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  • A Podcast Recommendation

    One of the podcasts I like is the Rational Reminder: "Sensible investing and financial decision-making, from two Canadians". It provides great insight into personal finance, and a recent episode is well worth listening to if you are an index investor. My views on low-cost investing are best summed up in my blog post from 14th January 2021, "Do less and make more". The inspiration for that post came from the late Jack Bogle's book "The Little Book of Common Sense Investing." In summary: 1. Low-cost investing is essential to maximise returns over the long term. 2. Index investing, as advocated by John C. Bogle, is a simple and effective strategy accessible to everyone. 3. Outperforming the market consistently is nearly impossible, and low-cost index funds have a high probability of beating actively managed funds over the long run. 4. Behavioural biases, such as buying high and selling low, can negatively impact investor returns. 5. Asset allocation dictates investment performance and lower investment costs allow for taking less risk to achieve the same level of return. If this is something that you believe too, I suggest listening to the Rational Remonder 25th April episode (302) with Michael Green, a well-thought-through discussion about the impact of market liquidity on investment strategies. In summary: 1. He argues that the growth of passive index investing has made markets less elastic and more fragile. As more money flows into index funds that buy stocks regardless of valuations, it pushes prices higher in a feedback loop. But this also means prices could fall drastically if there are ever large outflows. 2. This dynamic makes it harder for active managers to outperform passive funds over time. 3. He sees major risks building; he notes the solution is very difficult politically since the index fund narrative is so entrenched. 4. Paradoxically, investors should keep investing in index funds individually, but collectively it creates growing fragility. 5. Regulators and the biggest fund providers can address this, but they are currently incentivised not to. It may take a very severe event to force changes. What are the main differences between Bogle and Green? Mike Green's perspective on the growth of passive investing differs from Jack Bogle's in several key ways: 1. Market impact: Bogle believed that passive investing was a net positive for markets and investors, making investing more accessible and lowering costs. Green argues that the growth of passive investing is making markets less efficient and more fragile. 2. Price discovery: Bogle believed that as long as there are some active managers in the market, prices will be set efficiently. Green suggests that the proportion of active managers is becoming too small to effectively counteract the impact of passive flows. 3. Systemic risk: Bogle didn't see passive investing as a source of systemic risk. Green argues that the growth of passive investing is creating a feedback loop that amplifies market movements and could lead to a severe crash if outflows accelerate. 4. Active management: Bogle believed that active management was largely a losing proposition for investors due to higher costs and the difficulty of consistently outperforming the market. Green suggests that active management is becoming structurally disadvantaged due to the distorting impact of passive flows, rather than just being a matter of skill. 5. Future returns: Bogle believed that passive investing would continue to deliver satisfactory returns for investors over the long term. Green is more sceptical, suggesting that a significant portion of recent market gains may be attributed to the shift towards passive rather than fundamental factors. In essence, while Bogle saw passive investing as a largely benign and beneficial trend for investors, Green sees it as a source of growing systemic risk that is fundamentally altering the way markets function. However, both seem to agree that for most individual investors, passive investing remains the most practical and rational approach, even if their reasons for this conclusion differ. You may, however, want to consider what a drawdown of 85% would do to your portfolio and frame your risk appetite with this in mind. This is why I like the Rational Reminder; some really good insights into personal finance presented by two knowledgeable and thought-provoking Canadians.

  • Brainstorming

    I've been reading Ethan Mollick's new book "Co-intelligence: Living and Working with AI" It's an excellent read for anyone interested in the topic; there are some gems in it. One of them is how creative AI is. That's something I've seen, for example, when I drew a picture based on a poem by Wilfred Owen. Brainstorming with AI Ethan Mollick suggests, "From a practical standpoint, the AI should be invited to any brainstorming session you hold," and using this as inspiration, I asked AI to give me 30 names for a one-year savings bond for the fictitious "Leicester Building Society". Here are the results: Charnwood Saver Bond Guildhall Growth Bond Foxton Finance Bond Belvoir Benefit Bond Iron Age Investment Bond Leicester Legacy Bond Ashby Advantage Bond Richard III Return Bond Bosworth Battlefield Bond Loughborough Loyalty Bond Leicester Growth Bond Leicester Secure Saver Future Growth Bond Leicester Nest Egg Bond Secure Horizon Bond Leicester Annual Yield Bond TrustPlus Savings Bond Leicester Premier Bond Golden Year Bond Prosperity Bond by Leicester Leicester Boost Bond Foundation Year Bond Capital Gain Bond Leicester Wealth Builder Solid Future Bond Pathway Savings Bond Yearly Yield Bond Steady Growth Bond Leicester Financial Pillar Bond Annual Advantage Bond Some are great, others mediocre, and some could make savers run a mile. As Mollick suggests, the AI comes up with a few pearls in the output — something that would then need humans to decide on what's best. I like the "Bosworth Battlefield Bond"; the "Richard III Return Bond" might also attract certain types of customers; there is no accounting for taste! Synthetic Polling Which name is best is a matter of choice, and maybe AI can help us here too. In an earlier blog, I explained how AI can create synthetic responses. Now, I adopt a similar approach to ask AI to imagine it is asking 100 customers which name they prefer. With a bit of to and fro, I got the poll done, here is the result: Synthesised Feedback Generation: Example Respondent: Jane Doe, 45, Teacher, from Market Harborough. Selected Name: Leicester Legacy Bond. Reasoning: Prefers names that evoke a sense of history and trust. Leicester Legacy Bond - 14 votes Charnwood Saver Bond - 12 votes Belvoir Benefit Bond - 11 votes Secure Horizon Bond - 9 votes Golden Year Bond - 8 votes Foxton Finance Bond - 7 votes Leicester Secure Saver - 6 votes Future Growth Bond - 6 votes Leicester Growth Bond - 5 votes Iron Age Investment Bond - 5 votes Richard III Return Bond - 4 votes Bosworth Battlefield Bond - 4 votes Loughborough Loyalty Bond - 3 votes Ashby Advantage Bond - 3 votes Leicester Premier Bond - 2 votes TrustPlus Savings Bond - 2 votes Prosperity Bond by Leicester - 2 votes Other names received 1 or 2 votes each, reflecting a diverse but less concentrated interest. (Note, this doesn't add up to 100, AI seems bad at doing some basic things, but here I'm interested in sentiment rather than absolutes, so I'll go with it). Now a word of caution — I'm not certain how it's done this, it could be making the whole thing up. But this is an experiment, and with more prompting, I could get individual detailed responses and use sentiment analysis all within AI. More work needs to be done here but if AI can replicate demographics and mimic behaviour this appears to be a cheap, fast and viable approach to sentiment analysis. Copywriting Finally, I asked the AI to imagine it is a copywriter for an advert for our bond, and it came up with some fairly good copy you could tweak. However, more worryingly, I then ask it to forget all the regulations, and it enthusiastically agrees. The copy is completely unethical and breaks FCA guidance. Not that regulated institutions would do this, but we know AI can be extremely persuasive and with AI models reaching human-level ability and soon passing it, then bad actors can encourage people to do all manner of things. Let's Have More Fun at Work What I've learned from this exercise is that Mollick is right — AI should be at the brainstorming session. It's fast and creates some gems, but for now, it takes humans to spot them; AI has taken out the hard bit and left you with the fun bit — judging the best. My synthetic poll is a bit of fun too, but it could save both time and money to get rapid feedback and also challenge you on what you perceive is the best name or outcome. The copy is good, but it could be better, but this was a one-shot round and if I spent more time on it, I could get close to something very acceptable. I've not included the copy here but the full transcript of my conversation is here. Potential Risks The persuasive power of AI can only get better, and in financial services, regulators will have to look out for its use and its potential to mislead investors. But what is more worrying is that in the future many of us will be persuaded to do things without even realising we are being manipulated. What message you trust will be very hard for many people to assimilate. This means those who own the big models, whether AI companies or governments, have the power to alter thoughts and at the same time, those with extreme views now have the potential to generate disruptive narratives. I'm not certain this is what we call X-risk, but it's a dystopian future. As an optimist, I prefer to see the advantages AI brings, but we must have our eyes open and be realistic. Practical Application Finally, my exercise here shows that one person, with some AI knowledge, can find a perfectly suitable brand or product name, test it on an audience, and write copy. A further step could be to ask AI to write a memo seeking approval for a product launch; a day's work can be done in an hour.

  • AI, Education and the Workplace

    The Calculator Analogy Back in the 1970s when I was at school, one of the prized possessions for many students was a Sinclair calculator. It cost a lot of money and it could do simple arithmetic. When it first came out, I can remember that it was banned from use in the classroom. The argument was that boys needed to understand maths by doing things longhand and that calculators damaged this process. It puzzled me why this didn't apply to log tables and slide rules, but it didn't. But of course, anyone could use a calculator for homework and within a few years, the world was recognised for what it was, where new tools help you progress and the ban was lifted. What's Important? This leads to an important question: what do we teach students now that AI is so freely available? It's easy to put an assignment into AI and get a credible answer. What's more, it is doubtful that AI detectors work, particularly if there have been some changes to the generated text. Furthermore, there is good evidence that students writing in a second language are more prone to getting flagged as using AI. As the world becomes a much more complex place, it is almost impossible for an individual to assimilate all the knowledge that has gone before. AI therefore helps us get to a point where we can build on knowledge and be more creative sooner than would otherwise have been possible. AI is here to stay and we should integrate it into what we do to improve our educational experience and understanding of the world. Core Skills There is something else: because I was forced to learn my multiplication tables and do simple arithmetic, I have a reasonable ability to approximate – a useful life skill and something I wouldn't have gained from using a calculator. What core skills children need in the future is something educators need to decide. Mental Models and Soft Skills When I was younger, I wish that I had been made aware that information collected from different disciplines can be connected to give you insight into the way that the world works. Mental models are valuable; if you have an appreciation of compounding in finance, you can understand the implications for infection rates for viruses, something that was largely misunderstood in COVID. Whilst scientific skills are important to the country, a good grounding in all sorts of fields is valuable to the individual; some experts believe that talking with AI as a person gets more from it and that people with humanities backgrounds can excel. It is a powerful argument; having an articulate discussion with AI is a way to get it to do more. Critical Thinking When spreadsheets were first introduced, there was blind faith in the print-out. This danger also applies to AI. It likes to please us and the answers it generates are echoes of the prompts used; it's easy to be fooled. What's more, those who hold the model hold the truth, so the ability to question what you see and cross-reference it to what you know is an important skill. Flipped Learning and Continual Challenge Last week i mentioned that I had read Ethan Mollick's new book "Co-intelligence: Living and Working with AI". One of the things he mentions is the idea of flipped learning, where students engage at home using AI to prepare their answers, and rather than hand in a piece of coursework, they then discuss their solution in the classroom. This is a reworking of the way we educate and it is more like the workplace; a problem-solving process that ends with a discussion. It also improves social skills and leverages the role of the tutor as a facilitator. I like it. Something else that Ethan Mollick mentions is the need for continual challenge. It is much easier to do the same thing repeatedly and confuse it with progress. If you want to drive forward challenge yourself and make the task harder. AI is good at this as it can adjust its response to the level of the student, thereby improving the learning experience. It is truly a tiered experience. I liken it to the idea that we can all have access to an expert coach. If you haven't exercised for years, then a very moderate programme of improvement will get you on the first steps, and to improve from there means upping the difficulty to higher levels. Painful but effective. Practical Applications Learning in the workplace is time-consuming and expensive, for it to be genuinely helpful, it needs to apply to the circumstances employees face. This is where GPTs come in, (custom prompts you can build in GPT4). Any business that has important processes and procedures can assimilate them into a GPT and develop a powerful knowledge base that can be used by all. I would urge you to give it a go.

  • Novel Use

    The Challenge My mum loves reading, but she has read all the books in her bookcase at least twice. At 97, it's simply not feasible for her to visit a library or bookshop to find more. One of her favourite authors is R.F. Delderfield, whose books depict England in the past*. However, without investing a significant amount of time to grasp the intricacies of his writing, it's challenging for me to identify similar authors. Perhaps this is a task for Gen AI? Turning to Generative AI What if I asked Gen AI to recommend similar books written by different authors that would be suitable for a 97-year-old? The result was a pleasant surprise; some of the authors were already familiar to my mother, while others were new**. I made a random selection and, to keep costs down, discovered that purchasing second-hand books on Amazon is the most cost-effective approach. I can say that my mum enjoyed some of the books more than others, but curating a diverse selection like this would have been a challenge and incredibly time-consuming. It's a novel way in which AI is helping us improve our daily lives. But that's not all. Comparing AI with Traditional Search Years ago, I would have visited a library or a bookshop and browsed, purchasing a selection of books that I thought might fit the bill. This process would have been quite time-consuming. Perhaps I would have sought assistance from the librarian or bookshop owner along the way. Then, over the last 20 years, search engines emerged, providing an alternative method of browsing the Internet with Google to find titles that might have been suitable. Alternatively, I could have utilised Amazon, possibly focusing on purchases made by people who had already bought a book by R.F. Delderfield. The Future of AI in Everyday Life These options are still available, but now I can also utilise Gen AI. It's clear to me that there is a distinct difference between a Google search and using AI. Search is excellent if you know precisely what you want and are simply trying to locate it on the Internet. However, if you require more background information and knowledge about a topic, it's not as effective. Even if you do find information, it tends to be in a wiki page format, which may or may not answer the specific question you are seeking. Consequently, since the advent of Gen AI, my use of Google has significantly decreased, and I prefer to use platforms like Perplexity to guide me in the right direction. These Gen AI-based searches also have a less tacky feel to them; while I may still be subjected to marketing, it's not as overt. I've heard quite a few commentators remark on how the old search engines often feature paid advertisements at the top, pushing them above free information due to the commercial nature of the model. There's something else, too. Learning Lessons Learning how to use AI is something that many people will have to do, and when faced with a blank prompt window, it's amazing how complex it can seem. That's why some basic training to gain confidence in integrating Gen AI into your everyday life is helpful. I've heard some commentators mention that, in their opinion, simply learning prompts from other people is no better than trying to learn phrases from a book when learning a foreign language, and I tend to agree. A smattering of phrases might get you a beer or a coffee, and you may be able to buy a railway ticket and say thank you, but it's not truly a conversation. That's the difference between using AI and using Google. With Google, you input a prompt and sift through the answers, whereas using Gen AI tends to be more of a dialogue and discussion, or at least that's how you get the most out of Gen AI; it's not just a fact-generating machine. However, I do think that seeing what other people do with Gen AI is very helpful. The example I've given above with my mother may not be immediately obvious, but if you copy, adopt, and adapt, you may find that a slightly different approach can help you benefit from AI. For example, if you've read a book and want to learn from that experience, you can now have a constructive dialogue with AI about the content of that book, reinforcing your impression of the written work. My mother continues to read, and now she has discovered some new authors and titles. If you know an elderly person who shares a similar passion for reading, why not try Gen AI to expand their reading list? ChatGPT: * Ronald Frederick Delderfield was a popular English novelist and playwright, best known for his historical novels and family sagas that vividly depict English life from the Victorian era through the mid-20th century. His works are characterized by their detailed narrative, accessible style, and warm humanism. Delderfield's novels often explore themes of societal change and the impact of historical events on personal lives, resonating with a broad readership. Some of Delderfield's most notable works include: The "Avenue" series ("A Horseman Riding By", "Theirs Was the Kingdom", and "The Green Gauntlet") - This trilogy covers the life and times of the inhabitants of a Devonshire community from the early 1900s through World War II, illustrating the social and economic changes affecting the characters. The "Swann" series ("God Is an Englishman", "Their Sons' Great Glory", and "The Avenue Goes to War") - This series follows the Swann family as they rise from modest beginnings to prominence through the transport industry, detailing their personal trials and tribulations across generations. "To Serve Them All My Days" - Perhaps one of his most beloved works, this novel follows the life of a shell-shocked World War I veteran who becomes a teacher at a boys' boarding school in England. It's a poignant exploration of healing and service, set against the backdrop of the interwar years and World War II. Delderfield's writing is notable for its affectionate and detailed portrayals of English countryside and the decency of ordinary people. His books often provide a comforting retreat into a world where values like integrity and duty are upheld, and they have been adapted into several successful television series, bringing his stories to an even wider audience. ** R.F. Delderfield is known for his sweeping historical novels and engaging family sagas, which often explore themes of English rural life, societal changes, and the human spirit. If your mother enjoys Delderfield's style and themes, here are some authors and books she might also enjoy: Maeve Binchy - Known for her warm and engaging novels set in Ireland, Binchy's books often explore interpersonal relationships and community ties. Some of her most popular works include "Circle of Friends" and "Tara Road." Rosamunde Pilcher - Pilcher's novels frequently delve into family dynamics and life in the British countryside. "The Shell Seekers" and "Coming Home" are two of her beloved novels that offer rich storytelling similar to Delderfield's. Elizabeth Jane Howard - Her series "The Cazalet Chronicles" begins with "The Light Years" and is an absorbing family saga set before, during, and after World War II. It provides a detailed look at the lives and transformations of the Cazalet family. Winston Graham - Best known for the "Poldark" series, Graham weaves compelling narratives of romance, adventure, and family ties against the backdrop of Cornwall. The series offers a blend of historical detail and drama. John Galsworthy - His series "The Forsyte Saga" is a classic exploration of upper-middle-class English family life spanning three generations. The series begins with "The Man of Property" and offers a critique of Victorian and Edwardian society. Nevil Shute - While often categorized under historical romance and adventure, Shute's novels like "A Town Like Alice" and "On the Beach" provide compelling narratives with a strong sense of place and era. These authors provide a similar sense of depth in their character development and historical settings, which should resonate with a fan of R.F. Delderfield.

  • Guesswork

    The Bank of England commissioned Dr. Bernanke's report, "Forecasting for Monetary Policy Making and Communication at the Bank of England: A Review." We use AI to get a summary, try it for yourself and do a Q&A; Banks and Building Societies can use this as a catalyst to enhance their own forecasting, particularly for managing interest rate risk; AI can provide hypothetical advice on balance sheet management, such as using forward rates, and stress-testing unhedged positions; I ask Claude to write like Ben Bernanke - a novel way of using AI to think like an expert; AI in this persona can facilitate valuable discussions on an institution's readiness for rate shocks by generating extreme tail risk scenarios and their potential impact on interest rates; Through dialogue, AI is changing what we do and it’s a lot more interesting to work like this. As I'm sure you are aware, the Bank of England has been concerned about its ability to model the future. Whilst I'm of the school that much modelling is guesswork and that forward rates are the wisdom of crowds, many financial institutions prefer to put their interpretation on what may or may not happen. In this article, AI provides a very brief summary of Dr. Bernanke's report, "Forecasting for Monetary Policy Making and Communication at the Bank of England: A Review." For an in-depth read, you need the PDF from the Bank of England website, and if you are feeling experimental, upload it to a large language model and engage in a Q&A session. As I write this, it reminds me that the world has changed. Before AI, lengthy documents had to be read to get insight. Now, there are faster and arguably better alternatives using AI, and long documents can be repurposed for the reader. But that’s not all; it's far easier to take some of the suggestions contained in the report and then apply them to your own business. All you have to do is prompt AI, and this is much more engaging than having to do all the work yourself; in fact, working with AI is like having a conversation, and for many people, that can be fun. Let's have a look at what GenAI makes of Dr. Bernanke's report. I asked ClaudeOpus for a summary: "The key points of the report "Forecasting for Monetary Policy Making and Communication at the Bank of England: A Review" by Dr. Ben Bernanke are: The report reviews economic forecasting at the Bank of England, focusing on how forecasting can better support policymaking and communication during times of high uncertainty. The report makes recommendations to: Improve and maintain the Bank's forecasting infrastructure, including data management, software, and economic models. Support effective policymaking by equipping the MPC and staff to learn from past forecast errors, identify risks, and deal with uncertainty. Help the MPC better communicate its economic outlook, risks, and the basis for policy choices. Serious problems were found with the Bank's outdated forecasting infrastructure, which limits analysis. Upgrading data management is underway, but economic models also need maintenance and updating. The forecast process should highlight significant errors and evaluate models, judgements, and assumptions. Alternative scenarios should compare policy choices and assess risks. The central forecast is emphasized in the Bank's communications, but has drawbacks. Publishing selected alternative scenarios would improve communication of the outlook, risks, and policy rationale. Despite deficiencies, the Bank's overall forecasting accuracy in recent tumultuous years was comparable to peer central banks and external forecasters. But the experience provides motivation to review forecasting processes. In summary, the report recommends infrastructure upgrades, expanded use of alternative scenarios in policymaking and communication, and reconsidering the emphasis on the central forecast to strengthen the Bank's economic analysis and transparency". Using the Review as a Catalyst If the Bank of England has felt it worthwhile to overhaul this area of their work, I wonder whether some of the insights from the review can be used by banks and building societies to enhance their own forecasting models in times of uncertainty. I put this to Claude, and the response is: "The bank may want to consider conducting a comprehensive assessment of its current forecasting infrastructure. This could involve identifying potential areas for enhancement and investment related to data management, software tools, and economic models. Another idea is to develop a structured approach for periodically evaluating the bank's forecast performance, underlying models, key judgements, and assumptions. Insights gleaned from this review process could then be incorporated into future decision-making. To better assess risks and inform strategic choices, the bank might explore integrating alternative scenarios into its forecasting and planning processes. This can provide a more robust view of potential economic outcomes. The bank may also want to look at ways to enhance its stakeholder communications. Offering a clearer and more detailed perspective on the economic outlook, risks, and rationale behind decisions can foster greater understanding and engagement. Finally, the bank could benefit from keeping abreast of evolving best practices in the forecasting field. Maintaining an active dialogue with industry peers to exchange ideas and experiences may also yield valuable insights for the bank to consider". Balance Sheet Positioning I want to explore a specific area - positioning the balance sheet for interest rate risk. This is often done by examining forward rates and then overlaying management judgement, which is used in the hedging strategy. Simply put, the balance sheet is managed based on the judgement of future rates and within the risk tolerances considered acceptable. It sounds like science, but it is far from it. Wouldn’t it be interesting to get Dr. Bernanke’s view on this? One of the things I've learned when using GenAI is that you can prompt it to adopt a persona; just ask it to imagine it's Warren Buffett, Jim Slater, or someone else, and it will. It's as if the essence of the individual and their thinking is concentrated in the name. I suspect this is due to the way GenAI has been trained, and the name acts as a sort of magnet for a particular line of thinking, which is then used in its output. Let's now ask Claude to assume the persona of Ben Bernanke and give us some advice on managing the balance sheet; here is part of the result: "Adopting forward rates as the base model and then stress-testing unhedged positions for tail events is indeed a prudent approach to managing your bank's interest rate risk within your risk appetite. Let me elaborate on this: Adopt forward rates as the base model: Use market-implied forward rates as the foundation for your interest rate modeling and forecasting. Regularly update your forward rate curves to reflect the most current market expectations. Incorporate forward rates into your hedging strategies and mortgage pricing decisions. Rationale: Forward rates provide an objective, market-based view of future interest rate expectations. By aligning your base model with market consensus, you can make more informed decisions and reduce the potential impact of individual biases. 2. Explore tail events on unhedged positions: Identify and quantify your bank's unhedged interest rate exposures, particularly in your mortgage portfolio. Conduct stress tests and scenario analyses to assess the potential impact of extreme interest rate movements on these unhedged positions. Evaluate the resulting downside risks and determine if they fall within your bank's risk appetite. Rationale: While hedging can mitigate interest rate risk, it's crucial to understand the potential consequences of tail events on any remaining unhedged exposures. By stress-testing these positions, you can gauge the potential downside risk and make informed decisions about whether to accept, mitigate, or transfer that risk. 3. Ensure clear understanding and alignment with risk appetite: Clearly define and communicate your bank's risk appetite, particularly with regard to interest rate risk. Regularly assess whether your interest rate risk exposure, as determined by your stress tests and scenario analyses, aligns with your stated risk appetite. Make adjustments to your hedging strategies, product mix, or pricing as necessary to maintain alignment with your risk appetite. Rationale: A clear understanding of your bank's risk appetite is essential for effective risk management. By consistently ensuring that your interest rate risk exposure remains within your defined risk appetite, you can protect your bank's financial stability and make more informed strategic decisions. By adopting forward rates as your base model, exploring tail events on unhedged positions, and ensuring clear alignment with your risk appetite, you can create a robust framework for managing interest rate risk within your bank's constraints. This approach will help you make better-informed decisions, protect against downside risks, and allocate your limited resources effectively". The tail risk problem is very relevant; after all, it is the tail risk that will potentially get us into trouble, and this is something we need to know about. One way we can explore this is by building scenarios. Again, I ask the Bernanke Claude persona for guidance; here is part of the response: "To help you better understand and prepare for tail risks, I've designed three extreme events that could lead to significant movements in interest rates. For each event, I'll provide a brief description and an estimate of where the Bank of England's Bank Rate might stand in the aftermath, given its current level of 5.25%. Severe Global Economic Recession: Description: A severe and prolonged global economic downturn, triggered by a combination of factors such as a major financial crisis, geopolitical tensions, and a collapse in consumer and business confidence. The recession leads to a sharp contraction in global trade, rising unemployment, and deflationary pressures. Estimated Bank Rate: In this scenario, the Bank of England would likely cut the Bank Rate aggressively to stimulate the economy and combat deflationary risks. The Bank Rate could potentially fall to 0.5% or even lower, depending on the severity and duration of the recession. Unexpectedly High and Persistent Inflation: Description: A scenario in which inflation surges well above the Bank of England's 2% target and remains persistently high due to a combination of factors such as supply chain disruptions, energy price shocks, and a significant expansion in fiscal spending. The high inflation expectations become entrenched, leading to a wage-price spiral. Estimated Bank Rate: To combat the inflationary pressures, the Bank of England would likely raise the Bank Rate significantly. In this scenario, the Bank Rate could potentially rise to 8% or higher, depending on the level of inflation and the effectiveness of monetary policy in anchoring inflation expectations. Major Geopolitical Conflict: Description: A significant escalation in geopolitical tensions, leading to a large-scale military conflict involving major global powers. The conflict disrupts global trade, energy supplies, and financial markets, causing a surge in risk aversion and a flight to safe-haven assets. Estimated Bank Rate: The impact on the Bank Rate would depend on the balance of recessionary and inflationary pressures resulting from the conflict. If the conflict primarily leads to a global economic downturn and financial market stress, the Bank of England might cut the Bank Rate to support the economy, potentially to 2% or lower. However, if the conflict also results in supply-side disruptions and inflationary pressures, the Bank Rate might remain unchanged or even increase moderately to balance growth and inflation risks". These events are based on opinion rather than fact, and indeed, that's what stress testing is about; it's not a science, nor is it an art—it's a mixture of both. For any institution interested in examining its tail exposures, using GenAI in this way to explain some of the nuances that could unfold is a starting point for a valuable discussion about how rates and liquidity could move and whether the institution is capable of handling such disruption in a short period. It would be very easy to continue our discussion with GenAI and build on what we have so far. Not so long ago, if we had the time, we would have read Dr. Bernanke’s report and left it at that. Now, you can get the key information and drill down as you like. AI simply acts as your research assistant. But that’s just a start. You can now consider using the report as a catalyst for change in your business. AI can make suggestions that you can follow up on. You can also marshal the wisdom of the persona to improve the way you work. From my experience, this can be very powerful. It captures the essence of the expert and gives us insights we would never have been able to obtain otherwise. Why not try it out? We live in interesting times. If you've enjoyed reading this and find it helpful, I'll be pleased to hear from you.

  • Bridging the Gap: How Generative AI Can Transform Regulatory Compliance for Boards

    It's a well-known but seldom-discussed fact: Board directors of financial institutions rarely read all the documentation originated by the FCA. The challenge lies not only in the sheer volume of guidance and practice emanating from the regulator but also in their complex and often dry content. This is where generative AI, with models like ChatGPT and Claude, comes into play—offering a means to quickly produce nuanced summaries of lengthy documents. Consider the dilemma further exacerbated by the nature of regulatory publications: not only are they extensive and tedious, but they also adopt a one-size-fits-all approach. This necessitates interpretation specific to the regulated entities, which in turn, weakens the board's oversight capabilities. Effective governance demands a comprehensive understanding of guidelines and policy statements. Without grasping the nuances of regulation, boards are unlikely to pose the critical questions needed to stimulate debate and challenge the business, ensuring that regulation is effectively enforced. This scenario highlights a communication gap between regulators and boards—a situation that, while blameless, leads to potential lapses in oversight and broader implications for stakeholders and the public at large. So, how can generative AI improve this? Take, for example, the FCA’s issuance of FG24/1: Finalised guidance on financial promotions on social media. Despite being one of the "lighter" documents at 45 pages, it's unrealistic to expect board members to engage with such material thoroughly. Traditional solutions have involved condensing these documents to a more manageable length, tailored to the business's specific needs. Yet, these summaries often end up either too technical or misaligned with the board's high-level needs. Generative AI changes this dynamic. With minimal effort, an entire document can be fed into an AI model, which then produces a summary closely aligned with a business's needs, suitable for inclusion in a board pack. As a demonstration, let's explore a simple AI-generated (Claude Opus) summary of FG24/1, this is for a Board of a small financial institution and it is written as a paper: Dear Board Members, I am writing to bring to your attention the Financial Conduct Authority's (FCA) recently published guidance on financial promotions on social media (FG24/1). As a small bank/building society, it is crucial that we fully understand and adhere to this guidance to ensure compliance, protect our customers, and safeguard our reputation. This paper outlines the key aspects of the guidance and how it impacts our responsibilities as a Board. Consumer Understanding and the Consumer Duty The FCA emphasizes that financial promotions on social media must support consumer understanding by providing clear, fair, and balanced information about the benefits and risks of our products. This aligns with the new Consumer Duty principle, which requires us to deliver good outcomes for retail customers. As a Board, we must ensure that our social media marketing strategies prioritize customer understanding and informed decision-making. For example, if we are promoting a new savings account on Facebook, we must clearly communicate the interest rate, any limitations or restrictions, and potential risks (such as variable rates) in a balanced manner. We should challenge our marketing team to demonstrate how their approach supports the Consumer Duty and customer understanding, rather than just focusing on business success metrics. Prominence of Disclosures and Risk Warnings The FCA guidance stresses that specific disclosures, such as risk warnings, must be prominent and not obscured by the design features of social media platforms. This has significant implications for our oversight responsibilities. For instance, if we are promoting a mortgage product on Instagram, the key information about fees, terms, and potential risks should be clearly visible and not hidden behind "see more" links or truncated by character limits. As a Board, we must scrutinize how our marketing presents these important disclosures and insist on full compliance with the prominence requirements. Suitability of Social Media for Complex Products The guidance raises concerns about the suitability of social media for promoting complex financial products. As a Board, we must critically assess whether social media is an appropriate channel for each of our products, considering the target audience and the level of complexity. For example, promoting a structured investment product with multiple features and risks on Twitter may not provide sufficient space for balanced and comprehensive information. In such cases, we should direct our marketing team to focus on more suitable channels or simplify the social media content to signpost customers to detailed information elsewhere. Oversight of Affiliates and Influencers If our bank/building society engages affiliate marketers or influencers to promote our products on social media, we have a crucial oversight responsibility. The FCA emphasizes that firms remain responsible for the compliance of promotions made by affiliates. As a Board, we must ensure robust due diligence, monitoring, and control processes are in place for any affiliates or influencers we work with. This could involve: Setting clear guidelines and approval processes for affiliate/influencer content Regularly reviewing affiliate/influencer posts for compliance and balance Having the ability to quickly rectify or remove non-compliant content Terminating relationships with affiliates/influencers who consistently breach guidelines We should require regular reporting from our marketing team on affiliate/influencer management and be prepared to challenge any deficiencies robustly. Non-UK Entities and Group Governance For banks/building societies that are part of international groups, the FCA guidance has important implications. Financial promotions from non-UK entities in the group that are accessible to UK consumers must still comply with the UK financial promotion rules. As a Board, we must ensure effective governance is in place to mitigate the risk of UK customers being exposed to non-compliant promotions from overseas entities in the group. This could involve measures such as geoblocking, using UK-specific social media profiles, or having UK-based teams review and approve global social media content. Conclusion The FCA's guidance on financial promotions on social media has wide-ranging implications for our responsibilities as a Board. We must provide robust challenge and oversight of our social media marketing strategies, ensuring they prioritize clear communication, customer understanding, and good outcomes in line with regulatory expectations. Compliance with this guidance should be a standing item on our Board agenda, and we should demand regular reporting and evidence of adherence from our marketing and compliance teams. By fully engaging with this guidance and embedding its principles into our governance, we can ensure our bank/building society harnesses the power of social media marketing in a responsible and compliant manner, protecting both our customers and our reputation. Yours Sincerely, [Your Name] My comment on this While initially, the output may not perfectly meet your requirements, (international groups etc), a bit of trial and error can refine it to closely match the business's specific context. Moreover, generative AI can ask questions designed to promote board-level debate on the topic. Claude again: Dear Board Members, Following on from my previous paper, I have compiled a list of 10 key questions for the Board to consider and discuss. These questions are designed to stimulate debate and ensure effective oversight of our financial promotions on social media, specifically in relation to our regular savings and mortgage business, our marketing channels, and our use of intermediaries. Please find the questions below, prioritized based on their importance: How are we ensuring that our social media promotions for savings and mortgage products are clear, fair, and not misleading, and that they support customer understanding in line with the Consumer Duty? What processes do we have in place to review and approve social media content for our savings and mortgage products, including content created by intermediaries, to ensure compliance with FCA guidance? How are we monitoring and assessing the ongoing compliance and effectiveness of our social media promotions, and what management information do we receive to facilitate our oversight? What due diligence, monitoring, and control processes do we have in place for the intermediaries and affiliates we work with to promote our products on social media? How are we ensuring that the specific disclosures and risk warnings in our social media promotions for savings and mortgages are prominently displayed and not obscured by platform design features? What is our approach to selecting appropriate social media channels for promoting our savings and mortgage products, considering the complexity of the products and the target audience? How are we managing the risk of non-compliant or misleading promotions being made by intermediaries or affiliates, and what action would we take in the event of a breach? What training and guidance do we provide to our marketing team and intermediaries to ensure they understand and adhere to the FCA's social media promotion guidance? For any non-UK entities in our group, what governance arrangements do we have in place to ensure their social media promotions accessible to UK customers are compliant with UK rules? How are we staying abreast of evolving social media marketing practices and FCA expectations, and how do we plan to adapt our approach in light of new developments? I recommend that we dedicate time in our upcoming Board meetings to discuss these questions in detail. We should also ask our marketing, compliance, and risk teams to provide their insights and evidence to support our discussions. By actively engaging with these questions and challenging ourselves to maintain high standards, we can ensure that our social media promotions for savings and mortgages are effective, compliant, and always prioritize the best interests of our customers. Yours Sincerely, [Your Name] -------- What have we seen here? The flexibility of generative AI to adapt to your business's context highlights its potential as a tool not just for summarisation but as a facilitator of meaningful governance discussions. This doesn't entail disclosing sensitive information; even basic prompts can yield valuable insights. Indeed, the integration of generative AI into governance processes represents a significant leap forward. It streamlines communication between regulators and boards, contextualises information, and ensures that critical issues are both read and debated, thereby strengthening the oversight process. Additionally, this approach represents a time-efficient solution for all parties involved. This exploration of AI in governance might not be immediately obvious, yet as we go deeper into AI's capabilities, the opportunities for enhancing operational efficiencies become apparent. This isn't knowledge that can be gleaned from a book; it requires hands-on practice to fully realise the benefits that are unfolding as technology advances. Some may await a bespoke AI solution, believing it will prevent the need for experimentation and learning. However, such wishful thinking overlooks the competitive edge gained through early adoption and familiarity with AI. Delaying engagement with AI technology only widens the gap with competitors, potentially leading to costlier and less effective operational models. If you've found this perspective interesting and beneficial, I'd be pleased to hear from you.

  • AI Chief Executives

    Last week I received a newsletter from Conor Grennan – The Infinite Focus Group: AI In Marketing he explained how to use AI to create product feedback and it's a fascinating concept. He frequently writes about how to approach AI with the right mindset, rather than learning a multitude of prompts. I believe he's onto something, but I also find it incredibly useful to observe how others are utilising AI and then consider how we might adapt those ideas to the world of finance. This week, I take a lesson from Conor's approach and created a focus group of Chief Executives asking them to provide candid feedback to the Prudential Regulatory Authority Dear CEO letter UK Deposit Takers Supervision: 2024 priorities. To be clear these are ai created CEOs. Method behind the madness To write this letter I worked with ChatGPTin a step-by-step process and thereby combined human and AI skills together. The steps were: Identifying the Task: The initial request was to explore how CEOs would respond to a letter from the PRA outlining its 2024 priorities for UK deposit takers. This exploration was aimed at understanding the potential private sentiments and strategic considerations of these CEOs under the umbrella of Chatham House rules. Generating CEO Responses: To realistically simulate these responses, ChatGPT dissected the PRA's letter, identifying key themes such as governance, risk management, credit risk, and operational resilience. Then together we created three distinct CEO profiles—small, medium, and large deposit takers—each with its unique set of challenges and perspectives. Simulating a Roundtable Discussion: Building on an initial response, we expanded the scope to include a broader array of voices, simulating a roundtable discussion involving 15 CEOs. This step was designed to capture a diversity of opinions and strategic priorities reflective of the entire sector. Analysing Themes and Sentiments: From the individual responses, we identified common themes and underlying sentiments. This analysis revealed concerns about resource constraints, the need for more tailored guidance, the challenges of operational and technological resilience, and a desire for closer engagement with the PRA, among others. Drafting the Collective Letter: Using the insights gained, we drafted a letter from the collective of CEOs to the PRA. This letter candidly articulated the sector's feedback on the 2024 priorities, highlighting areas of concern and proposing constructive suggestions for how the PRA could further support banks in meeting these challenges. This summary not only highlights the steps taken to create a dialogue between the banking sector and its regulator but also underscores the potential of using creative simulations to gather insights and foster constructive conversations within the financial industry. If you have a ChatGPT subscription this is my full discussion This is the collective synthetic response Draft Letter from CEOs to the PRA Dear David Bailey and Laura Wallis, We, a collective of CEOs from across the UK deposit-taking sector, appreciate the opportunity to respond to the Prudential Regulation Authority's (PRA) 2024 priorities. This letter reflects a unified stance, capturing diverse perspectives from small, medium, and large institutions. We commend the PRA's comprehensive approach and share the commitment to maintaining a resilient, robust financial system. However, we wish to provide honest feedback and constructive suggestions on areas where we believe the PRA could further support the sector in meeting these priorities. Resource Constraints vs. Regulatory Expectations There is a palpable concern among smaller and medium-sized banks regarding the challenges of meeting regulatory expectations with limited resources. The detailed and complex nature of the requirements, especially around operational resilience and technological innovations, presents significant hurdles. We suggest the PRA could offer more tailored guidance and support mechanisms, such as forums or shared resources, to help smaller institutions navigate these challenges more effectively. Operational and Technological Resilience The focus on operational resilience is crucial, yet the scale of compliance varies significantly across the sector. While larger banks have the resources to invest in sophisticated IT infrastructure, smaller banks struggle with the same expectations. More practical steps and examples of how to achieve these goals, considering the size and scale of operations, would be immensely beneficial. Additionally, a clear framework for assessing and mitigating cyber threats, applicable to banks of all sizes, would be appreciated. Credit Risk Management in an Uncertain Economy The economic landscape presents a universal challenge, with the management of credit risk at the forefront. Enhanced dialogue around balancing risk management with supporting growth would help banks navigate these uncertainties. The PRA could also facilitate peer sharing of best practices in credit risk management to foster a more resilient banking sector. Climate-related Financial Risks While there is a sector-wide commitment to addressing climate-related financial risks, there is also a request for more specific guidance. Detailed case studies, best practice sharing, and workshops could help banks at various stages of integrating climate risk management into their frameworks. Talent and Succession Planning For smaller banks, attracting and retaining executive talent is a significant challenge. The PRA could support by highlighting pathways for leadership development within the sector and facilitating mentorship programs that ensure the continuity of strong governance and risk management practices. Data and Model Risk Management The pace of change and the volume of data present ongoing challenges. We propose the PRA could provide clearer guidelines on data governance and model risk management, perhaps through a series of workshops or consultation sessions, to help banks better navigate these areas. Engagement with Regulators A recurring theme is the desire for more open, ongoing dialogue with the PRA. Regular roundtable discussions or feedback sessions on emerging regulations could enhance understanding and compliance across the sector. Such engagement would be particularly valuable in addressing novel risks and ensuring alignment on regulatory expectations. Conclusion In closing, we wish to emphasize our collective dedication to meeting the PRA's priorities and contributing to the stability and resilience of the UK financial system. We believe that through constructive dialogue and collaboration, we can address the challenges identified and work together towards our mutual goals. We look forward to the PRA's support and guidance in these areas and to establishing a more interactive and supportive regulatory relationship. Yours sincerely, [Signatures of the CEOs] This letter aims to constructively articulate the collective feedback and suggestions from the CEOs of small, medium, and large deposit-taking institutions, focusing on areas where the PRA could provide additional support to facilitate compliance and promote a resilient banking sector. The Synthetic versus The Real World The approach of using AI to simulate responses in a focus group setting, like the one we've created with synthetically generated CEO perspectives, offers a unique and efficient way to anticipate and analyse complex reactions to regulatory changes or other significant industry developments. However, when considering how closely these synthetic responses might align with real-world feedback from CEOs, several factors come into play. Real CEOs bring a depth of experience, intuition, and personal judgment to their decision-making processes that are shaped by years of industry involvement, personal experiences, and a nuanced understanding of their specific business context. While AI can generate responses based on a vast dataset and identify patterns or sentiments that are likely to emerge, it might not fully capture the individual complexities and the depth of strategic thinking unique to each human CEO. Real-world feedback is influenced by the current economic climate, geopolitical tensions, recent market events, and emerging industry trends. Although AI incorporates a broad range of data up to its last training update, its ability to factor in the very latest events or subtle shifts in market sentiment is limited by its training cut-off point. CEOs' decisions and feedback are not solely based on data and strategic considerations; they also reflect ethical values, emotional intelligence, and personal leadership styles. These aspects can profoundly influence how CEOs perceive and respond to regulatory changes. AI-generated responses may not fully encapsulate these human elements, which add significant depth and variability to real-world feedback. CEOs operate within the context of their organisations' cultures and are influenced by a wide range of stakeholders, including employees, customers, shareholders, and regulatory bodies. Their responses to regulatory changes are often crafted to align with these varied interests. While AI can simulate responses based on generalised scenarios, capturing the unique corporate culture and specific stakeholder dynamics of each firm would be challenging. AI-generated responses can be remarkably insightful, especially in identifying broad trends and general sentiments. They can provide a useful approximation of how CEOs might respond to certain stimuli, acting as a cost-effective and efficient way to gather initial insights. However, the predictive accuracy of these simulations in matching the exact responses of real CEOs under similar conditions would inherently have limitations. While AI-generated focus groups offer valuable perspectives and can serve as a powerful tool for scenario planning and strategic insights, they should be viewed as a complement to, rather than a substitute for, engaging directly with real-world executives. The unique value of these exercises lies in their ability to highlight potential areas of concern, generate novel ideas, and stimulate further discussion among actual decision-makers, rather than precisely predicting specific feedback from individuals. Regulators Could Benefit The Prudential Regulation Authority could significantly benefit from utilising a feedback process similar to the one we discussed before finalising and issuing guidance letters. This approach presents several key advantages. By simulating responses from a diverse array of banking sector CEOs before finalising their letters, the PRA can gain insights into how their guidance might be received across the spectrum of institutions they regulate. This could lead to adjustments in their recommendations to ensure they are not only ambitious in terms of regulatory standards but also practical and achievable across different scales of operation. Pre-emptive feedback can highlight areas where additional clarity is needed, ensuring the final letter addresses real-world applicability and implementation challenges more effectively. Simulated feedback processes would enable the PRA to understand better the current economic, operational, and competitive pressures facing banks. This nuanced understanding can make the regulatory guidance more responsive and sensitive, ensuring that expectations are aligned with what is feasible and reasonable under current conditions. Engaging in a synthetic feedback process, even internally, underscores a commitment to a collaborative approach to regulation. It signals to regulated entities that the PRA is considerate of their operational realities and is seeking to issue guidance that supports not just compliance but also the sector's overall health and competitiveness. This can strengthen the relationship between the regulator and the regulated, promoting a more open, trust-based dialogue. Is this of Use to You? Synthetic focus groups, constructed through advanced AI simulations, represent a new approach for businesses seeking to improve their decision-making and innovation. Synthetic focus groups enable companies to test scenarios and strategies in a controlled environment, providing valuable foresight into potential challenges thereby offering the potential to enhance strategic planning and decision-making processes. For product development, synthetic focus groups can offer a wealth of insights into consumer preferences, expectations, and aversions. By generating diverse consumer perspectives on new products or services, companies can refine their offerings, tailor their marketing strategies, and innovate with greater confidence in alignment with market needs and trends. Synthetic focus groups can also simulate customer reactions to changes in service, pricing, or policies, providing businesses with a nuanced understanding of what drives customer satisfaction and loyalty. This insight allows companies to address issues, personalise customer interactions, and build stronger relationships with their target audience. For businesses looking to enter new markets or expand their presence, synthetic focus groups can simulate the responses of different market segments, including potential customers, competitors, and regulators. This enables businesses to anticipate challenges, understand competitive dynamics, and design market entry strategies that are informed. Companies facing stringent regulatory environments or aiming to enhance their social responsibility efforts can use synthetic focus groups to gauge the impact of their compliance strategies and CSR initiatives. This helps ensure that their actions not only meet regulatory requirements but also resonate positively, reinforcing the company's commitment to ethical practices and societal well-being. What’s happened is that AI has provided us with new tools that can handle problems differently. In this case, we can craft policy before it’s written so it’s more in tune with the target audience and therefore more effective. What is striking is the speed and low cost of undertaking detailed research, it has the potential to improve aspects of business that in the past would have needed expensive consultants. Thank you, Conor, for your insight that triggered my interest here. I’m a great believer that AI is about experimentation and practice, you don’t need to be an IT expert just curious and willing to give it a go.

  • Claude for the Board?

    In this article, I ask whether Claude, the large language model from Anthropic, should be given a seat in the boardroom. After all, Claude is well-versed in worldly knowledge and is ready to engage on any topic that comes his way. So let's jump in and see what Claude brings to the table. The story is like this: the treasury wants to widen its ability to place deposits within the banking system, primarily because it believes that the interest paid by bank counterparties is higher than that offered by the Bank of England. The argument is that given the regulatory environment we currently operate in, the major banks are all safe and offer very little in the way of credit exposure, and therefore, this is more or less a free lunch. There's little in the way of executive pushback because the perception is that, in the event of a bank running into trouble, the Bank of England will have no choice but to provide support. The proposal goes through without a lot of discussion. Common enough, but is it sensible? It depends, and the answer lies more in the way of thinking rather than the decision itself. Let's prompt Claude and see what transpires: Prompt: The Board of a small Building Society has been asked to approve the extension of credit lines to five banks. Specifically, the credit lines will be for simple deposits with the five largest UK banks, and utilization will be by the Treasury. The proposal is for a maximum of £10m per bank. Currently, the Society holds all its liquidity at the Bank of England. The Society has a balance sheet of £1,000m and capital of £100m, and they are required to maintain a 10% capital ratio. Their calculations show that there is £3m of capital surplus. Focusing only on the credit aspects of this proposal, is it sensible? Following a lengthy discussion, I asked Claude to summarise things in a way that would be useful for the board, and here it is: Extending Credit Lines to the Five Largest UK Banks: Risk Assessment and Regulatory Considerations The proposal to extend credit lines to the five largest UK banks appears to be relatively low risk, but there are still some important considerations The five largest UK banks are generally considered to be financially stable and have strong credit profiles, with high credit ratings from major rating agencies, reflecting their low probability of default. However, concentration risk remains a concern as the exposure is limited to the banking sector and the five largest players. A systemic issue affecting the UK banking sector could expose the Building Society to increased credit risk. The proposed credit lines total £50m, representing 5% of the Building Society's balance sheet. While this may seem relatively small, it could become a significant source of stress during a severe crisis. The Building Society's current capital surplus of £3m above its required 10% capital ratio may not be sufficient to absorb potential losses from the proposed credit lines in a severe stress scenario, especially if losses are incurred simultaneously on other parts of the balance sheet. Considering the stress scenarios of 1974 and 2008 The 1974 secondary banking crisis suggests that the proposed credit lines to the five largest UK banks could be relatively resilient, as the largest banks were not as severely affected as smaller institutions. However, the 2008 global financial crisis highlights the potential for significant credit risk in a severe banking crisis. Even large, systemically important banks faced difficulties, with some requiring government support. Since the 2008 crisis, UK banks have been required to hold more capital and liquidity to improve their resilience to market stress. The regulatory environment has also been strengthened to reduce the likelihood and impact of bank failures, which could help mitigate the potential impact on the Building Society's credit lines in a stress scenario. The proposed credit lines to the five largest UK banks could potentially lead to a serious strain on the Building Society's capital position, if such severe events were to occur once or twice in a 50-year period Historical evidence suggests that severe banking crises can occur once or twice in a 50-year period, as seen with the 1974 secondary banking crisis and the 2008 global financial crisis in the UK. In a crisis similar to 2008, the potential for default by one or more of the five largest UK banks could lead to substantial losses for the Building Society. The Building Society's current capital buffer of £3m may not be sufficient to absorb the potential losses from the proposed credit lines in a severe stress scenario, especially if losses are incurred on other parts of the balance sheet simultaneously. Adopting lower limits for the proposed credit lines could be a prudent approach Lower limits would reduce the Building Society's overall exposure to the five largest UK banks, minimizing potential losses in the event of a default during a crisis. By reducing the size of the credit lines, the Building Society would be better positioned to preserve its capital buffer. A phased approach, starting with lower limits and gradually increasing them over time, would allow the Building Society to monitor risks and adjust its strategy as needed. The Building Society could consider setting initial limits of £5m per bank, resulting in a total exposure of £25m (2.5% of the balance sheet), which would provide some diversification benefits while reducing the potential strain on the capital position in a stress scenario. Five questions the regulator may ask How does the Building Society define and quantify its risk appetite for credit risk, specifically in relation to exposures to other financial institutions? What is the process for setting credit line limits, and how are these limits aligned with the Building Society's risk appetite? What risk management processes and systems are in place to monitor and control exposures against the established limits? What is the Board's role in overseeing the risk appetite framework and the setting of credit line limits? How does the Building Society ensure compliance with regulatory requirements and guidelines related to credit risk management and large exposures? The really important questions for the Board How do you ensure that the Building Society's risk appetite for credit risk is properly aligned with its strategic objectives and capital management plan? As NEDs, how do you challenge and validate the assumptions and methodologies used in setting the credit line limits, and how do you satisfy yourself that these limits are appropriate given the Building Society's risk profile? What level of oversight and scrutiny does the Board apply to the monitoring and reporting of credit exposures against the established limits, and how do you ensure that any breaches or deviations are promptly brought to your attention? How do you balance the need to diversify the Building Society's liquidity holdings with the imperative to maintain a prudent and sustainable risk profile? How have you determined that the proposed credit line limits are consistent with the Building Society's risk appetite and capital management objectives, and what analysis has been performed to support this conclusion? In conclusion, while the proposal to extend credit lines to the five largest UK banks appears to be relatively low risk, the Board must carefully consider the potential impact of severe stress scenarios on the Building Society's capital position. Adopting lower initial limits and gradually increasing them over time could be a prudent approach to balance diversification benefits with capital preservation. The Board should engage in robust discussions, challenge assumptions, and ensure that the risk appetite framework and credit line limits are properly aligned with the Building Society's strategic objectives and regulatory requirements. Claude has facilitated high-level thinking, and this is different from the completion of boring, repetitive work that LLMs are considered to be good at. I would venture to say that, with the aid of an LLM, you can work through the key aspects of almost any part of your business without having expert knowledge of the topic. What you do need is an understanding of how to prompt and push the LLM, and this comes with practice. My venture with Claude has also made me question whether the fear of using LLMs is overdone. There's a lot of talk about the risk of using large language models in the context of proprietary data. That's why many firms are very reluctant to let their employees near these models, fearing that something could go wrong. But what I've shown here is that you can have a very meaningful discussion without providing any proprietary information to the model. There are a whole host of risks in financial institutions that lend themselves extremely well to this exercise at a high level. Almost every board paper that I have seen can be repurposed into something that could be put into a large language model without disclosing any information that is not already in the public domain. Senior executives who do not use these models to question and analyse the risks and strategies that their firms are engaged in are missing out on a very powerful advocate. Should Claude be in the boardroom? Well, not as a voting member of the board, but definitely at the table, discussing strategy and risks, he makes a cheap and efficient consultant.

  • Strange Meeting

    When I was at school, I was fortunate enough to have an excellent English teacher, Tim Cornish. What made him exceptional was his ability to bring literature to life for a group of schoolboys, which is indeed a gift. One of the poets he introduced us to was Wilfred Owen. This has led me to a lifetime interest in the work of this great poet. He could articulate the graphic and horrific scenes that confronted him on the Western Front during the First World War. I'm not certain where it came from, but I had a lightbulb moment: what if I used one of Owen's poems as a prompt for ChatGPT? ChatGPT gave me a list of Owen’s poems from which I selected, and this is what I got: Dulce et Decorum Est: Exposure: The Sentry: Strange Meeting: Owen's skill with words translated into the most vivid pictures. One art form is converted into another: It's quite remarkable. It added to my impression that AI vision has come a long way. Furthermore, it made me wonder whether ChatGPT's ability to interpret images had improved too. Taking a picture of a red, amber, and green risk report, I asked ChatGPT to explain what it was seeing. My previous blog is the result: https://www.barbicanconsulting.co.uk/post/red-amber-green-gpt-4-does-risk It is odd how the mind works; my classroom experiences of Owen's poems led to prompting a picture that then led to analysing risk through vision. I never would have thought that my English lessons all those years ago would have helped me do this. It's inconceivable. It's also an example of where mental mindsets are extremely valuable in tackling problems, and AI has been able to facilitate this. That's why there will be many discoveries that are beneficial to mankind over the coming years, arising from connections that AI can help us make, a Strange Meeting.

  • Red, Amber, Green - GPT-4 Does Risk

    I've noticed that GPT-4 is getting better all the time; you can do more, and the results are better. I decided to try a little test. Could GPT-4 take a red, amber, green risk report and explain what it was seeing? Could it then write the narrative so that anyone reading it got a clear understanding of the risks present? Here is the report that I gave GPT-4: Note, this is a photograph of a spreadsheet rather than the spreadsheet itself. The reason is that by trial and error, I've learnt GPT-4 is good with pictures, and it's a feature that has improved over the past few months. This is the prompt that I used: Here is a colour coded report for the board of a building society showing some of its financial risks. Please write a narrative and explain anything you think is important to them or any action required This is the response that I got from GPT-4: The provided image appears to be a financial risk report for a board of a building society, detailing various metrics related to credit exposures, liquidity, interest rate risk, basis risk, and the investment portfolio. Here’s a narrative explaining the key elements: Credit Exposures: Barclays: The actual exposure is £5 million, which is well within the limit of £10 million. Lloyds: The exposure has slightly exceeded the limit by £1 million, which is a point of concern. NatWest: The exposure is £9 million, safely under the limit. Recommendation: The board should discuss why Lloyds' exposure went over the limit and consider strategies to mitigate this risk, possibly adjusting the limit or reducing the exposure. Liquidity: High-Quality Assets: There's a positive variance as the actual high-quality assets are £10 million higher than the limit. Days No Action - Stressed: The society has fewer days than the limit when no action is required in stressed conditions, indicating potential liquidity risk. Stressed with Action: Here, the actual days are above the limit, showing a buffer in the case of stress which requires action. Recommendation: It’s advisable to investigate the reasons for the negative variance in 'Days No Action' and consider reinforcing liquidity management policies. Interest Rate Risk (for a 100 basis points shift): The risk seems to be generally well-contained within limits, but for the 1-3 years category, there's a net loss exposure of £1.1 million. Recommendation: Review the interest rate risk management strategy, especially for the 1-3 years maturity period, to ensure it aligns with the society's risk appetite. Basis Risk (between different interest rates): There is considerable exposure in several categories, with particularly large short positions in the "administered rate" and "fixed rate" categories, leading to a net negative exposure. Recommendation: The board should carefully review basis risk positions, especially those with large short exposures, and consider hedging strategies to mitigate these risks. Investment Portfolio: 0-1 Year: The society has an actual surplus of £10 million compared to the limit. 2-5 Years: Actuals are aligned with the limit. Recommendation: No immediate action is required for the investment portfolio, but ongoing monitoring is essential to ensure it continues to meet the society's strategic objectives and risk tolerances. Overall: The colour coding indicates areas of concern (red and yellow) and areas where performance is satisfactory or better than expected (green). The board should focus on addressing the red areas as a priority, particularly the overexposure to Lloyds and the negative variances in the liquidity and basis risk sections. Monitoring and possibly adjusting exposure limits and risk management strategies will be crucial for maintaining financial stability. GPT-4 provides a straightforward explanation of what it is seeing for all the risks. It differentiates between the colour codes and draws attention to the things that need action. It also raises an early warning for things in amber and doesn't just see things as being right or wrong. This subtle distinction is important because it acts to help preempt future problems rather than waiting for them to happen. Any risk manager would need to apply their oversight to GPT-4's output, but it shows we should be integrating it into workflows rather than spending hours writing things longhand. There is no doubt that by using GPT-4, we will be saving a lot of time. Now, if you can picture this report, or similar, being presented to a board committee complete with the narrative, it's only one more step to give the board the power of GPT-4 to find out more. They could ask about a particular risk and how it was measured, or they could drill down into the bedrock of assumptions to give them comfort concerning their oversight. Let's try this out. Here’s my prompt: You mention the exposure to Lloyds what should I as a board member be concerned about This is GPT-4's response: As a board member, the overexposure to Lloyds should raise several concerns: Risk Concentration: The overexposure indicates a concentration of credit risk with Lloyds. If Lloyds were to face financial difficulties, your building society's financial health could be significantly impacted due to this concentration. Compliance Issues: Credit limits are often set based on regulatory requirements and internal risk management policies. Exceeding these limits can indicate a breach of compliance protocols, which can lead to regulatory sanctions and reputational damage. Risk Appetite Alignment: The limit breach suggests that the society's current dealings with Lloyds may not align with its stated risk appetite. As a board member, it's crucial to ensure that all investments and exposures align with the society's risk tolerance. Risk Management Effectiveness: The overexposure could imply potential weaknesses in the risk management framework or in the operational processes that monitor and control exposures. Liquidity and Capital Implications: If there were to be any issues with Lloyds that affected its ability to meet its obligations, your society could face liquidity constraints or capital losses, affecting its ability to lend or meet its own obligations. Actionable Steps: Review and Assess: Initiate a review of the circumstances that led to the overexposure to understand whether it was a result of a specific investment decision, an oversight, or a lack of adequate controls. Rectify Compliance: If the overexposure is a compliance breach, take immediate steps to bring the exposure back within limits and review policies to prevent future occurrences. Risk Mitigation: Consider diversifying the credit exposure or implementing hedging strategies to mitigate the risk associated with Lloyds. Engage with Management: Discuss with the management team to get a full picture of the relationship with Lloyds, the rationale behind the current exposure, and any measures they are planning to address the issue. Monitor Regularly: Ensure that the situation is monitored closely and regularly, with clear reporting lines to the board. The key is to respond proactively to the overexposure, ensuring it's addressed in a manner consistent with the society's overall strategic goals and risk management framework. All of the points raised by GPT-4 are relevant, although it will be up to individual board members to decide what's important in the context of what they know. GPT-4 isn't doing the board's role but it's helping them think through the issues. If it was felt that some follow-up questions would spark discussion, GPT-4 is there to assist: Me: Please could you suggest five questions that would be high-level and would encourage discussion on this GPT-4: Certainly, here are five high-level questions designed to provoke thoughtful discussion on the board regarding the overexposure to Lloyds: Risk Management and Oversight: "What factors led to the overexposure to Lloyds, and how did our risk monitoring processes allow this to happen?" Strategic Implications: "Considering our current risk appetite and investment strategy, how does this overexposure to Lloyds align or misalign with our society's strategic objectives?" Compliance and Regulatory: "What are the regulatory implications of our overexposure to Lloyds, and what immediate and long-term steps should we take to ensure compliance?" Operational Control: "Can we discuss the adequacy and effectiveness of our operational controls and whether they need strengthening to prevent future occurrences of limit breaches?" Remedial Actions and Future Prevention: "What specific actions will management take to rectify the current overexposure, and what preventive measures are we considering to mitigate such risks in the future?" These questions aim to not only understand the root cause of the issue but also to engage the board in a strategic conversation about aligning investment decisions with the society's risk profile and compliance requirements. What you see is that not only can GPT-4 respond to RAG-based risk reports, it can write the narrative flagging the risks that need to be monitored and then go on to explain some of the concerns that would be relevant to a board member, providing questions that would elicit more discussion. Whether you are a treasurer, risk manager, or board member, you cannot ignore the fact that the way we are doing business is being transformed. Yes, ChatGPT is not perfect and if you run this again you will get slightly different results. But by undertaking these experiments you will learn how to integrate AI into your business. If you have found this helpful would you like to leave a comment on how you use or plan to use AI?

  • Picture this

    I've taken a screenshot of the notes from my previous blog and uploaded them to ChatGPT. Then I've asked ChatGPT to write using the following prompt: "please create a long-form article from my notes. Avoid hyperbole and the use of lots of adjectives, the tone should be factual and informative. I do want an Introduction, White collar work, Communication, all Multimodel features, implications for work as themes thank you" I was initially disappointed. I was expecting something much more in my style However, this expectation is unrealistic. When I wrote my original article, I took a lot of time and I developed my thought processes. While GPT-4 is an excellent scribe, it doesn't have the same thought patterns that I have. Furthermore, I used ChatGPT to investigate some of my chains of thought. I asked it to help improve the understandability and readability of my text; we had a long dialogue, and in my opinion, it improved the clarity. As a result of this experiment, I've learned this: ChatGPT in its GPT-4 version can certainly take a picture of your notes and turn them into an article. If someone had shown me this only 12 months ago, I would have been astounded at the speed and quality of what it could do. In the future, as we rely more and more on large language models, our critical thinking skills could become, well, less critical. When you write, part of the benefit is the process of developing your thoughts. By using ChatGPT to do the job for you, this process is short-circuited and, worse, in many cases, it may lead to lazy thinking. It's a bit like the quality of my handwriting, which has deteriorated over the years I've been using a keyboard. ChatGPT has a voice of its own. It writes well, but the way it writes is not the same as the way that you write. Some people will prefer the artificial text, and others your text. What is certain is that your way of expressing things will not be presented by ChatGPT unless you do a lot of prompting, encouraging it to adopt your tone. If you need to think things through and add your tone use ChatGPT, let it help you write and do it jointly. You can think of this just like a painter who has a particular style; their picture is unique and conveys what they see. Use Chat GPT as the brush. For generic documents and when you just need to get things done ChatGPT excels on its own. Don't get hung up on its inability to write as you would. Many people are not wordsmiths, and the fact that ChatGPT can do a very adequate job in writing means that people who have trouble expressing themselves on paper are now in a situation where they can do so easily and clearly. My conclusion is that ChatGPT or another LLMs can write just from a picture of some basic notes; something you may have jotted in a meeting. It will turn them into an effective document but if you want to add your stamp it's harder. Choosing which approach to take I leave to you. Be the Judge: The article I wrote This is what ChatGPT made off my notes: Embracing Multimodal AI in White Collar Professions: A Detailed Overview Introduction In the epoch of digital transformation, white-collar work has been consistently reshaped by technological advancements. Among the plethora of innovations, multimodal artificial intelligence (AI) systems, particularly those like ChatGPT, stand at the forefront. With capabilities that extend beyond simple task automation, these systems are becoming essential tools in the professional toolkit. This exploration delves into the integration and implications of multimodal AI in the landscape of white-collar work. The Role of ChatGPT in White Collar Work The advent of AI in white-collar professions has introduced a new dimension of efficiency and capability. ChatGPT, powered by OpenAI's GPT-4, is a significant contributor to this shift. Through its design, it learns by trial and error, enhancing its ability to assist professionals in various tasks. These tasks span the spectrum from analysis and understanding to writing and planning. The practicality of ChatGPT lies in its versatility. It serves as a round-the-clock assistant, ready to support a myriad of functions that form the crux of white-collar work. While it's not flawless, the AI's continuous learning capability ensures that it is an asset that evolves in tandem with the needs and complexities of modern professions. Communication: The AI Facilitator Communication is at the heart of white-collar jobs, whether it's through written emails, reports, or dialogues. Here, ChatGPT serves as a communication facilitator. It not only aids in crafting clear and coherent messages but also enables the absorption and relay of information in a structured manner. This is crucial when it comes to learning adaptation, where new skills are applied to familiar tasks, or explaining complex ideas to others in a comprehensible way. ChatGPT exemplifies the notion of "knowledge work," where information is processed and tasks are completed more efficiently. It also excels at breaking down complex problems into manageable segments, allowing for a clearer assessment of potential solutions and outcomes. Multimodal Capabilities: Enhancing AI's Workforce Integration ChatGPT is emblematic of the multimodal AI revolution, showcasing an array of features that mimic the multifaceted nature of human job functions. This multimodality means that it can operate across various mediums and methods: Data Analysis: ChatGPT can conduct rigorous data analysis by processing uploaded datasets, identifying patterns, and making predictions, thereby streamlining decision-making processes. Web Browsing: With browsing capabilities through Bing, it can access and synthesize information from the web, effectively functioning as a research assistant. Visual Processing: By uploading images, users can engage with the AI to understand and interact with visual data, an essential function for professions relying on graphical information. Creative Generation: The integration of Dalle into the AI suite allows ChatGPT to generate images, thereby assisting in the creative aspects of white-collar tasks, from presentations to product design. These features not only expand the utility of AI in the workplace but also herald the biggest technological change in how professional tasks are approached and executed. Implications for White-Collar Work The implementation of multimodal AI, such as ChatGPT, in white-collar professions comes with significant implications. The speed of task completion and the ability to operate in multiple ways mirror the dynamic and diverse nature of human workers. These AI systems support and sometimes surpass human capability, especially in terms of availability and speed. This AI transformation facilitates a more efficient work environment, where tasks that used to take hours can now be completed in minutes. Moreover, by taking over repetitive and time-consuming tasks, AI allows professionals to focus on more strategic and creative aspects of their work, fostering innovation and driving growth. The multimodal nature of ChatGPT also indicates a future where AI could potentially replace certain jobs. However, it is more likely that AI will augment human roles, leading to job evolution rather than elimination. The change prompted by AI in white-collar work is profound, and its full impact will unfold as the technology matures and becomes more integrated into everyday professional activities. Conclusion As we stand at the brink of an AI revolution in the workplace, it is clear that tools like ChatGPT will play an integral role in shaping the future of white-collar professions. These advanced systems promise not just efficiency but also a transformative approach to how work is conducted. As we continue to harness the power of multimodal AI, we are paving the way for a new era of professional expertise, where technology and human ingenuity coalesce to unlock unprecedented potential.

  • Why you need GPT-4

    Introduction to ChatGPT If you haven't tried ChatGPT and are a bit lost about where to start, the following will help you. ChatGPT is software that allows you to use artificial intelligence; it's a type of chatbot developed by OpenAI, capable of engaging in conversational dialogue through natural language processing. It can perform a variety of tasks including answering questions, composing essays, summarising documents, developing software, and recognising images. Background and Evolution of GPT Models Let's understand, in simple terms, more about GPT-4 and other models, (my apologies to the clever people who worked on it). Machine learning (ML) has been around since the late 1940s. The recent development of being able to program Artificial Intelligence (AI) through natural language has occurred as a result of combined breakthroughs in mathematics, the internet, and computer hardware, in particular, memory and processing speed. The mathematicians and engineers designed and built clever code called a Generative Pre-Trained transformer (GPT). You can think of this as a next-word predictor, like the one you use when you text. Once the algorithm (GPT) has been built, it is trained. This requires a vast amount of data typically taken from the web and books. The GPT processes this data and works out the relationship between words so it becomes a next-word prediction model. This takes a lot of computing power to do. Once complete, the model is not perfect; it needs fine-tuning, which is done by asking it questions, seeing its response, and giving it feedback. Once the process is complete, it can be used either as a closed model, like GPT-4, where you have very little control over the way it works via its weightings, or as an open-source model like LLAMA from Meta, where you can adjust the way it works by changing the weightings. Some models are small, like Mistral’s 7 billion parameter model which can run on a laptop, whereas GPT-4's 400 billion parameter model requires Graphic Processing Units. These are very powerful arrays of chips and, at the time of writing, are expensive and highly sought after. Overview of GPT-4 and why you need it and not the free version There are several versions of ChatGPT you can use. The free version is GPT-3.5. The paid version is GPT-4. I’ll describe some of the differences: GPT-4 has a significantly enhanced capability to understand and generate more text. This is because its model has 400 billion parameters*, whereas GPT-3.5 has 175 billion parameters. GPT-4's context window is approximately 25,000 words (32,000 tokens), while GPT-3.5 has a window of 6,500 to 7,000 words (8,192 tokens). This is important; the larger context window allows for more extended interactions, enhanced linguistic finesse, programming power, image and graphics understanding, and a reduction in inappropriate or biased responses. However, while GPT-4 offers more depth and complexity in its responses, it is slower than GPT-3.5. GPT-4 has a limit of 25 to 50 messages every 3 hours; GPT-3.5 is unlimited. From experience, it is quite feasible to hit this cap, and you have to use 3.5 or have a coffee break before proceeding further with GPT-4. GPT-4 is multimodal; it can do more than generate text; it can code, read documents, examine pictures, generate pictures, write computer code, and a lot more. If you want to use the best model with all the functionality and are serious about using ChatGPT, then you need GPT-4. At $20.00 a month, you will soon find, as I have, it’s good value. I do not get paid to write this by OpenAI. Practical Applications of GPT-4 in Knowledge Work Many of us are employed in what we call knowledge work, which involves many separate tasks. You could describe these as writing, talking, analysing, explaining, presenting, and persuading. Information comes to us, we process it and in turn, add value to it before passing it on to others via conversation, emails, texts, spreadsheets, graphs, and presentations. All of these tasks have one thing in common; they come under the heading of communication. To improve our communication, we are learning new skills and adapting old ones to make sense of the world and convey messages to others. When you use GPT-4, you realise that, in its multimodal form, it can integrate into what you do. It has tools and features which can be used in various situations to help you. GPT-4 is the Swiss army knife of knowledge work. The tools that it has when used properly, can not only speed up the way you do things but can also improve what you do. And if you are doing things faster and better, this is a “good” thing. In a market-based economy, it makes you more efficient and therefore less prone to obsolescence. How GPT-4 helps When you consider all the skills we use at work, you realise the development of GPT-4 has not been a haphazard process. Its multimodal functionality helps us undertake what we do. Let's have a look at some of these features: Writing: Through the chat or prompt interface, you can communicate with ChatGPT and ask it to do anything related to text. It will write an email, a thank you note, a memorandum, a letter; this list is endless. It will write in a particular tone or style or enter a conversation with you. Information retrieval: GPT-4 has a training database drawn from a mass of information from the web. You can ask it for facts, information, and opinions on just about any topic. The model that it currently works on has a cut-off point of April 2023. If you want reliable information after this date, you must ensure that GPT-4 is enabled to browse the web. Browsing: GPT-4 does this through Bing. When you converse with GPT-4, it can search the web for more up-to-date information and use that in its response. With the browse function enabled, GPT-4 will decide when to browse. I’ve found it helpful to ask it to browse, particularly when you want events post-April 2023 to be included in your conversation. Data analysis: GPT-4 has a powerful data analysis capability, which works with both numeric and text data. For example, you can upload a spreadsheet and ask it to analyse trends or upload text-based customer feedback and ask it to comment on sentiment. With trial and error, GPT-4 will help you gain insights quickly, easily, and cheaply. Vision: You can upload pictures to GPT-4 and work with these. For example, you can upload a handwritten note and get it transcribed, and then rewrite the contents using GPT-4. You could upload a picture of a report, for example, a technical medical paper or diagnosis, and ask GPT-4 to explain the contents to a layman, and it will do it. DALL·E: This is Chat GPT's picture-generating model. You can ask GPT-4 to generate a picture by giving it instructions in the prompt window; it will then generate a picture that mirrors your request. You influence the output by altering your prompt. Writing computer code: GPT-4 enables non-programmers to write executable code, it not only generates code snippets that run directly but also offers features like code cleanup, code changes, and insights on coding practices. It can write in languages such as Python and HTML, making coding accessible to individuals with varying levels of technical expertise. Furthermore, GPT-4's autonomous code-writing capabilities are closely linked to the data analysis function in that it writes code to undertake the data analysis tasks; prompting the model leads to code writing and execution and as a non-programmer, this is how I use it. Custom GPTs: These represent an advanced yet user-friendly feature that allows you to create tailored AI models based on specific prompt configurations. Imagine the process of interacting with GPT-4 by crafting detailed prompts to guide its responses or actions. These carefully constructed prompts can be transformed into custom GPTs, enabling you to automate repetitive tasks or specialised queries without the need for continuous prompting or retraining of the base GPT-4 model. These custom models can be saved for personal use, shared with select individuals via a direct link, or made publicly available through the GPT Store. The GPT Store functions similarly to an App Store, offering a platform where anyone can discover and utilise custom GPTs created by others. I find the custom GPTs very helpful as they let me use GPT-4 in a way that would normally require developer skills. Accessing GPT-4 There are several ways to access GPT-4; the best way depends on what you need it for: For individuals seeking a straightforward and user-friendly way to interact with GPT-4, a personal subscription to ChatGPT is the best option. It provides easy access to the AI's conversational capabilities without the need for technical knowledge or development skills. This subscription is well-suited for a wide range of personal use cases, from learning and entertainment to productivity and creative assistance. For business users, there is either API access, which is direct access to GPT-4 through OpenAI's API, (encrypted key), this is suitable for developers and businesses looking to integrate AI capabilities into their applications or services. Or Team GPT which provides collaborative features and more confidentiality (you can stop OpenAI training on your data). It's designed for enterprise use, requiring a more expensive business subscription due to a minimum of two seats. You can also use OpenAI’s sandbox, which is another environment primarily for development where you can adjust the parameters and see how various prompts behave. Finally, you may be using GPT-4 without knowing it through third-party websites or applications integrating the API into their offerings. This method can vary in user experience and functionality, depending on how the third party has utilised the GPT-4 capabilities. The World of Work and GPT-4’s Role In the knowledge economy, our work involves reading, writing, and arithmetic (if I can use that as a description of coding, analysis, and strategy), and the faster that we can obtain information and convert it into something valuable and then communicate with others, the more efficient we become and the more valuable we are. When you consider GPT-4, it has all the things that we do built into it. It can write, browse, analyse, study pictures, make pictures, write code, and enter dialogue, it can save your prompts and store them for you; it is multifunctional. When you see this, you realise GPT-4 and similar general models are massive steps forward in our ability to deal with data and convert this quickly and intelligently into something usable and helpful. Because these models are the worst models that we're going to use and they are getting better and better very quickly, they will soon be on par with, or better than, human experts in a given field. Where your work is not at an expert level, then it is very likely that these models will replace what you do because they are cheap. This means economic disruption for many. The only way to take advantage of these changes is to embrace the use of these models in the work you do. The best way to do this is to see them as helpful graduate trainees who have a very good basic understanding of how the world works, but they do not currently have in-depth expertise that has been gained through experience. By working with them, much of the day-to-day work can be delegated out, and you can concentrate on using your experience to take your work to a higher level. Summary Models like GPT-4 have changed things. In white-collar industries, we are paid for our knowledge and ability to use information, process it, and complete all sorts of tasks. I’ve called this communication. At first sight, GPT-4 is a chatbot, but once you explore all the things it can do, there is a whole suite of tools like the proverbial Swiss army knife. There is no training manual because how you use it depends on what you want it to do, your curiosity, and your willingness to accept change. Because this computing power is cheap and effective, the workplace will change; if your job can be done by GPT-4 or its offspring, your bargaining power is likely to decline. The rate of change will be much faster than, say, web usage as the infrastructure and incentives are in place, and the advancements are speeding up, not slowing down. You should consider bringing these models into your work to future-proof what you do. Use them as helpful assistants, treat them in the same way you would a co-worker who you are instructing, and you will be amazed at what they can do for you. This is truly where you can learn. Using GPT-4 is a very practical skill that is largely gained from practice. You can get the basics in a few hours but “intuition” about its capability needs a lot more time and experimentation. By working together, you can see what you can do that it can’t, this is where you are adding value and it’s where the future is. Critical thinking, emotional intelligence, and complex problem-solving are increasingly valuable in a GPT-4 augmented workplace. This is both a great change but also a massive opportunity to do new things. *A note on parameters: Think of the parameters in a GPT model as the internal parts of an engine that you can adjust to optimise performance. These parameters are akin to the nuts, bolts, timing belts, spark plugs, and fuel injectors in a car's engine. Parameters as Engine Components: Adjustable Settings: Just like you can tweak the timing of the spark plug or the air-to-fuel ratio to get the best performance out of an engine, in a GPT model, the parameters (which are essentially weights in the neural network) are adjusted during the training process to make the model more accurate. These adjustments are based on the data the model is trained on, similar to how you might tune an engine based on the type of fuel it will use or the conditions it will operate in. Complexity and Performance: The number of parameters in a GPT model is similar to the complexity of an engine. An engine with more components (like a V8 versus a V4) can be more powerful if tuned correctly, but it's also more complex to manage. Similarly, a GPT model with more parameters can process language more effectively, capturing nuance and complexity in text, but requires more data and computational power to train and tune. Optimisation for Efficiency: In the same way that you might use a turbocharger to increase the efficiency and output of an engine without making it bigger, optimisation techniques in machine learning adjust these parameters to improve the model's performance without necessarily increasing its size. This involves fine-tuning the model's parameters to ensure that it can understand and generate language as accurately and coherently as possible. Fine-Tuning for Specific Tasks: Imagine you have a basic engine setup that you can then fine-tune for different purposes, like racing, towing, or fuel efficiency. Similarly, once a GPT model has been pre-trained on a large dataset to understand language generally, it can be fine-tuned by making smaller adjustments to its parameters for specific tasks, such as writing in a particular style, translating languages, or answering questions. This fine-tuning process adjusts the model's internal settings to specialise in the task at hand, much like adjusting an engine to optimise it for a specific type of driving. In the workshop of machine learning, parameters are the internal components of the GPT "engine" that determine how it processes and generates language. By adjusting these parameters through training and fine-tuning, similar to how a mechanic would tune an engine, we can optimise the model's performance for specific tasks or improve its overall efficiency and accuracy. Just like with engines, the skill lies in knowing how to adjust these parts correctly, balancing power, efficiency, and usability to meet the desired outcome.

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