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How things have changed. When I first started in a dealing room, there wasn't much knowledge to be shared around; indeed, many people considered dealing to be an instinct. You just learned as you went along, and hopefully, some magical insight would come your way. Today, it's different. If you're starting in treasury, there are several key skills that you require. You need to master some of the technical knowledge—let's call it the hard skills—to understand financial instruments, market dynamics, and risk management principles. It's non-negotiable; you either know it or you don't, and if you don't, you are always going to be missing some of what is happening. This is not just the mechanics, and it isn't an academic understanding; it's practical knowledge—the impacts from decision-making. In short, experience counts. It is quite amazing how much you can learn from doing a job on a day-to-day basis. In addition to mastering these hard skills, it's crucial to stay updated with the latest technological advancements in the financial sector. The rise of fintech, blockchain, and algorithmic trading (much of which has passed me by) has dramatically transformed the landscape. Being proficient with analytical tools and platforms gives you a significant edge. Furthermore, understanding regulatory frameworks and compliance requirements is essential, as the financial industry is heavily regulated and will remain so. But this technical prowess doesn't operate in isolation. Soft skills frequently differentiate between competent treasury professionals and exceptional ones. Communication is particularly important. What we do is complicated and getting the message across in a way people understand gets results. I've seen many situations where people who are technically gifted fall down on their explanations. This is a huge source of frustration for all concerned. The next time you write something, ask yourself, can the layman understand it? Moreover, effective communication isn't just about simplifying complexity; it's also about listening. Understanding concerns and objectives facilitates better collaboration and leads to more informed decision-making. How often have you seen situations where marketers want to price things uneconomically, whilst the true cost of hedging is passed on? Getting to the nuts and bolts of why and how we price things based on their cost is essential. Self-awareness is also a skill that needs to be developed. In a high-pressure environment, emotions can influence choices. It's easy to think that you are right and the market is wrong, and therefore hold out on past decisions. Standing back, taking a measured view and changing your mind can be a strength. But trust was the main attribute I looked for when running a treasury. Can I leave this person to get on with the job and know that it will be done properly and without cutting corners? Is this person someone that I feel comfortable with when they are taking risks? Are they overconfident in a way that could be costly? Do they acknowledge when they are getting things wrong? Do they ask when they do not know? These things are essential; if you can't trust the people that you are working with, you are in trouble.

When I was trading, policy wasn’t a priority. This attitude wasn't just mine; it was common throughout the bank. The entire organisation seemed to operate more on gut instinct than formal guidelines. I don't recall ever seeing a document until the mid-1990s. Even when such things existed, they were often poorly written and irrelevant to what we did. It’s no wonder they went unread. Things changed after Nick Leeson. Banks were compelled to scrutinise their risk practices closely, and having a policy became widespread. Though turgid, these documents provided something to show a framework was in place. However, in practice, they were largely ignored. Today, well-crafted explanations are essential. Documenting procedures forces focus. It raises awareness about your activities and their purposes. Treasury policy explains what you do, how you do it and why you do it. Structure it into sections that can stand alone. For instance, it should outline the role of the treasury, its relationship to the firm, and its overarching purpose. It should also identify the principal risks being managed, such as credit, market, liquidity and operational risk. Additionally, it should explain how these risks are influenced by broader business operations. For example, selling mortgages introduces interest rate risk, while holding deposit accounts brings liquidity risk. This explains the context. Beyond identifying risks, the policy should detail the methods used to manage them. Think about how risk is monitored and the limits in place. Above all, the document must be succinct. Low-level details should be relegated to appendices for easy access. Effective governance, or the management process, must also be explained. This is often a weak point, with unclear committee structures and ambiguous responsibilities. Improving this aspect helps clarify decision-making processes. The treasurer should not be solely responsible for creating the policy. Input from other stakeholders, particularly the Asset Liability Committee, is vital. Circulate the draft for feedback; collective input can significantly improve things. Drafting and revising policy is time-consuming and seen as a low priority. The good news is existing material can be refined. AI can expedite the process. Rather than inputting the entire document all at once, break it into smaller sections and use the AI to make improvements. Try prompting: "This is part of our treasury policy, and I would like to enhance it. Please ask me questions that will help refine this section, ask the questions sequentially, and let me answer before moving to the next question, once you have gathered the information you need to rewrite the piece in straightforward terms and UK English". With AI as a supportive tool, it’s also easier to create an overview of what the treasury does. This serves as a preface to the policy document, making it accessible to everyone in the organisation. To get things started, I’ve included a simple checklist of the key elements below, and remember, proportionality is in order. If you liked this sign-up for regular posts

In sports, unforced errors are "mistakes made by players that occur independently of the opponent's action. They arise when the athlete has the opportunity to make a successful play but fails due to their own shortcomings, which can be attributed to errors in technique, focus, or judgement". This concept of unforced errors can also be applied to banking, trading, and risk management. Here, a mistake occurs due to a poor decision without any direct pressure from external factors or forces, such as competition or changes in the regulatory regime. It is simply a lapse in judgment, operational inefficiency, or failure to follow the processes established by the institution. For example, this might involve compliance errors where regulatory requirements or internal policies are not met. This is down to an oversight or lack of due diligence, even when the bank has the resources and knowledge to avoid such an issue. It can also include operational mistakes such as entering incorrect data, failing to meet deadlines, and mismanaging customer accounts. Examples in trading include execution errors where traders execute a trade incorrectly – buying or selling the wrong amounts or currencies, mixing up buy orders with sell orders, or placing an order at the wrong price due to a lack of attention or discipline. Emotional decision-making is another factor, where a trader deviates from their original strategy, driven by emotions such as fear of missing out, overconfidence, or loss aversion. In risk management, we might see a failure to follow risk controls, ignoring warnings from risk models, or a lack of diversification, resulting in concentrated portfolio risk. These are a few of the unforced errors I've seen: The realisation of asset swaps being held in an accrual environment to boost short-term profit and loss. The cause is management pressure to increase the business’s bottom line. Hedging long-term risk with shorter-dated contracts, anticipating that the hedge can be rolled over in the future at favourable prices. This assumes future conditions will be like the past, which may or may not happen, thereby increasing the risk of losses and reducing the hedge’s effectiveness. Investing in fixed-interest securities denominated in a foreign currency and hedging the risk using currency swaps. This strategy ignores potential regulatory changes involving capital and counterparty risk. Building up a portfolio of assets that appears to give a good return without fully appreciating how the risks could evolve. Examples include equity release portfolios and emerging market bond portfolios hedged with swaps. The nature of these underlying assets is complex and long-dated, and they may be subject to market illiquidity should they need to be sold. Buying and selling in the short term to benefit from market opinions, while failing to factor in the transaction costs. Building a new pricing model and entering numerous trades based on this new model without proper oversight as to whether it is accurate and robust. Ignoring basis risk. (Remember Base versus admin rates)? This failure to deeply consider the relationships between spreads used in pricing and the factors driving them resulted in a concentrated portfolio of loss-making deals. Basis risk of some type is embedded in most financial institutions. Holding onto investments that fall below credit criteria, in the hope that they will mature in the future and repay the principal sum. Adding higher-yield assets to liquidity portfolios because they are eligible from a regulatory standpoint while ignoring the potential impact of market disruption. This is especially concerning when these assets need liquidating during a crisis. Increasing risk limits because the underlying trades or assets are generating strong returns, only to discover that when things go wrong, the losses exceed anything experienced before. Looking at this sample, I can see some common themes. There is an over-reliance on assumptions about future market conditions. Whether it is hedging long-term risk with short-term contracts or building a complex portfolio of assets without fully appreciating or exploring how risks might evolve. Simply extrapolating current favourable conditions is not a sound way of making decisions. A second trend is the prioritisation of short-term gains at the expense of long-term stability. Traders and institutions often focus too heavily on immediate profit because that is how they are incentivised, which leads to downplaying potential future risks and leaves portfolios vulnerable to shifts in market conditions. Furthermore, many of the errors involve insufficient or ineffective risk management, as short-term performance has overridden longer-term considerations. There is also a lack of deep analysis and oversight to integrate robust controls that adapt to market dynamics and enforce changes to positions. Regulatory missteps also play an important role, where potential changes in the regulatory environment have not been fully accounted for, leading to higher capital charges in the future. This reflects a lack of forward-looking risk management. Finally, these issues are often compounded by emotional decision-making, driven by pressure from management or market sentiment. Whilst hindsight is a wonderful thing, two questions capture many of the unforced errors I have mentioned: "How are we prepared for changes in the market or regulations, and what plans do we have if things don’t go as expected?" "Are we taking any big risks for short-term gains, and how are we making sure these decisions are safe for the long term?"

I've been helping a company that wants to implement Value at Risk, (VaR). During this process, I've found myself reassessing how I think about risk. It has led me to reconsider what's important and that's what this post is about. VaR is a method of quantitatively measuring market risk. Market risk comes from daily price changes. If you hold a trading portfolio and you re-price, it affects your P&L and presumably, you want some measure of what any loss could be. About 30 years ago, JP Morgan came up with a single number: Value at Risk. VaR is supposed to tell you how much you can lose in one day for a given confidence level. You can think of it as an estimate, but it is not an absolute certainty. Losses can exceed all expectations. You can already see that it's somewhat complex and subjective. That might surprise you, particularly if you thought that hard and fast numbers were supposed to give you the truth. Let's put that into perspective. If you're running a trading book, how long can it take to exit a position? This has a bearing on the holding period - I mentioned one day, but it could be 10 days, 30 days, or 6 months. One of the advantages of value at risk is that you can calculate it for any traded asset, so in theory, you can add numbers across asset classes. But this ignores any diversification benefits. You will have days when some assets zig and others zag. This gives rise to two types of value at risk: an undiversified value and a diversified value. Which is more appropriate depends on your viewpoint. The undiversified value tends to overstate the risk, whereas the diversified value understates it, (particularly when everything is sold at once in times of stress). We also need to consider the confidence interval. Do you feel comfortable with 95 days out of 100, or 99 days out of 100? Then there's tail risk. You'll be aware that occasionally, everyone runs for the exit at once; it's a behavioural problem. Losses become much greater than anyone anticipated. There are a lot of moving parts, so implementing this model or any model presents a whole series of questions before things can take shape and once you start to build, you get a whole slew of operational issues. For example, Where does the data come from? How accurate is it? How do you capture it? How do you go about the value-at-risk model itself? How do you show the results? How do you determine whether the model works? It's why I've changed my opinion. I saw risk management as presenting risk so that you can see the exposures, whether they are market, credit, or operationally related. Ideally, you had many different ways to do this and could collate the information to cross-reference and gain a picture of the exposures you faced. It's like seeing a picture for the first time in black and white and then adding colour to give you more perception. However, risk is about understanding how an uncertain future can affect your business. It's not just a number from a spreadsheet. Importantly, many risks cannot be modelled with precision. The fact is you cannot rely on VaR when we need it most. Once you realise this, you need to consider how to cater for the unexpected. Namely, do you have stress testing? And, how is it created from plausible scenarios? Equally important, how do you build flexibility in your business so that you can alter things within an appropriate time frame? There also needs to be some way in which you link risk to capital, profit, and expertise. The value of these discussions far outweighs the results from the models themselves. It's a great opportunity to explore the way risk influences your business. If you liked this sign-up for regular posts

When I first started talking about the opportunities with artificial intelligence, I called it the "low-hanging fruit"—the simple but transformative ways businesses could begin to use AI. Back then, there was a clear gap between talking about AI and its actual use in organisations. I believe for many that gap is still there, but what is hard to take on board is the speed at which AI is progressing. It's exponential. Tools like GPT-4 have gone from being a curiosity to part of the toolkit in less than two years. Further, it's not just OpenAI; there is plenty to choose from. And the only way to understand what AI can do for you is to start using it—see for yourself just how quickly it can change your day-to-day work. AI feels like using spreadsheets back in the 1980s. Not everyone was convinced they were useful. But those who took the time to figure them out quickly saw how much easier they made things. What's more, for those of us in trading, it gave us the edge—and that meant money. Let me give you an example from my own recent experience. Last week I wrote Headlines Don't Matter . Using Google's NotebookLM, I took that post and turned it into multiple formats, each tailored to a different need: Briefing Doc: Headlines Don't Matter Theme:  This excerpt argues that constantly reacting to daily news headlines is detrimental to financial decision-making, especially for long-term investors. Instead, it advocates for focusing on building resilience and flexibility into financial systems to weather unforeseen economic shocks. Key Ideas and Facts: Short-term volatility is irrelevant:  Daily economic events and news, while seemingly impactful, are quickly overtaken by new developments and do not materially affect long-term trends. "The reality is that what’s news today will be overtaken by events tomorrow." Predicting the future is futile:  The author emphasizes the inherent unpredictability of the future, citing COVID-19 as a prime example of an unforeseen event with dramatic consequences. "As for next month or next year, there’s absolutely no way we can determine what will happen." Focus on resilience, not prediction:  Instead of trying to predict the unpredictable, the author recommends building systems robust enough to withstand shocks. "We need to focus on the degree of flexibility we have in the systems or businesses we are running." Historical examples of impactful shocks:  The excerpt highlights major historical events like the 1973-1974 Oil Crisis, Black Monday (1987), and the Global Financial Crisis (2007-2008) as examples of unpredictable events that caused significant and lasting economic disruption. "These were all completely unpredicted; the sequence of what unfolded was beyond comprehension at the time." Practical steps for financial resilience:   The excerpt suggests concrete measures to enhance financial resilience, including: Maintaining sufficient liquidity and capital reserves Diversifying credit risks Avoiding illiquid long-term assets Resisting the allure of high-risk, high-return investments in unproven areas Quotes of Note: "Reading too much into the news is positively damaging to financial decision-making." "These are the very types of things we need to protect ourselves against in the future. But here's the paradox: you won’t read about them in the news until it’s too late." "By avoiding the trap of reacting to every piece of news, we position ourselves to better withstand the unpredictable shocks that truly matter." Overall Message:  This excerpt encourages a shift in perspective from short-term reactivity to long-term strategic thinking in financial decision-making. It advocates for building systems that are inherently robust and adaptable, enabling them to weather unforeseen challenges and thrive in the face of uncertainty. FAQ: Headlines Don't Matter 1. Why shouldn't I base my financial decisions on daily headlines? Daily headlines often focus on short-term market fluctuations and breaking news that may not have a lasting impact on your long-term financial goals. These headlines can create a sense of urgency and encourage impulsive decision-making based on fear or speculation. The reality is that markets are constantly evolving, and trying to time them based on daily news is generally ineffective and can even be detrimental. 2. But what about major economic events? Don't those matter? Yes, major economic shocks do matter, but they are unpredictable and impossible to anticipate based on daily news. The events that truly reshape the financial landscape, like the 1973 oil crisis or the 2008 financial crisis, are often black swan events that catch everyone off guard. 3. If I can't rely on news, how can I make informed financial decisions? Instead of reacting to headlines, focus on developing a sound financial strategy based on your long-term goals, risk tolerance, and financial situation. This includes diversifying your investments, maintaining adequate liquidity, and having a plan to manage unexpected expenses. 4. What about expert analysis and predictions? Can't those help? While expert analysis can provide valuable insights, remember that even the most experienced analysts cannot predict the future with certainty. Economic forecasts are based on assumptions and models that can be disrupted by unforeseen events. Instead of relying solely on predictions, focus on understanding the underlying fundamentals of the market and your investments. 5. So, are there any situations where paying attention to the news is helpful? Yes, staying informed about broader economic trends and policy changes can be helpful for understanding the context in which you are making financial decisions. However, avoid making knee-jerk reactions based on sensationalized headlines or short-term market movements. 6. How can I protect myself from unpredictable economic shocks? The key to navigating unforeseen events is to build resilience into your financial strategy. This involves: Maintaining liquidity: Having enough cash reserves to cover expenses and avoid being forced to sell assets at a loss during market downturns. Managing risk: Diversifying your investments across different asset classes and geographies to reduce exposure to any single event. Avoiding excessive leverage: Don't take on more debt than you can comfortably manage, especially when interest rates are rising. 7. What should I do when faced with a major market downturn? Resist the urge to panic sell. Remember that market downturns are a natural part of the economic cycle. Instead of making impulsive decisions, review your long-term financial plan, consult with a financial advisor if needed, and consider opportunities to buy undervalued assets. 8. What's the main takeaway when it comes to news and financial decisions? Focus on what you can control: your own financial plan, risk management, and long-term goals. Don't get caught up in the daily noise of headlines. Stay informed, but make rational decisions based on sound principles rather than emotional reactions. A Study Guide: Headlines Don't Matter Short-Answer Quiz Instructions:  Answer the following questions in 2-3 sentences each. Why does the author argue that daily headlines are irrelevant for most investors? Explain the difference between price takers and price makers in the context of the treasury market. How does the author characterize the relationship between news consumption and financial decision-making? What specific characteristics do the major economic shocks listed in the text share? Why does the author use the example of COVID-19? According to the author, what is the paradox of preparing for major economic shocks? How does the author define flexibility in the context of financial businesses? What does the author recommend regarding liquidity in managing financial businesses? What advice does the author offer concerning investments in long-term assets? What is the main takeaway from the excerpt regarding navigating financial uncertainty? Short-Answer Quiz Answer Key The author argues that daily headlines are irrelevant for most investors because they are primarily focused on short-term fluctuations and speculation, which do not significantly impact long-term investment strategies. Most investors are "price takers," meaning they react to market prices rather than influencing them. Price takers, like most investors in the treasury market, accept the prevailing market prices. In contrast, price makers, such as large institutional investors, have the power to influence and set market prices through their trading activities. The author suggests that excessive news consumption can negatively impact financial decision-making. The constant influx of information, often negative and speculative, can lead to anxiety and impulsive decisions, deviating from well-considered long-term strategies. The major economic shocks listed in the text (e.g., the 1973-1974 Oil Crisis, Black Monday, the Global Financial Crisis) share the characteristics of being unpredictable, unprecedented in their unfolding, and impactful enough to cause lasting changes in economic and financial systems. The author uses the example of COVID-19 to illustrate the unpredictable nature of major economic shocks. Its sudden emergence and widespread impact highlight the limitations of forecasting and emphasize the need for flexibility and preparedness in financial strategies. The paradox of preparing for major economic shocks is that they are inherently unpredictable and often unforeseen. This means that while planning is crucial, the specific events we prepare for may differ significantly from the actual shocks that occur. The author defines flexibility in the context of financial businesses as the ability to adapt and withstand unexpected economic downturns and market volatility. This involves maintaining sufficient liquidity, diversifying risks, and avoiding overexposure to illiquid assets. The author recommends holding sufficient liquidity in financial businesses to provide a buffer against unexpected losses and market downturns. This allows businesses to weather financial storms without resorting to drastic measures that could compromise their long-term stability. The author advises against excessive investments in long-term assets that are difficult to liquidate during times of market stress. Holding a significant portion of illiquid assets can limit a business's ability to respond effectively to sudden market changes and economic shocks. The main takeaway from the excerpt is that focusing on long-term strategies and maintaining financial flexibility is more effective than reacting to short-term news and market fluctuations. By prioritizing resilience and preparedness, businesses and individuals can better navigate the unpredictable nature of financial markets. Essay Questions Critically evaluate the author's argument that daily headlines are irrelevant for long-term investors. Do you agree or disagree? Provide specific examples and evidence to support your position. Discuss the challenges and benefits of maintaining flexibility in financial management. How can businesses balance the need for stability with the ability to adapt to unforeseen circumstances? Analyze the impact of the major economic shocks discussed in the text on global financial markets. To what extent have these events shaped current financial practices and regulations? Explain the author's concept of "price takers" and "price makers." How do these roles influence market dynamics and investment strategies? Develop a comprehensive plan for a hypothetical business to prepare for potential future economic shocks. Consider specific strategies for managing liquidity, diversifying risks, and maintaining operational flexibility. Glossary of Key Terms Bid-Offer Spread:  The difference between the price a buyer is willing to pay (bid) and the price a seller is willing to accept (offer) for a security or asset. Capital:  Financial assets or resources used in the production of goods and services. Credit Crunch:  A sudden and severe shortage of credit availability, typically caused by a loss of confidence in the financial system. Dow Jones Industrial Average:  A stock market index that tracks the performance of 30 large publicly owned companies listed on the New York Stock Exchange (NYSE) and the Nasdaq. Economic Indicator:  A statistic used to assess the overall health and performance of an economy, such as GDP growth, inflation, or unemployment rates. Exchange Rate Mechanism (ERM):  A system used by European countries to maintain stable exchange rates between their currencies before the adoption of the euro. Global Balance of Power:  The distribution of political, economic, and military power among nations on a global scale. Inflation:  A general increase in the prices of goods and services over time, leading to a decrease in the purchasing power of money. Liquidity:  The ease with which an asset can be converted into cash without significant loss of value. Price Maker:  A market participant, typically a large institution, that has the power to influence and set market prices through their trading activities. Price Taker:  A market participant who accepts the prevailing market prices without having the ability to influence them. Recession:  A significant decline in economic activity, typically lasting for two or more consecutive quarters, characterized by negative GDP growth, rising unemployment, and reduced consumer spending. Solvency:  The ability of a company or individual to meet their long-term financial obligations. Sterling:  The currency of the United Kingdom, also known as the British pound. Subprime Mortgage:  A type of mortgage loan granted to borrowers with lower credit scores and higher risk profiles. Treasury Market:  The market where government securities, such as bonds and bills, are traded. Podcast: Headlines Don't Matter I asked the AI to discuss the blog from the context of a Non-Executive Director: This is just scratching the surface. AI can take one piece of work and turn it into committee meeting notes, onboarding materials, regulatory documents, or client communication—all in a matter of minutes. It’s incredibly versatile. The podcast was a revelation. It opened up a new way to disseminate information to people who prefer to listen rather than read, and what’s more, you can tailor the content specifically to the audience. My work habits have changed. I turn to Large Language Models, they save time and often give better results whether that's for finding information, making sense of it or using it to provide insight to others. I didn’t learn that from reading about AI—I learned it by using it. A year ago, I wrote: “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.” That’s true today. The benefits are there for the taking, and getting started isn’t complicated. Sign up and experiment with it, and see what it can do. It’s that simple.

Two recent events have dominated the headlines: a spike in oil prices due to unrest in the Middle East, and a sudden weakening of Sterling following the governor's hint that interest rates could decrease faster than expected. Such news might lead to expectations of capital flowing out of the pound rather than into it. Reading these headlines, you could be forgiven for thinking the world is coming to an end.   When I was trading, these sorts of events really mattered. There was nothing like breaking news or unexpected economic figures to get dealers back at their screens, ready to react to any unanticipated outcome. The level of frenetic activity that would kick off immediately after an economic indicator came out unusually high or low was astonishing.   But unless you're an active trader, this sort of thing doesn't matter. Most people who execute transactions in the treasury are passive participants—they are price takers rather than price makers. While it's better to deal when the market is trading normally because bid-offer spreads are relatively narrow, there's little benefit in trying to anticipate economic events or news to enter into transactions at more favourable rates, no matter how tempting it may be.   The reality is that what's news today will be overtaken by events tomorrow. If we hang on to every headline, we're always looking for the next piece of news to frame our decision-making. Our decisions become constantly in flux, ebbing and flowing with events, leaving us feeling uncomfortable and uncertain.   The fact is, that tomorrow is unpredictable. As for next month or next year, there's absolutely no way we can determine what will happen. And I can guarantee that whatever does unfold will be something unexpected. Remember COVID-19 and how it affected our lives? It came out of the blue like a meteorite.   Daily headlines may be interesting, but they are not helpful. Reading too much into the news is positively damaging to financial decision-making. We're wired to be fascinated by bad news, which encourages us to consume more and more of it. We pore over the analysis that goes with it. Economists and observers write pages about what could happen as a result of recent events. This is speculation—it makes tenuous connections between current events and second-and third-order effects, making the likelihood of the prediction being correct so remote that it's not helpful. It's guesswork.   So, when does the news matter?   It matters when it unfolds into a major shock that is outside anything that can be perceived at the time and leads to permanent changes in income, consumption, employment, growth, productivity, and the global balance of power. Think of events like:   - The 1973-1974 Oil Crisis : This marked a turning point in global energy markets and had far-reaching economic and political consequences that continued to shape policy and market behaviour for decades.   - Black Monday (1987) : On 19 October 1987, the Dow Jones Industrial Average lost 22% of its value in a single day, triggering a global decline across major markets.   - The Early 1990s Recession (1990-1991) : Lasting five quarters, this recession was caused by high interest rates in response to rising inflation from the Lawson Boom and efforts to maintain British membership in the Exchange Rate Mechanism. It resulted in a 25% decline in company earnings and unemployment rising to 10.7% by 1993.   - The Global Financial Crisis (2007-2008) : Triggered by the collapse of the subprime mortgage market, this crisis led to a severe global recession. The S&P 500 lost more than 50% of its value from its peak in October 2007 to its lowest point in March 2009. It caused widespread bank failures, credit crunches, and a sharp decline in consumer spending, affecting economies worldwide.   - The COVID-19 Recession (2020) : While brief, this was the deepest recession since 1709. The majority of the GDP decrease occurred in March and April 2020 due to the COVID-19 pandemic. It triggered an inflationary shock and a wider cost of living crisis that continues to impact the economy. And while these types of shocks will happen again, you won’t read about them until they’ve happened.   These events share some similarities. They were all completely unpredicted; the sequence of what unfolded was beyond comprehension at the time. None of them became news headlines until they were already happening, and no amount of foresight or planning could have insulated you against them.   These are the very types of things we need to protect ourselves against in the future. But here's the paradox: you won't read about them in the news until it's too late, and the effects on your business will only be known after the event. So, is there anything we can do to improve our chances of survival?   Yes, there is.   We need to focus on the degree of flexibility we have in the systems or businesses we are running. For financial businesses, this means:   - Holding sufficient liquidity to buy time without it being ruinous. - Having sufficient capital to cover unexpected losses. - Spreading credit risks to maintain a balanced portfolio, ensuring that no single exposure is enough to create a solvency issue. - Avoiding investments in long-term assets that cannot be liquidated in times of market stress. - Not piling into uncharted areas of business just because the returns are so attractive that they cannot be ignored.   By avoiding the trap of reacting to every piece of news, we position ourselves to better withstand the unpredictable shocks that truly matter.

We have a budget due on Wednesday, 30 October 2024. If this budget is poorly received by the market, gilt yields could rise again. This may lead to losses, especially for longer-dated gilt holders, whose higher interest rate risk makes them more vulnerable. It's a risk that is already on the table; investors don’t have to wait until budget day to see its effects. If you are concerned about the potential fallout, the only practical solution is to shorten the duration of your portfolio. Who is particularly affected? Gilts are often seen as risk-free investments—after all, they are backed by the government, and the expectation is that the government will always repay. However, while repayment is virtually guaranteed, the value of gilts can fluctuate significantly. This makes them far from risk-free for investors looking at market value , particularly those managing positions on a mark-to-market basis. Even if you hedge with swaps, there's no guarantee that credit spread risk won't move against you, creating unexpected losses. One of the key considerations when investing in gilts is understanding that longer-dated gilts carry more risk. Simply their interest rate risk and spread risk are greater. The only effective way to reduce this risk is to shorten the maturity or duration of the holdings. The risk posed by changes in government policy can further complicate matters for gilt investors. This is particularly relevant when considering how both interest rate and spread risks can interact with broader fiscal decisions. A clear example of this was the mini-budget announced on 22 September 2022, which included significant tax cuts and expansionary fiscal measures estimated at £45 billion per year. The markets reacted negatively, and gilt yields spiked sharply—specifically, the 30-year gilt yield increased by 120 basis points over just three days, one of the largest increases ever seen in such a short period. This dramatic rise was driven by concerns about the government's ability to finance these cuts, potential inflation, and future borrowing needs. The upcoming budget serves as a timely reminder for investors managing gilt portfolios to reassess their strategy. If your primary goal is to find a safe place to park liquidity do you need to run these risks? Shortening the duration is an option that is open to you.

Back when I began my trading career, risk management was almost non-existent. We didn't have many tools, and traders marked their positions. It was simpler, but there were many blind spots. As the markets got more advanced, we started using tools like basis point value (BPV) and simple yield curve shifts to understand risk better. By the 1990s, value at risk (VaR) became popular—it was a single figure that showed how much you could lose. It worked well in normal times, but it didn’t hold up when markets became highly volatile. Because of this weakness, we now use a variety of risk measures to get a more complete picture and cross-checking these different measures is a practical way to ensure our information is reliable. It's like verifying a news story—you want to see if multiple trustworthy sources confirm it before accepting it as accurate. In a recent speech , Huw Pill, Chief Economist at the Bank of England, talked about why cross-checking forecasts is important for making better decisions. He explained that relying on just one forecast could lead to blind spots, especially in a complex and unpredictable economy. Pill emphasised using different models to double-check assumptions and build a stronger foundation for decision-making. This isn’t just about monetary policy—it’s also a lesson for risk management in finance. In the past, relying on just one tool like VaR led to mistakes because it was too narrow and didn’t provide the full picture, especially when markets became unpredictable. Cross-checking different risk measures, like Pill does with forecasts, helps make sure we’re not putting too much faith in just one model or number. Pill’s approach of using multiple models is about turning guesswork into a solid analysis. But there is a downside: just because a model is complex doesn't mean it's always reliable. If a model is a "black box"—meaning we don’t fully understand how it works—we need to question how reliable it truly is. To be fair, Pill identifies this risk; all that complexity might not hold up because the connections in the models aren't fully understood. Common sense and an understanding that the economy is a complex system remind us of the limits of any risk model. For senior executives, the message is simple: do not rely on just one perspective. Cross-reference your metrics. Use different models, stress-test different scenarios, and make sure you’re getting independent confirmation. Also, remember that complexity in models can be a risk itself—many people do not want to admit they do not fully understand a model, which can lead to weak assumptions becoming the basis for important decisions. Whether you’re looking at interest rate risk, credit exposures, or market volatility, using multiple trustworthy measures provides greater confidence that what you’re seeing is accurate. Just like Pill uses cross-checks to validate forecasts, you should be cross-referencing to validate your risk measures—as long as you understand the models you are using.

While reducing risk is often helpful, trying to remove it completely can be damaging. If banks matched all their positions perfectly, they would struggle to turn a profit because they wouldn’t be adding any real value. The key is to take the right kind of risks—those that offer a return—while avoiding exposure to potentially unlimited losses. Comparisons For clarity, let's look at three examples. By comparing these, we can gain insights into when risk is potentially beneficial and when it is not. Fixed-Rate Retail Bonds: When customers purchase a fixed-rate retail bond, banks immediately face an interest rate risk. We build up the risk, wait until it reaches a significant level, and then hedge it. This flexible process requires judgment and isn't purely mechanical, but the risks are largely under our control. Funding Fixed-Rate Loans with Variable Rate Deposits: In this scenario, banks fund a fixed-rate loan book using variable-rate retail deposits. In this case, the retail deposit rate is an administered rate. The risk is controllable but significant. Prepayments and Extensions in Mortgage Portfolios: Managing prepayments and extensions is particularly challenging. Customers tend to prepay when interest rates drop and extend when rates rise, creating uncertainty. The risk is difficult and costly to hedge . These examples illustrate the varying degrees of manageability. When considering risk appetite we need to think about which risks make sense. Are they: Market-Offsettable: Where a liquid and continuous market allows you to back out exposures if needed. Non-Hedgeable: Where the market doesn’t offer a way out and you can only reduce exposure by limiting volumes or building features into products to cap the downside. Driven by tempting margins some firms take on more non-hedgeable exposures from the outset hoping things will work out fine. In this situation ask "Are we taking enough of the right type of risk?"

Work in Treasury revolves around communication, with reading, writing, discussion, and analysis serving as the primary tools. Information is exchanged through various formats—spreadsheets, documents, and meetings—each playing a vital role in how we collaborate and deliver results. Given this, AI appears to be a promising tool for us, as it is inherently multimodal (capable of processing and generating text, images, and other types of data) and can handle the varied nature of our tasks. However, while AI excels in certain areas, it also has notable shortcomings, particularly in the context of treasury workflows. Ethan Mollick referred to this phenomenon as the “jagged edge” (the uneven performance of AI, excelling in some areas while falling short in others). To understand AI's role in treasury, it’s important first to recognise where AI truly excels. For instance, AI can process lengthy documents and distil them into concise, useful formats, such as summarising key points from regulatory papers or converting them into memos for non-experts. Additionally, AI is skilled at analysing structured data and generating insights, often without the need for data downloads—simply capturing an image allows AI to interpret it. Similarly, you can take a snapshot of a risk report , have AI explain it, and even tailor the explanation for a specific audience, all within a short time. Fact-finding is another area where AI helps; tools like Perplexity (an AI search tool that provides concise answers to complex queries) can sift through web content and provide summaries that directly address your questions. Moreover, AI can summarise meetings, producing transcripts or key documents that capture what’s discussed and agreed upon. It can even conduct virtual surveys, acting as a customer and providing feedback on your product—something that traditionally would require hours and significant expense. These capabilities illustrate how quickly AI has advanced in a short space of time. However, it’s crucial to acknowledge AI’s limitations. One noticeable issue is inconsistency in its responses—you might ask the same question multiple times and receive different answers. This variability can be both advantageous and frustrating, especially when you’re relying on AI to generate scenarios based on its training data. Often, these scenarios result in familiar approaches rather than novel ideas. To unlock more creative outputs, you need to prompt the AI repeatedly, which requires time and patience. In addition to scenario generation, AI can struggle in other areas. Despite recent improvements in AI’s ability to handle basic tasks in Excel, the process remains cumbersome and error-prone. Consequently, relying on AI to build Excel models isn’t practical yet, which is disappointing given how crucial spreadsheets are in treasury work. (AI is said to be much better at coding, something I don’t have the skills to judge). Reflecting on my experience with AI over the past 18 months, it’s been a mix of trial and error. Sometimes I stumble upon surprisingly effective solutions; other times, I spend hours trying to get AI to perform a task, only to be met with failure. The challenge in treasury is that most professionals lack the time or inclination to experiment extensively with AI, given the demanding nature of their roles. Nevertheless, this hasn’t deterred many from using AI at work even when it’s not officially available. There is considerable evidence that employees use AI privately to tackle work-related challenges. This trend raises concerns about sensitive information being uploaded without adequate oversight. It also leads to less internal discussion and collective learning about AI’s capabilities and limitations—a missed opportunity. Given these dynamics, how should AI be introduced in treasury? Simply providing employees with access to tools like ChatGPT or Claude and expecting them to figure it out on their own isn’t sufficient. While some might successfully adopt AI this way, the process is generally slow, laborious, and often doesn’t yield the best results. To fully benefit from AI, users need to explore its capabilities thoroughly, which requires a more structured approach. A practical starting point is to experiment with pilot use cases (small, controlled experiments) that identify straightforward and time-consuming tasks—prime candidates for AI assistance. Analysing your workflow, particularly repetitive tasks that AI could handle, is key. For example, writing, idea generation, and creating strategic documentation are areas where AI could make a significant impact. Collaborating with colleagues on these pilot projects allows for the comparison of results and helps build a solid set of use cases to understand how AI can best support your work. However, before these pilot projects, it’s important to ensure that your team is comfortable with using AI. As noted earlier, experimentation is necessary, but not everyone will feel at ease with it right away. Therefore, training is not only helpful but necessary to build confidence. The goal is to foster a mindset where AI is seen as a helpful co-worker, albeit one whose output can range from brilliant to average. I've found many attempts with AI may not work, but some will, and these will offer huge returns. It’s much like the 80/20 rule (a principle suggesting that 80% of outcomes come from 20% of efforts). Encouraging this trial-and-error approach and sharing insights across the team will help build a strong case for AI adoption in treasury. Moreover, employees with limited treasury experience can achieve tasks that would typically be beyond their reach, as long as they understand that AI is an aid, not a replacement. This brings us to the issue of hallucinations (AI generating incorrect or misleading information). While these were a significant concern a year ago, they are less frequent now, though they still occur. The problem is that AI can be a convincing source of misinformation, and only by carefully reviewing its output can you detect inaccuracies. Thus, the skills required for using AI include not just flexibility in experimentation and prompting but also thorough analysis to ensure accuracy, particularly in the area we work in. Some might argue that it’s better to wait for AI to improve and become more integrated into existing products. However, I believe this would be a mistake. As AI evolves, delaying its adoption leaves you behind, risking obsolescence without realising it. The key is to think ahead—where do you see AI in three to five years? Although predictions are challenging, there’s a strong case that AI will soon be able to handle complete workflows (end-to-end processes managed by AI), a concept referred to as “agency” (the ability to delegate tasks to AI, with humans overseeing the results). We will need to rethink what it means to work in Treasury because much of what we currently do will be done by AI. In this world, advanced skills, experience, and understanding of how to work with AI will become increasingly important. In the shorter term, the upcoming release of GPT-5 (the next version of OpenAI’s language model), anticipated in the next few months, could bring significant advancements. I plan to revisit some past use cases with the new model to see how things have progressed.

When I first started work, there were no signs of computers in the office. Then, the first standalone PCs appeared with their large floppy drives and very limited programming ability; principally, we used Lotus, a forerunner of Excel. Not many people knew what to do with it, but it helped do straightforward calculations like adjusting margins on loans to achieve a return on capital. The takeoff, if I can call it that, of computing was relatively slow, and most people were sceptical.   A big change occurred around the same time as the 1987 stock market collapse. I remember we started to use Lotus or Excel in pricing transactions, particularly repos and swaps. The 1987 collapse was also my first real experience of seeing people made redundant in dealing rooms. It was ruthless, and those made redundant were just given a bin liner and told to empty their desks and vacate the office. It put the fear of God into quite a lot of my colleagues, particularly those with mortgages and families. Suddenly, the lucrative business of trading in the City became a lot more precarious.   Against this backdrop, the derivatives markets started to open-up, with more and more people trading in swaps and options. Here, Excel spreadsheets transformed dealers' fortunes; if you had a reasonable understanding of numbers and markets, you could find opportunities to make almost risk-free money. It seemed like the gravy train was on the rails again, but very soon this vision became less realistic as margins were competed away. Two different types of traders emerged: those who used PCs and those who didn’t.   Why do I relate to this story? I think certain parallels are happening today with artificial intelligence. It's not dissimilar to our use of spreadsheets all those years ago. If you work in treasury and risk, you must now be wondering how your job will be impacted by the new technology, and you may also worry about your future, particularly as some people suggest that much of what you do will be done by artificial intelligence at some point in the not-too-distant future.   Let's consider for a moment the sorts of things you do on a day-to-day basis in Treasury. You're involved in things like liquidity and cash flow management, funding and investment strategies, interest rate risk modelling and monitoring, foreign exchange exposure management and dealing regulatory compliance and oversight. You may also be involved in identifying and assessing risks, developing policy, managing and monitoring limits, stress testing and scenario analysis, and reporting on all of this to your management.   What this involves is a solid foundation in finance and an understanding of the business, in order not only to look at things in detail but also to get the big picture. Increasingly, you're expected to be tech-savvy, and that's about working with complex financial software and tools for data analysis and forecasting.   So, where are we today with AI?   Here I'm going to draw on a survey called the 2024 Work Trends Index Annual Report . Some of the key aspects of this report show that AI adoption in the workplace is accelerating very quickly. Some 75% of global knowledge workers now use AI, and 46% of AI users started using it less than six months ago. Employees are generally enthusiastic, with 90% of AI users saying it saves time, and 79% of leaders agree that AI adoption is necessary to stay competitive. However, some 60% of them worry that their organisation lacks an implementation plan.   In short, AI is transforming roles rather than replacing them, leading to a notable change in skills in the next five years, and new AI-specific roles are starting to emerge in companies.   How could things change for us?   Let's just assume for the time being that AI continues to progress as it has done over the past 18 months, but there is no significant breakthrough, it just gets better. Here are a few things we could point to in terms of where AI would become more involved.   Liquidity and cash flow management become more precise, possibly with AI providing some real-time, accurate forecasting and suggesting optimal cash positions. AI starts to help you understand market data and recommend funding and investment strategies. Interest rate risk monitoring becomes something more dynamic, with AI helping you assess potential impacts and suggesting some hedging strategies. Regulatory compliance becomes more streamlined in the normal workflow of updating policy documents. Risk identification becomes something that is looked at more comprehensively, with AI now processing more data and spotting potential risks that may have been harder for us as humans to analyse. Stress testing and scenario analysis become more sophisticated and less set in stone, with more frequent help from AI to run complex simulations. Risk reporting is enhanced by data visualisation from AI, which now allows complex risk profiles to be more understandable to stakeholders across the business.   AI becomes a very helpful assistant to those of us working in treasury and risk. We spend less time on data gathering and routine analysis, and more on how the business works and the strategic thinking around it.   The skills required will need to evolve. While a strong background in finance will remain important and understanding the business will be crucial, professionals in treasury and risk will need to become more adept at working alongside AI tools. Think in terms of understanding the basics of programming and prompting to oversee and interpret information that is generated using AI. It's not just about using the AI itself, but looking at what AI generates and turning that into something that is an actionable business strategy.   What happens further out?   I'm just going to play out a couple of scenarios here that are relevant. Let's suppose that there is a successful transition to AI agents—agentic AI. This is where AI can take over a lot of your day-to-day routine tasks. What would this mean for the treasury or risk department?   Liquidity management becomes largely automated, with AI systems moving funds between accounts and optimising cash positions across instruments. AI agents manage investment portfolios, making real-time decisions based on market conditions and the company's strategy. Interest rate risk is managed using predictive AI models that automatically adjust hedging strategies. Foreign exchange management becomes proactive, with AI systems executing trades to minimise exposure based on your risk appetite. Regulatory compliance is continually monitored and managed by AI, which adapts to new regulations as they are introduced. Risk identification becomes predictive; AI systems don't just identify the risks that we currently have but help us understand future ones based on trends and data.   In this environment, people working in treasury and risk will evolve into what we could loosely call AI-human collaboration managers. Their role will be more about setting the overall strategy, defining the ethical boundaries that AI can work within, and handling the complex, nuanced decisions that require human judgment.   The next scenario is a paradigm shift.   It is where there is artificial general intelligence (AGI) that can match or exceed human capabilities across all domains that we operate in. In an AGI world, the traditional tasks of treasury and risk management will be entirely handled by AI systems. AGI would manage all aspects of liquidity, funding, investment, and decision-making to optimise financial outcomes while balancing multiple complex factors. Risk will be managed across the board, with AGI systems continually monitoring, predicting, and mitigating all forms of financial and operational risk. Regulatory compliance would be automatic and proactive; AGI would not only follow current regulations but would start to anticipate and prepare for future regulatory changes.   In this scenario, roles are entirely altered. Just about everything that we currently do will be done by AI. So what would we do?   Our input would be left to things that are unique to human beings. That means we would set the overall vision for the company and its financial future, aligned with broader goals in society. Ethics becomes more important because it's all about human value judgements. We may provide some sort of human touch in stakeholder relations—maybe we'll explain strategies to shareholders, regulators, and the public on a face-to-face basis. We will have the role of making sure that AGI aligns itself with our own purpose and values, and maybe we'll collaborate with other people, like policymakers, to shape the regulatory environment in the future.   Far-fetched? Maybe, but rapid change is inevitable and so the future becomes even more uncertain. It's what we felt back in the 1980s with the advent of PCs. We couldn't see around the next corner; we just had to meet it as it came up. Those that survived and flourished took hold of the new technology and used it creatively. In the scenarios I've painted, the idea that we're going to have to work closely with AI is almost a given. I just can't see how you can ignore it as it becomes more powerful. This means learning about what it can do, taking every bit of your workflow and testing it out regularly in AI to see what can be achieved. This will make you a more valuable employee and potentially set you up well for the future.   With agentic AI a lot of the boring stuff can be removed from our day-to-day workload, and this presents both opportunities and challenges. It allows us to be more efficient; we can be more data-driven, and we can use our uniquely human skills—those of strategic thinking, ethical reasoning, and interpersonal communication—to develop and run our business. This reskilling may not lead to a huge cut in jobs; it might just be that we can do a lot more with the people who currently work in the firm.   The big question is: what if AGI is achieved?   It's safe to say all bets are off. Very few people will be required to run your traditional treasury and risk management area. The jobs that remain will be much more in a senior oversight and governance capacity. I don't think this is unique to treasury and risk; this is something that would happen to many white-collar jobs.   You may be sceptical, and who knows what the future may bring, but it is certainly a scenario that is worth considering.   I don't think there's anything that you can do at this point that will fundamentally alter your future; you must get on in the current environment with what you're given and use your skills and natural curiosity to improve your standing in the workplace. This is exactly what we did all those years ago. Some people moved out of the business completely, whilst others moved in. The mistake would be that you think your current skills are good enough to see you through the next decade. This would have worked in the past but not now.

I was sitting one weekend morning, reading a copy of the Financial Times , only to see that this topic has resurfaced. ANZ, one of Australia’s biggest banks, is considering whether it should ban its staff from drinking alcohol during working hours. This follows bad behaviour that took place on the trading floor in Sydney.   Apparently, traders had been intoxicated in the office. The chief executive informed a group of MPs that there had been internal complaints about some of the traders who had breached the company’s code of conduct, returning from lunch under the influence and then using profanities on the trading floor. The chief executive argued that the incident was exaggerated and questioned whether it was truly a scandal. Nevertheless, he was concerned about restoring the bank’s reputation. Years ago, I had the pleasure of being a trader at a well-known bank in the City. It was fun, and there was money to be made. Things were a lot freer and easier. Alcohol flowed almost every day. I can recall colleagues being reprimanded for sleeping on park benches after a heavy night out with brokers (the charge being that it was unbecoming of a senior manager of a bank). My boss at the time insisted that all his team went out on Wednesdays and Thursdays for beer, and the penalty for not joining in was ridicule.   There was alcohol on the premises; it was on the desk, in the fridge, and on the table for lunch. Trades were even conducted over the bar phone in the local pub. The phone was set on speaker, and prices were relayed from the trading pits.   Drinking with brokers, colleagues and clients was frequent, and occasionally people were unwell afterwards. One client wrote:   "Thank you for a wonderful lunchtime seeing what you chaps in the City do. I can't remember how I got home, but I do remember my wife finding me in the railway sidings at 4:00 AM. Once again, it was a pleasure!”   All of this was expensed; the card just went behind the bar. Lunch for four, in today’s money, could be close to £750, with three-quarters of that spent on fine wine.   Dealers turned up to work, cigarette in hand, without their jacket, keys, or wallet—unable to remember where they’d been, just that they’d lost everything the previous night.   A fight erupted over socks. “Those are mine.” A late night had ended with six dealers all crashing at one flat.   Is it any wonder that a broker reminded me the bank was known as “Grog Bank”? This was almost 40 years ago, and how things have changed. Now, it’s hard to buy a coffee for a client, and alcohol at lunchtime... don’t even ask.   And this is where I’m surprised.   I’ve shared a few things that I saw and experienced, and there’s a lot I’ve left out because it’s completely unprintable even in hindsight. But in defence, the climate of the 1980s was far, far different from that which we are in today.   I honestly thought the old days had gone, but apparently, they haven’t. If it’s still happening, it’s probably occurring elsewhere too. In today’s climate, you’d have to be mad to allow it. The ongoing persistence of this problem indicates that some institutions haven’t fully addressed the challenges associated with the misuse of alcohol among traders.   We all know that it impairs judgment; that’s why you cannot drink and drive. It leads to poor decision-making; in our world, that means costly errors. These can arise from confusion between buying and selling, fat fingers and big figure mistakes, inappropriate hedging and position-taking, leaving exposures open, data entry errors and reversals, to name but a few.   It can damage client trust and confidence, attract negative media coverage, and harm the public perception of the bank. It may also lead to the bank being at the centre of claims from other staff who have been adversely affected by colleagues under the influence at work.   This will almost certainly lead to scrutiny from regulators and stakeholders, engaging management time in trying to sort out and firefight something that shouldn’t have occurred.   This is why establishing and enforcing proper policies that ban alcohol consumption during working hours is crucial. This may also include monitoring staff to detect and address potential substance misuse. Furthermore, regular training about the responsible use of alcohol, to ensure it doesn’t impact personal wellbeing and professional conduct, is essential.   I know that some of the people I worked with all those years ago could have benefited from counselling and support programmes because they genuinely struggled with alcohol-related issues formed while in a stressful job and expected to consume prodigious quantities.   Let’s leave the drink out of the workplace. I’m sorry for all of those who missed out on the 1980s—yes, it was fun, well, at least that’s what I remember. But today is far better for you!