What's first strategy or risk appetite? A regular feature in the Investors Chronicle is a column called “Portfolio Clinic”, it evaluates a reader’s investments, appetite for risk and their retirement plans. Often the reader gets a shock - their assumptions about risk and return do not match and they are forced to adjust their plans. In this way investment strategy and risk appetite are inextricably linked. Businesses too have strategies that are closely connected with the risk they are prepared to assume. Firms that need to operate with tight safety, (nuclear power, refining, aircraft manufacturing), have strategies that recognise this as failure is costly - Tokyo Electric, BP, Boeing. In entrepreneurial sectors (technology, pharmacology, alternative energy) the strategy recognises greater variance in returns and the risk appetite accommodates. On this scale I’m sure we would agree that building societies are “conservative”. But does risk appetite drive strategy or vice versa? (A thought-provoking question for any NED). On the "edge" The strategy surely identifies the “edge” your business has and how best to exploit it.  It is only after defining your strategy to benefit from your perceived advantage that you can really see what it means in terms of risk. If you start with the risk appetite and attempt to fit the strategy around it, you are likely to miss out on identifying and exploiting the franchise your business has to offer. To be clear, I’m not suggesting an aggressive lending strategy that overrides risk but by putting the strategy before risk and using iteration to make sure that both work together you end up with a better outcome. It’s about recognising the idiosyncrasies of your business and where possible exploiting them within a risk appetite you are comfortable with. Notwithstanding I recognise that risk appetite in some Societies does drive the strategy and this leads to a further engaging question. How do you determine what your risk appetite is? You could respond with reference to solvency, profitability, franchise, resources, skill, expertise and experience – all sensible starting points. However what matters is the process. Does top down direction work with bottom up insight, to agree an appropriate risk limit? You can now see that by linking member value and the need to operate for extended periods under difficult conditions NEDs can explain how the risk limit(s) supports the strategic objective(s) of the society. What do I see? I’m not privy to what happens in all the sector’s forty plus Board rooms however I know Boards don’t find this easy. Published documents show that some Societies could do more to link strategy and risk together. Furthermore, I’m sure you can find instances in your own firm where it’s hard to determine the role strategy and risk take in driving what you do. When this happens, a common underlying factor appears to be regulation or the fear of falling foul it. As a consequence, sometimes we are guilty of trying to gauge the regulator’s appetite for risk (a bit like spoof). Whilst this may give NEDs some personal comfort it’s not what the regulator intends and what’s more it isn’t benefiting members per se. In Portfolio Clinic, it’s akin to holding only Gilts – great in the short run but ultimately a lot riskier than a balanced portfolio mainly because the return will be sub-optimal. It may seem unfashionable but making money is still important. Summary Strategy and risk appetite are closely linked. My view is that to get the most out of your business strategy comes first; There is an iterative process that connects strategy to your risk appetite and in this way a sensible balance is the outcome; I acknowledge that some Societies may put risk appetite as the driving force – if this is you, where does the risk appetite come from, is it the Regulator? NEDs may feel personally comfortable keeping risk to a minimum, but they should ask whether this is in member’s interests.

If you run your own money this may save you ten years of work, (honestly). My investment journey started over thirty years ago. I didn’t think too much about it, returns were high, my money went into “funds” and who worried about costs? It was easy to do some simple maths, the message couldn’t be clearer, if you wanted to keep your money, costs had to be low. At the time that meant paying at least 1% per annum plus hidden trading commissions. Herein was a problem over an investment lifetime even “modest” fees of 1% lead you to hand half of the investment return whilst still taking all the risk. It’s the simple maths of compounding and it’s like going to a restaurant, paying for a three-course meal and just getting the starter and desert. I started to read about indexing, something that had got going a lot earlier on the other side of the Atlantic. The pioneer, the late John (Jack) C. Bogle, had set up the mutual fund group Vanguard in 1974. My first introduction to his writing was “Don’t Count On it!” Now, over the years I’ve read quite a lot about investment. I really like Buffett’s style, Munger is pithy, Fisher asks the questions and Graham, well he’s the guru. But, to be honest, I’m just not as smart as they are. This is where Bogle differs, his strategy can be followed by anyone. Put 40%-60% in low-cost index trackers, the rest in low-cost bond funds and leave it well alone. It’s a case of the less you do the more you get. It’s even the strategy Buffett has followed for his wife’s trust. This year I picked up a copy of Bogle’s book “The Little Book of Common Sense Investing”, it’s an investment classic and I recommend reading it. In it he sets out the case for low-cost index investing in easy to read and insightful manner. There’s a lifetime of work here but some things really hit home, and I’d like to share them with you: Long run GDP growth is everything: Cumulative long run performance of the stock market is governed by GDP growth; one follows the other. Stock returns: Stock returns have two sources, investment return (dividend yield and earnings) and speculative returns, (the price of those earnings as expressed as the price/earnings multiplier). Investor returns: Returns earned by all investors must equal returns earned by the market. However, returns received by all investors equal returns earned by the market less fees and expenses. Outperformance: All evidence shows that over the long run market outperformance is random. So, although some win over short periods it is not something that can be done consistently. For this reason, by using low-cost index funds, your chance of beating the performance of a fund manager is 90% over 10 years and 98% over 50 years. We are all indexers: If you imagine a pie of all investors’ money and cut out the slice that is index tracking the remaining investors, as a whole, have to be indexers too. But individually they hold different positions and in the short run some win, and some lose. Collectively this group incurs significant trading costs that act as a drag on their returns. Market prediction: Short run predictions are guesses, but dividend yield, earnings and P/E can be used to provide a predictive look at what returns may be in the next ten years, the conclusion is that we are looking at substantially lower returns (3%) on a balanced portfolio over the next decade. After inflation of 2% and investment costs of 1.5% that’s a flat or negative real return only avoided by getting rock bottom costs. Behavioural bias: This plays against the investor. They tend to buy funds that have gone up and sell those that have gone down. This buy high, sell low, strategy acts as a drag on returns leading to a reduction in the order of 1.5% per annum. Keep it simple: Between 1926-2016 total market annual return was 9.8%, for the S&P 500 it was 10%. The S&P 500 index is about 85% of the value of all US stocks and being capital weighted doesn’t need rebalancing. The winning strategy is to own the index at minimum cost and let compounding work in your favour. Exchange traded funds: Bogle’s resistance to ETFs is clarified, he did not like the way they encourage trading but held for long periods broad range low-cost ETFs achieve much the same as an index fund. Asset allocation: Investment performance is dictated by asset allocation; Bogle considers the merits of 50/50 working up to 80/20 stock/bond portfolios. In essence it depends on the risk you can afford to take and what you feel comfortable with. What’s particularly thought-provoking is the common sense view that lower investment costs mean that you can take significantly less risk to achieve the same level of return. It’s not often less leads to more, and what I particularly like is that Bogle is an amusing writer, full of anecdote but above all backs up his case with irrefutable analysis where you can clearly see that he’s on your side. Don’t take my word for it, read the book and in the years to come you won’t regret it, after all he is setting out a strategy, where you will have an investment portfolio, that is worth at least a third more, or as I put it earlier, the option to retire early. I’m sure you too will see John Bogle as one of the greats in the investment world.

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.