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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!

If you've been away on holiday this summer, are you feeling refreshed and ready for autumn?  Most people generally agree that taking a break now and then is beneficial for both the mind and body.   A colleague I used to work with didn’t have that view.   We worked at the same bank, and in the six years he was there, he took no days off and regularly worked weekends. When he eventually left, he had accumulated over a year of untaken leave, which the bank paid out in full.   Less widely discussed, but valuable for employers, leave of absence is an effective way to uncover wrongdoing within the organisation.   Many frauds require constant attention from the perpetrator to maintain the deception, meaning they can rarely afford to take time off. When you examine many rogue trading cases, certain patterns often emerge.   Take the John Rusnak case, for example. The trader was involved in the valuation of trades and manipulated the figures to hide losses. He created false trades that appeared profitable to mask these losses. He also coerced colleagues into actions detrimental to the bank, such as suppressing confirmations or bypassing proper workflow on valuations. It is challenging to maintain a fraudulent scheme like this without interfering with aspects that could expose it. This often involves not only executing transactions but also managing risk, settling transactions, and convincing management that everything is above board.   This is why leave plays a significant role in uncovering financial fraud, especially in trading environments. It disrupts ongoing schemes because fraudsters are forced to step away from their roles, making it harder to maintain false records and hide losses.   When traders go on leave, their positions and records are typically reviewed by colleagues and supervisors. This independent oversight increases the likelihood that irregularities will be spotted and questioned.   Leave also separates the trader from other staff managing transactions, which can help uncover unauthorised practices or systems the fraudster may have coerced colleagues into adopting. There is also a deterrent effect; traders know that mandatory leave increases the risk of any fraud being discovered during their absence.   But what do we mean by "leave," and what type of leave can help uncover fraud? It’s important to have a well-thought-out policy. There needs to be mandatory time off; this isn’t about occasionally choosing to take a holiday; it’s a requirement that the employee is absent from the office for a period mandated by company policy or industry regulations.   The leave typically needs to be continuous, with two weeks generally considered sufficient to disrupt ongoing fraud.   Furthermore, the employee should not have access to trading systems, work-related networks, or communication with colleagues about work matters, ensuring complete disconnection.   Some organisations go so far as to implement policies where the timing of mandatory leave is announced or given at short notice, preventing perpetrators from preparing for their absence.  During which audits or reviews of the employee’s work, trades, and records may also be appropriate. Handover of trading responsibilities should include sign-off of the actual positions, allowing for closer scrutiny of the trades and their valuations.   Of all these points, the most challenging today is the issue of hybrid working. As work increasingly takes place outside the office—although less so in trading—it’s crucial to ensure that leave of absence means a genuine break from the systems. Various approaches can be taken, which are beyond the scope of this discussion, but it’s essential to have a strategy to prevent remote access during leave periods, especially in this situation.   Looking at the Rusnak case, it’s tempting to think that such frauds are a thing of the past and that current systems and controls are robust. However, this is a timely reminder that as past events fade from memory, we should not forget what happened. It is a small thing but if you cannot provide an auditable account of your employees’ annual leave—ensuring they are absent and disconnected from systems for at least two consecutive weeks—then it’s time to do it.   A vacation is not only good for employees; it’s great for the business too!

One thing I’ve learnt about AI is that it can be very helpful, yet it sometimes falls short of the task at hand. This became clear during a recent assignment focused on developing a value-at-risk model. This article covers both the success and failure I experienced with AI. Here’s what worked. A colleague introduced me to Otter.ai, a service that provides meeting transcripts (and more). Although other providers like Zoom offer similar services, and you can transcribe audio files to use with tools like GPT-4, this was my first time using Otter. It was straightforward. We invited the AI to the meeting as a participant, and by the end, it had generated a transcript and a set of notes. But it didn’t stop there. I could use either a single transcript or multiple ones from client meetings to generate various documents, whether a list of action points, memos in a specific style, an explanatory document, or just questions from the meeting. Otter’s capabilities seemed limited only by my imagination and needs. For treasury teams, this kind of AI application offers immediate, practical benefits. By automating the process, you can concentrate on what’s said rather than being busy taking notes. It means nothing is missed, and Otter’s ability to generate documents directly from transcripts can help save a significant amount of time, particularly if you find writing is a slow process. Now, what failed. The assignment required building a prototype to demonstrate some of the main features a working risk model would have. In treasury and risk management, building Excel spreadsheets like this is routine, and many banks heavily rely on Excel. This led me to wonder if AI tools like GPT-4 or Claude could create a working value-at-risk model in Excel. I tried this a few months ago with little success. Would it work now? I gave the AI some data and parameters. Then I instructed it to ask any clarifying questions one at a time before proceeding—a method I find useful for prompting. The AI asked sensible questions, and I thought this time it would work. However, the results were disappointing. Claude struggled to understand the data in the spreadsheet, while GPT-4 managed better. With some prompting, GPT-4 eventually calculated daily price changes and daily volatility. Despite this progress, I still couldn’t generate a working model. Frustratingly, AI is excellent at discussing concepts and can provide a detailed analysis of different risk approaches, but when it came to writing formulas in Excel and manipulating data to create a functional spreadsheet, it just didn’t happen. For us in treasury and risk, this highlights a critical point: while AI can already do many tasks, particularly in analysis and decision support, it may not (at the moment) do everything you expect. However, this experience also underscores the importance of staying informed about AI’s changing abilities. If you haven’t tried it yet, you should consider initiating small pilot projects to test AI in specific areas, such as data analysis or simple modelling tasks. By doing so, you can assess the practical benefits without disrupting key processes. Despite this setback, I’ve learnt a few valuable lessons. Experimentation is key when working with AI. You need to explore different models—like GPT-4 and Claude—or applications such as Otter to discover what AI can do. Being open to using both models and applications expands your understanding of AI’s potential. Over the past 18 months, I’ve developed an understanding of what AI is likely to accomplish and where it may fall short. I think it could do a lot more and that the limitation is me rather than it. Using multiple AI models can be beneficial. In this case, GPT-4 outperformed Claude in generating a working model, although this is not always the case. Sometimes Claude’s larger context window and its separate dedicated window (artifacts) make it an improvement over GPT-4. While I primarily use GPT-4, I turn to Claude when GPT-4 gets stuck. My introduction to Otter came through a colleague who had been using it for some time. I quickly realised that, despite my experience with other AI models, using Otter was not much different. They all operate through a prompt window, and some offer multi-modal functionality. Understanding this fundamental operation is useful when you’re working with new applications. Introducing AI to your team shouldn’t just involve giving everyone access. Instead, you should encourage experimentation, promote AI literacy, introduce pilot projects, collect experiences and knowledge, circulate that information within the firm, and quantify the benefits. There should be a coordinated effort, perhaps led by a group or committee, to manage this process. Learning and sharing are crucial mindsets. For example, knowing how Otter works—thanks to my colleague—has saved me a considerable amount of time and significantly improved my efficiency. It’s about collective solutions and keeping track of how people use AI. The benefits extend beyond time-saving. When I was introduced to Otter, it wasn’t just a time-saver for working on documents; it also allowed me to concentrate on the conversation without the distraction of taking notes, which I found particularly helpful. I also appreciated knowing that the conversation was recorded and could be reviewed later. While AI won’t do everything, it is improving. In some cases, its limitations are due to our lack of imagination or our inability to prompt it effectively. I’m sure others have successfully used AI to build spreadsheets—it just didn’t work out for me. However, AI can now handle spreadsheets in a more functional way than it could a few months ago and can execute basic commands using the data contained within them. Where this technology will be in three to five years is an intriguing question, particularly for those of us working in treasury. It seems inevitable that we’ll be able to instruct AI to build fully functional risk management models that provide both quantitative information and qualitative insights. That’s a topic I intend to explore in a later blog. For now, many of our roles, especially those in knowledge-based sectors, will undergo significant changes. The future is undoubtedly exciting, but for many, the speed of these changes will also be unsettling. If you liked this sign-up for regular posts

The Challenge Banking relies heavily on confidence; without it, customers withdraw their money. One significant source of risk is the perception that a bank's capital is insufficient to sustain losses, leading to a run on liquidity, which becomes unsustainable. Leverage plays a major role because risk-taking weakens capital, prompting cash outflow. These elements are interconnected and should be managed as a whole. The best people to see the emergent exposures are those at the coal face, often dealers, and the flow of information from them to management needs to be encouraged. Metrics like limits and RAG reporting do not cover this well. The challenge is to create a flexible approach to risk management by acting on this information. First, let’s look at connectivity.   Micro Case: Hedging Fixed-Rate Mortgages Hedging fixed-rate mortgages with swaps removes a large portion of interest rate risk but simultaneously increases credit, basis, prepayment, and liquidity risks. This is why some firms get caught out—they focus on the rate risk and ignore other mismatches, which over time can become disproportionate. A broader oversight of the entire risk picture is necessary.   Macro Perspective: Lessons from the 2008 Crisis Numerous cases from the 2008 crisis illustrate a similar problem. Northern Rock, for example, funded its mortgage business growth by creating mortgage-linked bonds, which were sold to investors to raise more liquidity for lending. When the funding dried up, the reliance on this source became evident. The message is clear: undiversified businesses feel the full force of market disruption. It is crucial to appreciate that doing things in isolation, whether concentrating on gap exposures or following a mono-line business model, heightens risk. We need to understand the linkages.   Further examples include:   Counterparty Risk : Lending to a counterparty might seem safe initially, but changing market conditions can put your cash at risk if the counterparty fails. Collateral Management : Using collateral to secure trades appears sensible until market volatility strikes and you need cash to facilitate a margin call. Trade Dependencies : Entering trades with one or two counterparties might be expedient, but it increases reliance on them. Finding substitutes at short notice is a challenge. This delicate balance of risks and linkages isn't limited to individual institutions—it goes much deeper. This systemic fragility means that in the event of a bank failure, central banks often need to bail out multiple commercial banks to prevent lasting economic damage. No wonder regulators now focus on reducing systemic risk, where the failure of one financial institution triggers a domino effect. Let’s look at what steps you can take to improve things.   Insights from Traders Although the Treasury isn't responsible for overall risk control, it's vital to communicate concerns as they arise. Therefore, ongoing dialogue with risk management is one way to improve what's happening and this is all about culture.   When front-office staff are encouraged to engage and share their observations with people outside the treasury, it fosters a flow of information and debate about what’s going on, helping the firm adapt to changes in the market. In all the market crises I’ve seen, the front office detects changes in markets first, sometimes months before things go seriously wrong. Such is the nuance of information that flows minute by minute. Recognising and acting on this to create a learning business model is a senior management responsibility and goes beyond traditional risk reporting.   How you do this is not fixed, but having the right channels and forum that encourages discussion improves things.   Painful Surprise Linkages are second-order problems that do not fit traditional limit and traffic light (red, amber, green) reporting.   For instance, a simple gap report may show interest rate exposure within guidelines, giving a sense of security. However, large risks can build up at short maturities, and because short-duration risk is seen as benign, it’s not perceived as a point of pain. But occasionally, particularly when there are changes to macroeconomic policy, severe market disruptions can arise, and short-term interest rates can shift dramatically, leading to substantial costs.   The best people to understand these risks are those dealing with them daily. It's essential to capture and disseminate their knowledge within the firm to prepare for what may arise. If you don’t encourage this, every once in a while you will be in for a surprise. If you liked this sign-up for regular posts

I worked with a client who provided a vivid lesson on the importance of the iceberg of risk. This is the story. The bank's treasury invested in a portfolio of long-dated gilts, using swaps to hedge against interest rate fluctuations. They were confident in their approach, after all, risk had been neutralised, and that’s what the reports showed. After some time a trend caught their attention. The value of their gilts was rising steadily while the swaps used for hedging remained relatively unchanged. Sensing an opportunity, they decided to liquidate their positions, resulting in substantial gains. Naturally, they expected commendation for their success. However, when the news reached the Board, their reaction was not so complimentary. How had they made money when there was no exposure? What transpired was a significant hole in their oversight; the lack of proper monitoring for credit spread risk. The potential for the Gilts to increase in value relative to the swaps had been completely overlooked. All was not as it seemed and it’s a timely reminder when we look at risk reporting that some things can be hidden despite our best endeavours. Reporting Shortfalls The crux of the issue was that the reporting mechanisms in place did not capture the hedging mismatch. The traders focused on the immediate profitability of the Gilt positions, calling it a windfall gain, without considering the broader implications of what had occurred. The institution was blindsided by a risk that should have been apparent. Credit Spread Risks Credit spread risks of this type abound, you immediately see the effect in a mark-to-market environment but not when you are in accruals. Here such exposures can remain hidden but can be painful. To prevent such oversights, it is crucial to incorporate an analysis of credit spread risks within the business. One effective approach is to implement a simple credit delta measurement to assess the sensitivity of the portfolio to changes in spreads. This involves calculating the change in the value of both the assets and their hedges given a shift in the relationship. It’s far from perfect but shows you the magnitude of what you are facing and if you want to go further you can try some scenarios. For example, consider a stress where credit spreads widen by 100 basis points. In this situation, the value of the gilts would decrease significantly, while the swaps would not provide sufficient protection, leading to substantial losses. By running such scenarios, the bank can better understand the potential impact of adverse market conditions and take pre-emptive measures to mitigate risks if they are considered excessive. On a slightly different note, it’s not a dissimilar exposure to the one pension trustees experienced on their Liability Driven Investment exposures in September 2022. Encouraging Openness One of the critical aspects of the situation I encountered was that the business prided itself on its internal communication about risk. But, for whatever reason, the risk remained opaque even though the traders were likely aware of the exposures early on. Whilst on this occasion we are talking about windfall gains the Board rightfully asked why such risks were not discussed earlier. This means probing deeper into the communication channels and ensuring that there are no barriers to transparency between the traders and senior management. This leads us to the role of the second line which should actively engage in reviewing trading/hedging strategies and independently verifying the risk exposures. If you don’t have the firepower in this area slippages like the one I’ve discussed will likely happen. In this case, it’s a combination of issues. The traders should have flagged things earlier, the second line should have twigged things, and the Board should have asked for a detailed analysis of the risk being put on before the event - after all, it was big. Ultimately, the key takeaway is that these things do arise, you can reduce the chance of it happening to you, but you can’t eliminate all risks unless you shut everything down.     If you liked this sign-up for regular posts

One of the principles ingrained in me is that risk management should be kept separate from the area or individuals taking the risk. The argument is that there is a conflict of interest: if those responsible for making money also handle risk management, their motivation for profit may overshadow everything else, taking us to a place we don’t want to be.   This model became an industry standard after the Nick Leeson incident, leading to Barings Bank's collapse. Consequently, banks were mandated to establish independent risk management to assess front-office activities. While this separation has clear benefits, it also presents a potential problem: it may signal to traders that risk is not their concern as long as they operate within set limits.   An example is when dealers adopt an "it's not my problem" attitude when given credit limits.   They utilise these limits without questioning the broader implications, assuming that as long as they remain within their boundaries, all is well. This can be even though the credit market is pricing a particular credit risk differently. This mindset is potentially dangerous and can lead to things being overlooked.   Another example arises from collateral management where a withdrawal of market liquidity leads to sudden and large price change. Traders know this can and does happen and if it’s not factored into your oversight, you will be in for a nasty surprise.   We are talking about situations where there is no right or wrong approach, one clear benefit of segregating risk management is that it removes the personal motive for gain from risk-taking, ensuring that institutional values are upheld. However, this can also mean that valuable insights about risk, which traders possess due to their close market interactions, may go unnoticed.   To address these issues, fostering a culture of mutual respect and open dialogue between risk managers and traders is crucial.   Risk managers should understand what it is like to be a dealer or trader—the pressure to generate P&L, the job's complexity, and market nuances not captured in reporting.   For example, they should be aware of what happens when markets “gap” and what this can mean for spread and basis risks, as well as practical advice such as "if you can't price it, don't do it." Understanding hedge rebalancing from convexity and risk pricing at extremes (e.g., deep out-of-the-money options and volatility smiles) is also vital; we need to speak the same language and respect each other's expertise.   Management should encourage an open culture where risks are discussed both formally and informally. This involves creating an environment where traders and risk managers can collaborate on their understanding of the exposures. It is also important to recognise that great outcomes do not always imply good decision-making and poor outcomes do not always imply weak decision-making. The focus should be on how the decisions themselves are arrived at and whether they are thought through.   What can help?   Rewards influence actions and should be skewed towards long-term performance, encouraging staff to see beyond a one-year horizon and ensuring they see the benefit of making prudent decisions. Conversely, attempts to make a quick profit should be discouraged unless you have known skills in this domain. Recording who says what and documenting decision-making processes ensure transparency and provide a basis for learning. The focus should be on whether decisions were well thought out and if the potential risks were adequately considered. Simple oversight tools can bridge the gap between risk management and traders. Metrics like deltas can provide insights into how small changes in interest rates or credit spreads (e.g., 0.01%) affect valuation, P&L, and liquidity. Subjecting these deltas to tail risk scenarios by asking "what if" questions can help prepare for extreme events. For instance, during the LDI problem, a simple delta analysis based on a significant shift in the gilt market would have highlighted the leverage and potential cash needs arising from derivative trades. Incorporating AI can provide a broader range of scenarios allowing risk managers and traders to evaluate outcomes and develop contingency plans.   Whilst I recognise the value of separate lines of defence, risk management should be something everyone is engaged in - because we all have different views and opinions the collective discussion will be valuable.   If you liked this sign-up for regular posts

Doing new things and launching new products is always exciting; however, from my experience, these innovative ideas often lead to long-term, ongoing problems. These real challenges can cost us significant amounts of money and management time to unravel. Stepping back to carefully consider how we might avoid such difficulties arising in the future doesn’t come naturally but is worthwhile. Here are three questions you should ask. Is it a Structured Product? Structured products are a classic example and, of all the things I’ve seen, are the easiest to avoid. They may appear simple and attractive on the surface, such as structured deposits offering higher interest rates. However, these come with hidden risks, such as selling options embedded in the product. These options are credit, currency, or interest rate driven and may lead to significant losses when the underlying risks materialise. If someone wants to sell you a structured product, ask yourself what’s in it for them.  If you can’t answer that, don’t even consider it Is it Long-Dated? Another area of concern is long-dated assets; these may be bonds or loans. They seem attractive due to their yields but pose substantial risks the further we go out in time, especially when hedged with derivatives. Long-dated derivatives can lead to greater margin calls, impacting liquidity during times of stress. Additionally, basis risk from the credit spread differential between the asset and the hedge can cause P&L volatility, especially in a mark-to-market environment. Does it Have the Illusion of Liquidity? Investments that appear liquid in normal conditions can become illiquid in stressed markets. Structured assets or those requiring special hedging can be difficult to sell when needed most. Any associated derivatives complicate liquidation, often forcing reliance on the original counterparty, which is not ideal. Why This Matters The difficulties associated with new products are often disproportionate to the financial benefits they bring and unravelling the problems they bring consumes an inordinate amount of senior management's time. That's time spent dealing with the complications rather than strategic initiatives. The question then arises: why do we pursue new things? The answer lies in the inherent excitement of new ventures and the desire to contribute to the business's bottom line, furthermore, the greater the pressure placed on the treasury to generate income, the more likely it is that we find riskier solutions. But that's not all. Many of the long-term problems we have to deal with are created outside the treasury, often from a business initiative that wasn’t thought through. This leads to the underlying problem sitting on the balance sheet, and the only place to manage it is in the treasury. Examples include many retail products that have structured risks (drawdown and repayment options); they are on the balance sheet for years, pay interest that doesn't naturally offset, and are subject to redemption, extension and withdrawals that we can't fully control. This is where problems can arise particularly when the marketing and pricing of these products is outside the treasury. If the motivation is to shift product, the sales volume can be achieved by adjusting the price, which may no longer truly reflect the risk you are getting into. From what I’ve seen, it’s a tricky discussion that’s made worse if marketing and treasury are separate profit centres. The “you are taking my P&L” argument has been going on for years and is at the core of things. What Can We Do? We need to be clear about what we want from the treasury. Pressure for returns will inevitably lead to increased risk-taking, often by doing new things. For all but the biggest banks, this makes very little sense. But what about those risks outside the treasury that have the potential to haunt us? Yes, there needs to be proper strategic thought; new products must align with the long-term objectives of the business, but this is vague. A more concrete approach is for the treasury to have a voice in product development and pricing A solution I've come across is where the treasury is responsible for product pricing, while marketing focuses on volume. The marketing team commits to the volume they will do and writes the internal ticket to manage that risk with the treasury. Treasury is now on the hook for managing the risk, whilst any over- or under-hedging costs are picked up by marketing. It’s amazing how this can focus discussion and create true collaboration to avoid costly mistakes. If it's something you don't do ask yourself this: What measures do you have in place to foster effective communication and collaboration between treasury, marketing, and product development teams, and how do we ensure new products are priced to reflect their risk? If you liked this sign-up for regular posts