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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

I've written this article for smaller banks, building societies, and corporates. It’s a topic I haven’t explored before, but it warrants consideration: the relationship you have with your banking counterparts. It’s an area where you can add significant value, not in outright profit but in cost savings by getting fair value. After all, giving up a basis point has the same effect on the P&L as paying a fee, but because it often goes unnoticed, it is frequently overlooked. The inspiration for this piece comes from my experience working with large banks in wholesale markets and later with smaller firms.   There is a marked contrast in how large banks interact with each other compared to how smaller firms are treated. When big banks deal with one another, they treat each other as professional counterparties. Consequently, pricing, execution, and the negotiation of legal agreements occur on a level playing field. Both sides understand the intricacies of the game. However, the dynamics are very different when you work for a smaller firm.   From the outset, you need to be clear that you are a customer of the bank, which immediately creates a conflict of interest. You want the best price, good execution, and fair documentation, but your counterparty views you as an opportunity to make money.   Smaller businesses face additional challenges. Dealing with multiple counterparties is difficult because they often lack the resources for thorough due diligence and relationship maintenance. This frequently means relying on one or two main banks for most services. Your banking relationships may have been in place for many years, and it may be time for a review. Let’s consider some important issues.   Firstly, it’s easy to assume that all banks have the same credit standing and rating, but they don’t. Your main banking relationship likely involves taking credit exposure through unsecured deposits with the counterparty or their operational ability to perform. Therefore, it’s important to be aware of the counterparty’s credit standing and have alternatives should it deteriorate. This main counterparty will also handle payments on your behalf. While you usually expect your main bank to perform this task consistently, it’s wise to have a backup bank for payments to settle intraday commitments.   Credit risk and payments are not the only considerations. You may also need to hold surplus liquidity with the counterparty, making credit risk important again. Additionally, the bank should offer you a fair price for cash deposits, and you can reference this against market rates. Once you are locked into a banking relationship, competitiveness can decline. Hence, dealing with more than one financial institution is advisable.   For longer-dated investments, such as bonds, competitive pricing remains crucial. A bank may excel in cash management but fall short in handling longer-dated products. If you regularly buy bonds, ensure your bank can consistently execute trades at fair market prices. Comparing several counterparties side by side helps gauge their performance. For regular transactions, consider using a broker to secure the best execution.   Now, let’s consider derivatives. Managing interest rate exposure through swaps requires a capable bank. Given the complexity of derivatives, you need a master agreement and familiarity with margining. Legal counsel should evaluate any agreements to avoid less favourable terms and conditions, as many aspects are negotiable. Ensure you have the skills to independently calculate and manage margin payments.   Typically, you may have only one or two counterparties for derivatives, which can create issues. Price competitiveness is critical, as these deals are often long-term. If your counterparty knows you are reliant on them, there is little incentive for competitive pricing. Additionally, your business model may require continuous hedging, necessitating ongoing transactions. Therefore, having at least one other counterparty ready to trade is prudent if, for whatever reason, your normal go-to is not able to trade.   Apart from payments, cash management, long-term investments, and derivatives, other factors also matter. Your relationship with banks isn’t solely about pricing and execution. Your bank must understand your business. For example, if you’re a building society, do they comprehend the issues around using derivatives for hedging? The way banks interact with you, whether through a central point of access or specialist salespeople, is significant, does it work for you? For complex transactions like derivatives, trust is key and if you rely on your bank for advice or expertise to help you manage risk, ensure you independently assess anything that is suggested.   To make sure you are getting the best value and service from your banking relationships, regularly review and evaluate what you are getting. Appraising your relationship with your bank(s) should be an annual exercise. This assessment can be both quantitative and qualitative. Quantitative measures include calculating how much you pay the bank in bid-offer spreads for transactions. This is not an exact science but more an approximation or estimate of the value of your business. For instance, if you regularly place overnight deposits and they take four or five basis points each time, it’s straightforward to calculate the value you give up over the year. Likewise, applying bid-offer spreads in bond markets and using mid-prices as fair value estimates helps you understand transaction costs. For derivatives, a simple basis point value for the swaps you transact over the year multiplied by a spread from mid-price provides a value estimate.   Subjective measures include the bank’s ability to offer quick and efficient service, understand your business, and provision of a range and depth of products and hedging solutions. Personal relationships also count. Once you’ve established quantitative and qualitative measures, you can build a scorecard, (see below), to assess bank counterparties’ performance, identifying areas for improvement or the need for new banking partners.   As evident from this article, managing bank relationships is largely in your hands. There is an inherent conflict of interest, as banks profit from your business. Your counterparties’ credit risk and long-term strategy are crucial to their ability to meet your needs. Some banks excel in specific areas, and you may need several counterparts for a comprehensive and continual suite of treasury products, from overnight deposits to fixed income and derivatives. Even for simple businesses, having at least two banks ensures operational resilience. Medium-sized financial institutions or corporates may require relationships with two to five banks to meet their treasury needs.   Keeping a regular scorecard of bank performance is helpful. A few basis points on longer-dated transactions can be costly if overcharged. Therefore, regularly reviewing and managing these relationships ensures you get the best value and service from your banking partners. Example Scorecard You can easily build a scorecard like this to evaluate your banking relationships. Simply add in your specific products and tailor the criteria to your requirements. This example combines both quantitative measures, such as cash deposits and bond investments, and qualitative measures, including operational performance, legal flexibility, and business understanding. By using this comprehensive approach, you can gain a complete view of the value and service offered by your banking partners, identify strengths, and address any areas needing improvement. If you liked this sign-up for regular posts

I never thought I would be writing this, but the regulation that banks now face as a result of the financial crisis in 2008 has given them some benefits. In particular, in the area of risk management, the regime is onerous. There are rules which we could describe as prescriptive, and there are also regulations that, while not telling you how to do things, make it very clear that senior executives are personally responsible. This means that banks and building societies have been forced to align their thinking with that of the regulator. By way of contrast, if we look at non-financial institutions that experience the same types of risks that banks do, the pressure has not been the same. Regulation is not as much of a concern; it is about whether the risk management you are putting in place represents value for money in a commercial sense. This is evident when I work with non-banks, where commercial reality outweighs any regulatory need. That said, things in the banking world are relevant and applicable to the corporate sector: let’s say it’s a chance to benefit from what banks have gone through (every cloud has a silver lining). With this in mind, let us look at how intraday risk can improve what we do. Intraday risk is the potential for financial loss or disruption that can occur during the course of a day and is primarily due to the management of liquid assets. Whilst your intraday risk may not be of the same magnitude as that of a bank, it’s worth putting it into context. As a trader, the bank I was working for had to finance the positions we collectively held. At the time, there was an active and liquid interbank market, so all you needed to do was borrow the right amount on an overnight basis. It was that simple. Two decades later, I was having a conversation where the Head of Operations explained the difficulties of settling extremely large bond trades. This was all about timing differences. Sometimes payments were hundreds of millions or billions of pounds, and they needed to be made before receiving a similar matching sum of money into the account. This intraday exposure created problems—the payment bank would not grant credit for such a short-term window of the magnitude required. The seriousness of this situation was explained as the reputational risk and cost associated with a failed settlement. There are several steps we can take to mitigate the risk, including real-time settlement, collateralised borrowing, and access to the Bank of England. These options aren’t available to non-banks. But, the risk can nevertheless be managed. Centralising liquidity management into one area or unit to ensure all surplus positions across the business are consolidated and managed centrally is one approach. Depending on the size of the business, this is not always easy because it relies on obtaining real-time information from various accounts. In addition, some type of risk analysis is beneficial as it allows you to understand the problem. This analysis would generally look at the cash flows themselves, but it may also monitor things like counterparty risk to ensure your cash is held with sufficiently robust counterparts. Almost irrespective of the size of the business, having several relationship banks reduces the reliance on any one particular bank to effect payments. This redundancy in your process is considered a useful risk mitigant. Furthermore, it should be tested regularly. These are all things borrowed from banking, and there’s more. Improved cash flow forecasting that also looks at real-time data is a very helpful way of understanding cash inflows and drawdowns in the business on a day-to-day basis. This helps us get closer to answering the question, “how much liquidity?” Steps could include obtaining information from the business; this is preferably daily time series data over the course of, say, a year. Such an approach would track outgoing payments and incoming receipts during the day to provide a real-time view of the intraday liquidity risk faced by the treasury at various points during the day, for example on an hourly basis or at the end of the day. We can then dig into exactly what causes the drawdowns because the larger they are, the more we have to set aside to deal with their eventuality. We could go further. For example, using a simple model that combines real-time monitoring of cash positions with some statistical modelling. This borrows a value at risk type approach which can then be improved by stress testing. Having this information to hand helps set internal limits for intraday liquidity and develop contingency plans for scenarios when and if it is insufficient. How far you go down this route is up to you; it all boils down to how intraday shortfalls would affect your business. You can think like a bank even if you aren’t one. If you liked this sign-up for regular posts

Hedging means different things to different people. For example, a bank manages its interest rate risk by converting everything to a variable rate. In this way, the balance sheet is floating, and interest rate movements only make minor changes to value. On the other hand, a company views hedging as a fixed cost of money, and therefore the interest payments are known. Looked at from either perspective, both approaches make sense, but they are different. When we talk about hedging, we think of matching risks so that they net out. However, in truth, this is very difficult. If you hedge everything perfectly, you are flat, but the cost of doing this can be excessive because you are continually entering into transactions to transfer the risk to somebody else, and this costs money. Financial institutions recognise this, and despite talking about hedged risk, often there are remaining mismatches which are far from hedged. There's nothing wrong with this because these exposures are part and parcel of running a business. However, we do need to understand exactly what is matched and what isn't. One of the big problems is that risk reports fail to show the nuances of hedging adequately. This failure to show where the risk lies can go unnoticed for a very long time. Furthermore, even if it doesn't show in the P&L it comes out in the wash at a later date. As a consequence, not only do we have a situation where you cannot see the risk, there is no immediate way in which you can feel the risk affecting the value of what you are doing. A classic situation where this occurs is with what we can call loosely basis risk, where hedging is 90% accurate. During normal times, the 10% unhedged risk causes little problem, but occasionally, it can assume a disproportionate value, particularly when markets have been disrupted. It is also useful to be aware that these basis risks are frequently not caught by risk limits, and traders know this. In this situation, if you put pressure on dealers to make more, these unreported exposures are more likely. Another feature of these risks is that they start off being quite small, but over time the balance sheet grows, and with it, the exposure increases. It is therefore important to ask whether you have captured all the things that influence the real mark-to-market. Let's put that into perspective: at a micro level, it involves putting together individual trades and the hedges that go with them and asking, "What affects the value of the package?". If some inputs or scenarios can change the value of what you have, then by definition, you are not fully hedged. If you do this properly, you will find a whole series of situations where balanced books simply aren't that. It may be benign, but we need to get some idea of the magnitude of the risks that are being run. Here, estimates or approximations are incredibly powerful because they can put the exposure into context for you. Is it small, medium, or large relative to the size of your business, the profits that you are making, and the skill sets that you have? One of the preferred tools I like to use is simple delta values, that on their own don't tell you about how much you can lose, but they can give you a good insight into the size of the risk you are potentially running. It's a type of smell test; if it's ripe, it probably isn't palatable. These simple methods can be extremely helpful if you are not conversant with what drives day-to-day changes in value. Traders have a model of risk largely built in their heads, and they can determine when it's small, medium, or large based on their experiences of the past. It's a sort of human value at risk model. It takes time and skill to develop this insight, and for many people, a simple back-of-the-envelope calculation can provide some surprising insight into what's going on. The next time you hear that the risk is hedged, dig deep to find interesting results. If you liked this sign-up for regular posts

Many years ago, when I was trading swaps, we received some unusual enquiries from a certain type of bank. This led to many swap transactions, most of which were medium to long-term. The interesting part was that these trades were equal and opposite. What do I mean by this? They simultaneously bought and sold the swap, creating pairs that effectively hedged each other, with the only cost being the bid-offer spread. This seemed strange, so we invited them to our office to learn more. They explained that swaps were excellent because, at the end of the year, they could review the pairs and find that one would be in profit and the other in loss. The profitable swap would be moved to the trading account, where the profit was recognised immediately. The losing swap was held in an accrual account, where the loss was spread over several years. This was attractive because dealers' bonuses were based on year-end profits. This story may make you smile, but it's true. Of course, today, this wouldn't be allowed. Items for trading are marked to market, and items for hedging are held in accrual accounts. Moving items from one to the other would raise questions. I share this story because, while you might think this doesn't happen anymore, similar issues still exist. So, what’s going on? You are likely familiar with using swaps to hedge fixed-rate products like mortgages and savings. For less sophisticated organisations, each swap matches a specific part of the retail product exactly. There are accounting rules to ensure this if you're holding items long-term in an accrual environment. More sophisticated organisations interpret this more broadly, taking an overall view of the balance sheet risks and hedging accordingly. This is called balance sheet hedging. Initially, things look balanced, no matter the measure—simple gap reports or more complex delta reports. But over time, things change. Hedged products often decrease in size faster than the derivative due to prepayment risk. This generally happens, leading to an over-hedged position, which causes problems. In theory, the over-hedged position needs trimming to keep risks neutral. But there's reluctance because of the early redemption cost. Consider why customers repay their mortgage or withdraw fixed deposits—it’s often because better rates are available. If so, the underlying hedge is likely underwater, and breaking it incurs a penalty. Here’s the dilemma: do we break the swap and incur the penalty, or use it as a hedge in our business? The choice has significant consequences. Breaking the swap now sends the cost to the P&L account immediately. Keeping it spreads the cost over the transaction's maturity, accruing the cost. Many managements keep the excess hedge, spreading future costs. Does this matter? It’s a moot point. Either you accept the loss upfront or spread the identical cost over time. There’s little to choose from—it impacts either this year’s profit and loss heavily or future years lightly, but it totals the same. So why spread it out? It appears more acceptable. But is it good management? I argue it isn’t. Here’s why: the retail products being sold have underlying options where the customer is long, and you are short. This is fine if you price it into the product and are aware. But spreading things out makes the losses from granting customer options seem smaller than they are and we become more tolerant of the situation. This isn’t the only example. Pipeline hedging for mortgages or fixed-rate deposits can lead to similar problems if expected business volumes don’t materialise. A related issue is thinking an over-hedged position is manageable as a directional play on interest rates. But we know predicting rates is risky. An excess hedge can lead to behavioural risk, supporting a risky stance on rates that has proven detrimental to numerous traders. What can we learn? Regularly rebalancing hedge portfolios is essential. If this causes pain, ask why and what can be done to avoid it in the future. Otherwise, you repeat the same mistake. The story of banks doing mirrored swaps is amusing, but similar issues persist, and we often fool ourselves by ignoring them. If you liked this sign-up for regular posts

Risk tolerance is a concept we frequently refer to, but how often do we have a serious conversation about what it truly means? By its very nature, risk tolerance is not a fixed, static thing. It changes and fluctuates day by day, influenced by a variety of factors. Time and time again, I've witnessed how easy it is for people to take on more risk when markets are on the upswing, and then quickly cut back on that risk when things start to go down. Link pay to profit, and guess what happens? Risk tolerance increases, as if by magic. Persuasion plays a role too. If you have a strong and powerful advocate pushing for taking on more risk, it's amazing how quickly colleagues fall in line and accept that view as well. Accounting also gets a look in. If you run your business in a mark-to-market environment, where everything ends up in the P&L today, it can be a remarkably sobering experience. Is it any wonder that some of the biggest losses I've seen have been in lending? The spreading out of pain over years through the loss of net interest margin seems, for some reason, far more acceptable than taking the hit upfront. But in the end, the net effect is the same - it hits capital. All these factors increase the difficulty of pinning down exactly what risk tolerance truly is. The techniques we use to understand market, credit, liquidity, and operational risk further complicate matters. They are predominantly quantitative measures, but you can't reduce everything to pure numbers - you need to apply subjectivity as well. I've frequently seen limit breaches, which incidentally breach the stated risk tolerance, be overridden by judgment. More evidence that tolerance is a flexible concept. But one of my main concerns is fungibility or lack thereof. Each category of risk has its own limits, and however you look at them, you can't easily compare. Loan exposures cannot be added to liquidity exposures to get anything meaningful, and this is a problem. You can't directly compare individual risks with each other. However, you can get a subjective feel for how much overall risk you are taking - is it low, medium, or high? Specifically, what is the chance that your capital will be significantly depleted from the individual risks you have? Once you boil things down to this very basic common denominator, you are in a much better place to question what you are doing. And there are three important questions to ask: 1.     Why should the market pay us to take this risk? 2.     Can we correctly price this risk? 3.     What experience do we have in managing this risk? Your answers to these questions should ultimately frame your risk tolerance. By constantly revisiting these fundamental issues, you can develop a clearer understanding of your true risk tolerance, beyond the day-to-day fluctuations and influences.

Treasury and dealing have undergone a remarkable change over the past four decades. What was once a field dominated by intuition, paper-based processes, and "barrow boys" has evolved into a highly regulated, technology-driven profession that demands a diverse set of skills from its practitioners. In this blog post, I'll discuss the key changes that have shaped things and how today's professionals can take out some of the tedium to make things fun again. From paper blotters to electronic trading Forty years ago, the treasury world was a far simpler place. Dealers relied on paper blotters to manage positions, trades were conducted over the phone, and the concept of risk management was rudimentary at best. Regulation was virtually non-existent, and dealers were expected to make money based on their gut instincts rather than sophisticated analysis. Today the picture couldn't be more different. Electronic trading platforms have replaced blotters, and the sheer volume and size of trades have grown exponentially. Information is now abundant and instantly accessible, thanks to resources like Bloomberg. Bid-offer spreads have tightened, and the number of major players in the market has shrunk, with most participants now classified as clients. New skills As things have evolved, so too have the skills needed to succeed in this field. Today's dealers are more likely to hold graduate and post-graduate qualifications, and proficiency with spreadsheets and data analysis is essential. Treasury professionals must be able to not only interpret data but also use it to forecast P&L and risk. The modern treasury environment also demands strong communication skills. Dealers must be able to explain complex strategies and concepts to a wide range of people, often without relying on jargon or technical terminology. Report writing, meeting preparation, and the ability to articulate the pros and cons of different approaches are now core competencies for treasury professionals. Moreover, dealers must be aware of the cognitive biases that can cloud judgment, such as recency, confirmation, and overconfidence. These concepts were once seen as virtues but academic studies have shown they aren't. Regulation rules One of the most significant changes in the treasury world has been the rise of regulation. The days of freewheeling, unconstrained dealing are long gone, replaced by a complex web of rules and oversight. Risk management is now at the forefront of every trade and the fear of censure looms large. Despite the challenges posed by regulation, some fundamental truths about markets remain unchanged. They are inherently unpredictable, and even the best-laid plans can unravel at the first sign of trouble. Extreme events, while unlikely, occur more frequently than expected, and we must be prepared to act quickly to adjust to the situation we face. AI makes light of work As the demands continue to grow artificial intelligence (AI) can help. It can be a powerful tool for automating routine tasks, such as report writing and policy updates, freeing up time for more strategic and interesting work. But AI's potential extends far beyond mere automation. It can also help dealers improve their skills in areas where they may be lacking, such as communication and data analysis. By using AI for scenario planning, tail risk assessment, and other nuanced tasks, a deeper understanding can be obtained. Perhaps most importantly, AI can make treasury work more engaging and enjoyable. By eliminating the tedious, repetitive aspects of the job you can focus on work that drew you to the field in the first place. Maybe it's not quite like the old days but the barrow boys were street-smart and saw an opportunity when it presented itself.