturities, the potential for interest rate hikes suggests a more conservative approach might be warranted to protect our net interest margin. Recommendations: Increase the proportion of floating-rate or short-duration assets to reduce the negative gap in the short term. Consider derivative instruments for hedging long-term interest rate exposure. Continuously monitor the interest rate environment and adjust our ALM strategy accordingly. This commentary would need to be tailored with more specific details from the financial institution’s own data and strategic positioning, but it provides a framework for discussing interest rate risk at an ALCO meeting.

In this fourth instalment of our series, we explore an intriguing application of large language models (LLMs) in treasury and risk management: envisioning future events and shaping our responses to them. This application is particularly compelling, considering that while we often concentrate on LLMs for text, visual, and data interpretation, I believe one of their true strength lies in sculpting business scenarios and strategies. High-level scenario and strategy planning is a critical task for knowledge workers. Here, LLMs like GPT-4 shine, not by replacing human decision-making but by offering a wide range of potential outcomes and strategies, enriching our planning processes. I put this to the test with GPT-4, prompting it to generate plausible yet severe scenarios for liquidity stress testing. The results were multifaceted. Initial prompts led to varied scenarios, including retail customer behaviour. Further prompting unearthed even more unconventional scenarios. GPT-4 didn't just list these scenarios; it grouped them into categories and this helped in deciding their use and applicability. The real advantage of working with GPT-4 is evident here. The output can be used to identify gaps in your liquidity risk analysis or compare it with existing industry knowledge. You can select any GPT-4 suggestion to spawn further discussions, evaluating their relevance and potential integration into your plans, including necessary updates to your mitigation strategies. For instance, I delved into a scenario centred on social media-driven customer behaviour – not something I’m particularly familiar with. GPT-4 helped me understand how this could unfold, its potential impact on our operations, and possible mitigating measures. Key Takeaways from This Use Case Scenario Development: LLMs like GPT-4 are formidable tools for crafting scenarios and strategies. I believe their role in business strategy will grow. Creative Assistance: GPT-4 rapidly generates scenarios, some amusing and far-fetched. It acts as a creative aide, but human discernment remains crucial in selecting the most pertinent scenarios. Effective Prompting: Tailoring prompts to your organisation's specific context enhances GPT-4's effectiveness. The more context and clarity provided, the more relevant the outcomes. Time Efficiency: GPT-4's swift responses save considerable time, allowing teams to focus more on discussion and evaluation rather than scenario generation. This underscores the value of human knowledge and experience when using LLMs. Strategic Application: Choosing relevant examples demonstrates the potency of this approach. GPT-4's suggestions on mitigating social behaviour risks can be integrated into your business strategy and tailored to your specific needs. This particular example may not be for you, but this analysis using GPT-4, facilitates change in what you do. ----------------------------------------- My conversation with GPT-4 Below are selected excerpts from my dialogue with GPT-4 on this topic. It's important to note that achieving these results required multiple prompts and some strategic thought in engaging with GPT-4. This is a common aspect of leveraging such a powerful tool effectively. Me: Imagine I'm a risk manager working at a building society or bank. I want to explore and add some stress scenarios to our liquidity policy. We are predominately retail funded and our assets are mortgages, gilts and cash deposits at the Bank of England. We use swaps to hedge interest rate risk arising from fixed rate mortgage assets. The stresses should be severe but plausible. I am particularly interested in anything new or unusual. Use all your knowledge and expertise to give me some examples. GPT-4: As a risk manager in a building society or bank with a portfolio primarily consisting of mortgages, gilts, and cash deposits at the Bank of England, along with the use of swaps for hedging interest rate risks from fixed-rate mortgages, your focus on developing severe but plausible stress scenarios for your liquidity policy is a prudent approach. Let's explore some innovative and unusual scenarios that could challenge your institution's liquidity position: Pandemic-Induced Economic Downturn: Consider a scenario where a global health crisis (similar to COVID-19) leads to a significant economic downturn. This could result in a sharp increase in unemployment, leading to a spike in mortgage defaults. Additionally, there might be a sudden withdrawal of retail deposits as consumers seek to cover living expenses, coupled with a drop in property values that affects the collateral value of your mortgage assets. Interest Rate Shock Combined with Inflation Spike: Imagine an unexpected, rapid increase in interest rates, possibly due to a sudden spike in inflation. This scenario would affect both sides of your balance sheet. On the asset side, the value of your fixed-rate mortgage assets and gilts could decline, while on the liability side, there could be pressure to increase interest rates on deposits to retain customers, squeezing your net interest margin. Technology-Driven Market Disruption: Envision a scenario where a technological breakthrough (such as a new digital currency or a disruptive financial technology platform) significantly alters consumer behavior. This could lead to a rapid outflow of retail deposits to these new platforms, creating liquidity strain. Additionally, this tech disruption could impact the real estate market, affecting the value of your mortgage assets. Regulatory Change Impacting Swaps Market: A sudden regulatory change or a political event that significantly alters the swaps market could be a stress scenario. For example, a change in regulation that increases the cost of using swaps or restricts their use could expose your institution to unanticipated interest rate risk, affecting your liquidity position. Climate Change-Induced Natural Disasters: Consider a scenario where severe climate events (such as floods, wildfires, or storms) significantly impact large geographical areas. This could lead to widespread property damage, affecting the value of mortgage collateral and potentially leading to increased defaults. Additionally, these events could disrupt economic activity, impacting retail deposits. Cybersecurity Incident Leading to Systemic Failure: A systemic cybersecurity incident that affects major financial infrastructure (like payment systems) could result in a temporary but severe disruption to liquidity. This could be due to the inability to access or move funds, combined with a loss of depositor confidence. Geopolitical Crisis Causing Market Turmoil: A geopolitical event, such as a major conflict or political instability in a key economy, could lead to significant market volatility. This could impact your gilt portfolio and potentially lead to a flight-to-quality, with depositors moving funds to perceived safer havens. In each of these scenarios, it's crucial to assess not only the direct impact on your assets and liabilities but also the second-order effects, such as the impact on borrower behavior, market confidence, and operational capacity. Incorporating such scenarios into your stress testing exercises will enhance your institution's preparedness for a range of severe but plausible events, ensuring robust liquidity risk management. Me: What about risks from the behaviour of retail customers or competitors? GPT-4: Considering risks arising from the behavior of retail customers and competitors is indeed crucial for a comprehensive stress testing framework, especially for a financial institution like yours that relies heavily on retail funding and operates in a competitive market. Here are some scenarios that encompass these aspects: Mass Retail Customer Withdrawals: Imagine a scenario where a widespread rumor or negative news (true or false) about the financial health of your institution leads to a loss of confidence among retail customers. This could trigger a 'bank run' scenario, where a large number of customers simultaneously decide to withdraw their deposits, significantly straining your liquidity position. Shift in Customer Preferences: Consider a scenario where there's a sudden shift in customer preferences towards newer financial products or services offered by fintech companies or digital banks. This could result in a steady outflow of retail deposits as customers move their funds to these new offerings, impacting your funding base. Competitive Rate War: Envision a scenario where one or more competitors aggressively cut rates on mortgages or increase rates on deposits to capture market share. This could put pressure on your institution to follow suit to retain customers, impacting your net interest margin and potentially leading to liquidity strains if deposit rates are increased significantly. Competitor's Technological Advancement: A competitor’s breakthrough in technology, such as advanced online banking platforms or AI-driven financial advisory services, could attract a significant portion of your customer base. This shift could be particularly impactful if it targets a key demographic, like tech-savvy younger customers, leading to a gradual erosion of your deposit base. Competitor's Strategic Alliance or Merger: A major competitor could form a strategic alliance with another financial player or undergo a merger, significantly enhancing their market presence and product offerings. This could lead to a loss of competitive edge for your institution, potentially resulting in customer attrition and a reduction in new business. Competitive Disruption in Mortgage Market: If a competitor introduces an innovative mortgage product – for example, significantly lower rates or more flexible terms – it could lead to a significant shift in the mortgage market. Your institution might face a double-edged sword: losing existing customers refinancing with competitors and facing challenges in attracting new mortgage customers. Social Media-Driven Customer Behavior: In today’s digital age, social media can rapidly influence public opinion and customer behavior. A viral social media campaign (positive for a competitor or negative against your institution) could swiftly alter customer perceptions and behavior, leading to unexpected shifts in your customer base and deposit levels. Incorporating these customer and competitor behavior-driven scenarios into your liquidity stress testing will provide a more robust understanding of potential risks and enable your institution to develop more effective strategies to mitigate these risks. It's also important to continuously monitor market trends, customer behaviors, and competitor actions to update these scenarios and ensure they remain relevant and plausible. Me: Now add a few unusual scenarios we could consider GPT-4: Adding unusual but plausible scenarios to your stress testing can help prepare your institution for a broader range of potential risks. These scenarios, while less conventional, could provide valuable insights into your institution's resilience. Let's consider some creative and unconventional scenarios: Digital Currency Disruption: Imagine a scenario where a major technology company or a consortium launches a new digital currency that rapidly gains popularity. This currency offers higher interest rates for savers and lower transaction costs, leading to a significant portion of your retail customer base transferring their deposits to this new digital currency, causing a sudden liquidity crunch. Social Movement Leading to Financial Behavior Shift: Envision a powerful social movement advocating for ethical and environmentally sustainable banking practices gaining momentum. This movement could influence a significant portion of your customer base, leading them to transfer their deposits to institutions perceived as more socially responsible, impacting your liquidity. Virtual Real Estate Boom in Metaverse Environments: With the growing interest in virtual worlds and metaverses, imagine a scenario where virtual real estate becomes a lucrative investment, diverting significant amounts of disposable income and savings from traditional assets like physical real estate to virtual properties. This could impact the mortgage market and lead to unexpected shifts in your customer's financial behavior. Global Supply Chain Crisis: Consider a scenario where a severe, prolonged global supply chain crisis significantly impacts the cost of living and doing business worldwide. This could lead to increased defaults on mortgages due to higher living costs and affect the employment sector, thereby impacting your retail customer's ability to maintain their deposits and financial commitments. Radical Regulatory Changes: A scenario where there's a sudden and radical overhaul of financial regulations, possibly driven by a global consensus on combating financial crimes or a major economic event. These changes could include stringent capital requirements or limitations on certain types of financial products, dramatically impacting your institution's operations and customer behavior. AI-Driven Financial Planning Trend: Artificial intelligence (AI) in financial planning becomes extremely sophisticated, leading to a trend where AI advisors recommend customers to diversify their assets away from traditional savings accounts and into a variety of new financial products. This could lead to a large-scale withdrawal of retail deposits as customers follow these AI-generated advice. Extraterrestrial Event Impacting Global Markets: While highly unlikely, a scenario involving a significant extraterrestrial event, such as confirmed evidence of extraterrestrial life, could lead to unprecedented market volatility. The uncertainty and media frenzy surrounding such an event could lead to erratic customer behavior, including panic withdrawals or hoarding of funds. While some of these scenarios might seem far-fetched, they serve to challenge conventional thinking and help your institution prepare for a wide range of possibilities. The key is to evaluate the potential impact of these unusual scenarios on your liquidity, customer behavior, and overall market stability, and to develop strategies to mitigate these risks. Me: Please create a list of all the stresses you have come up with GPT-4: Absolutely. Here's a comprehensive list of all the stress scenarios we've discussed, encompassing both conventional and unusual risks: Conventional Stresses Pandemic-Induced Economic Downturn: A global health crisis leading to economic downturn, mortgage defaults, and retail deposit withdrawals. Interest Rate Shock and Inflation Spike: Unexpected rise in interest rates due to inflation, affecting mortgage assets, gilts, and deposit rates. Technology-Driven Market Disruption: New digital currency or fintech platforms significantly altering consumer behavior and impacting retail deposits. Regulatory Change in Swaps Market: Sudden regulatory changes affecting the cost and usability of swaps, exposing interest rate risks. Climate Change-Induced Natural Disasters: Severe weather events damaging property and affecting the value of mortgage collateral. Cybersecurity Incident: A major cybersecurity breach affecting financial infrastructure, leading to liquidity challenges. Geopolitical Crisis: A major geopolitical event causing market volatility and affecting gilt portfolios and depositor confidence. Behavior and Competitive Stresses Mass Retail Customer Withdrawals: A 'bank run' scenario triggered by loss of confidence among retail customers. Shift in Customer Preferences: Customers preferring new financial products or services, leading to retail deposit outflows. Competitive Rate War: Competitors significantly altering rates on mortgages or deposits, impacting net interest margin. Competitor's Technological Advancement: Loss of customers due to a competitor's technological breakthrough in banking services. Competitor's Strategic Alliance or Merger: A major market shift due to a competitor's merger or alliance, leading to customer attrition. Competitive Disruption in Mortgage Market: A competitor introducing an innovative mortgage product, affecting customer retention and acquisition. Social Media-Driven Customer Behavior: Rapid shifts in customer behavior influenced by viral social media campaigns. Unusual Stresses Digital Currency Disruption: Launch of a popular digital currency leading to significant retail deposit withdrawals. Social Movement for Ethical Banking: Customers moving their deposits to more socially responsible institutions due to a social movement. Virtual Real Estate Investment in Metaverse: Shift of investment from physical to virtual real estate, affecting the mortgage market. Global Supply Chain Crisis: A supply chain crisis raising living costs, impacting employment and mortgage defaults. Radical Regulatory Changes: Overhaul of financial regulations impacting banking operations and customer behavior. AI-Driven Financial Planning Trend: AI advisors leading customers to diversify away from traditional savings, affecting deposits. Extraterrestrial Event Impacting Markets: Market volatility and erratic customer behavior triggered by an extraterrestrial event. Each of these scenarios poses unique challenges and opportunities for testing the resilience and adaptability of your institution's liquidity and risk management strategies. Me: Please provide a more detailed analysis of how Social Media-Driven Customer Behavior could affect us. In particular consider what could cause it, how it could unfold, the effect it could have on us and mitigating steps we could take now, or in the future should such an event happen. GPT-4: Analysis of Social Media-Driven Customer Behavior Impact Potential Causes Viral Misinformation or Negative Campaigns: False rumors or targeted negative campaigns about your institution's financial health, unethical practices, or customer service failures could go viral. Influencer Impact: Influential social media personalities could sway public opinion against your institution, either due to personal experiences, affiliations with competitors, or as part of a social cause. Customer Service Incidents: A small-scale customer service issue, if poorly handled and publicized online, could escalate rapidly in the social media ecosystem. Competitor's Positive Campaigns: Competitors might leverage social media effectively to highlight their strengths or innovations, indirectly casting a shadow on your offerings. How It Could Unfold Rapid Spread of Information: Social media accelerates information dissemination, with the potential to reach millions in a short span. Echo Chambers: Information, whether true or false, tends to get amplified within social media echo chambers, reinforcing negative perceptions. Mobilization of Public Sentiment: Social media can mobilize public sentiment quickly, leading to coordinated actions like account closures or protests. Media Amplification: Traditional media might pick up on these social media trends, further amplifying the issue. Potential Effects Run on Deposits: Widespread panic could lead to a significant number of customers withdrawing their deposits, straining liquidity. Damage to Brand and Reputation: Long-term damage to the institution's brand and reputation could deter potential customers and affect business growth. Reduced Investor Confidence: Shareholder and investor confidence could waver, affecting stock prices and future investment. Operational Disruption: Increased volume of inquiries and complaints could overwhelm customer service channels, leading to operational disruptions. Mitigation Strategies Current Measures Robust Social Media Monitoring: Implement comprehensive tools to monitor social media for mentions of your institution. Early detection is key. Crisis Communication Plan: Develop a crisis communication plan specifically for social media-driven events. Customer Service Excellence: Invest in exceptional customer service to prevent issues from escalating to social media complaints. Positive Brand Building: Regularly engage on social media with positive stories, customer testimonials, and transparency reports to build a reservoir of goodwill. Responsive Actions During an Event Rapid Response Team: Have a dedicated team ready to respond quickly to misinformation or negative publicity on social media. Direct Customer Engagement: Engage directly with affected customers to resolve issues and demonstrate commitment to customer satisfaction. Collaboration with Influencers: Collaborate with respected influencers to counteract negative campaigns with factual information. Transparent Communication: Regularly update customers and the public with clear, transparent information about the situation. Long-Term Strategic Measures Digital Literacy Campaigns: Educate customers about misinformation and digital literacy to build a more discerning customer base. Strengthening Online Presence: Develop a strong, positive online presence through regular, engaging content and community involvement. Liquidity Cushions: Maintain robust liquidity reserves to handle potential runs on the bank. Regular Simulations: Conduct regular simulations or drills for handling social media crises to ensure preparedness. By proactively managing your social media presence and being prepared to respond effectively to any crises that arise from it, your institution can mitigate the risks associated with social media-driven customer behavior. This approach not only helps in managing immediate crises but also contributes to building long-term resilience and trust with your customer base.