Why investing in a whisky firm can leave you with a VERY nasty hangover, by consumer champion TONY HETHERINGTON
Ms N.P. writes: I bought bonds in beverage company Linc Drinks Ltd and casks
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The Guardian
23 minutes ago
- The Guardian
The Guardian view on Britain's AI strategy: the risk is that it is dependency dressed up in digital hype
There was a time when Britain aspired to be a leader in technology. These days, it seems content to be a willing supplicant – handing over its data, infrastructure and public services to US tech giants in exchange for the promise of a few percentage points of efficiency gains. Worryingly, the artificial intelligence strategy of Sir Keir Starmer's government appears long on rhetoric, short on sovereignty and built on techno-utopian assumptions. Last week Peter Kyle, the technology secretary, was promoting the use of AI-generated discharge letters in the NHS. The tech, he said, will process complex conversations between doctors and patients, slashing paperwork and streamlining services. Ministers say that by applying AI across the public sector, the government can save £45bn. But step back and a more familiar pattern emerges. As Cecilia Rikap, a researcher at University College London, told the Politics Theory Other podcast, Britain risks becoming a satellite of the US tech industry – a nation whose public infrastructure serves primarily as a testing ground and data source for American AI models hosted on US-owned cloud computing networks. She warned that the UK should not become a site of 'extractivism', in which value – whether in the form of knowledge, labour or electricity – is supplied by Britain but monetised in the US. It's not just that the UK lacks a domestic cloud ecosystem. It's that the government's strategy does nothing to build one. The concern is that public data, much of it drawn from the NHS and local authorities, will be shovelled into models built and trained abroad. The value captured from that data – whether in the form of model refinement or product development – will accrue not to the British public, but to US shareholders. Even the promise of job creation appears shaky. Datacentres, the physical backbone of AI, are capital-intensive, energy-hungry, and each one employs only about 50 people. Meanwhile, Daron Acemoglu, the MIT economist and Nobel laureate, offers a still more sobering view: far from ushering in a golden age of labour augmentation, today's AI rollout is geared almost entirely toward labour displacement. Prof Acemoglu sees a fork: AI can empower workers – or replace them. Right now, it is doing the latter. Ministerial pledges of productivity gains may just mean fewer jobs – not better services. The deeper problem is one of imagination. A government serious about digital sovereignty might build a public cloud, fund open-source AI models and create institutions capable of steering technological development toward social ends. Instead, we are offered efficiency-by-outsourcing – an AI strategy where Britain provides the inputs and America reaps the returns. In a 2024 paper, Prof Acemoglu challenged Goldman Sachs' 10-year forecast that AI would lead to global growth of 7% – about $7tn – and estimated instead under $1tn in gains. Much of this would be captured by US big tech. There's nothing wrong with harnessing new technologies. But their deployment must not be structured in a way that entrenches dependency and hollows out public capacity. The Online Safety Act shows digital sovereignty can enforce national rules on global platforms, notably on porn sites. But current turmoil at the Alan Turing Institute suggests a deeper truth: the UK government is dazzled by American AI and has no clear plan of its own. Britain risks becoming not a tech pioneer, but a well-governed client state in someone else's digital empire. Do you have an opinion on the issues raised in this article? If you would like to submit a response of up to 300 words by email to be considered for publication in our letters section, please click here.
Finextra
24 minutes ago
- Finextra
A Data Dilemma: Reclaiming Time for Profit in the Financial Markets
The financial markets are a relentless, data-driven ecosystem. Success hinges on the speed and accuracy with which institutions can extract, analyze, and interpret the torrent of market information. Data analysts and quantitative analysts (quants) are the critical navigators of this complex landscape, yet they often find themselves mired in a Sisyphean task: wrestling with the sheer volume and complexity of messy financial data. This "data wrangling" bottleneck consumes valuable time, hindering the strategic analysis that drives profitability, maintains regulatory compliance, and ultimately, defines competitive advantage. This article delves into the core challenges facing data professionals in finance and explores how intelligent automation, particularly through the strategic application of Artificial Intelligence (AI), offers a powerful and transformative solution, directly impacting the bottom line. The Data Wrangling Bottleneck: A Costly Impediment to Profitability The core dilemma stems from the inherent complexity of financial data. Analysts in the financial sector are routinely confronted with: Data Silos & Fragmented Information: Imagine a global investment bank. Data is scattered across a multitude of sources: real-time market data feeds (Bloomberg, Refinitiv), internal trading platforms, legacy systems, and various proprietary databases. This fragmented landscape necessitates manual data aggregation, a time-consuming process that delays critical insights. Business Use Case: A proprietary trading desk struggles to correlate market sentiment derived from news feeds with trading volume data from different exchanges. Manual aggregation delays their ability to identify and capitalize on emerging trading opportunities, leading to missed profits. Imagine a global investment bank. Data is scattered across a multitude of sources: real-time market data feeds (Bloomberg, Refinitiv), internal trading platforms, legacy systems, and various proprietary databases. This fragmented landscape necessitates manual data aggregation, a time-consuming process that delays critical insights. Data Quality Issues: The Silent Killer of Accuracy: Inconsistent formats, missing values, and inherent errors are commonplace. This necessitates painstaking cleansing, validation, and transformation. Business Use Case: A hedge fund's risk management team relies on accurate pricing data for its portfolio. Data quality issues, such as inaccurate closing prices, can lead to flawed risk assessments and potentially disastrous trading decisions. The time spent correcting these errors directly impacts the speed and effectiveness of their risk mitigation strategies. Inconsistent formats, missing values, and inherent errors are commonplace. This necessitates painstaking cleansing, validation, and transformation. Manual Reporting Demands: Compliance at a Cost: The regulatory landscape is constantly evolving (MiFID II, Dodd-Frank, Basel III, etc.), demanding complex reporting on trading activities, risk exposure, and portfolio performance. These reports often require tedious manual processes, diverting valuable analyst time away from strategic initiatives. This is where data security and privacy become paramount. Regulations like GDPR and CCPA mandate strict control over data, requiring robust security measures throughout the data lifecycle. Business Use Case: A global asset manager faces mounting pressure to comply with evolving ESG (Environmental, Social, and Governance) reporting standards. Manually compiling and analyzing the necessary data to meet these requirements is time-intensive and limits their ability to focus on investment strategy and client service. Moreover, they must ensure the security and privacy of the data used in these reports, especially when dealing with client information. This includes secure storage, access controls, and adherence to data minimization principles. The regulatory landscape is constantly evolving (MiFID II, Dodd-Frank, Basel III, etc.), demanding complex reporting on trading activities, risk exposure, and portfolio performance. These reports often require tedious manual processes, diverting valuable analyst time away from strategic initiatives. This is where data security and privacy become paramount. Regulations like GDPR and CCPA mandate strict control over data, requiring robust security measures throughout the data lifecycle. These activities, while essential for operational integrity, represent a significant opportunity cost. This time drain translates into: Reduced Productivity & Diminished Returns: Analysts spend less time on value-added activities, such as model building, strategy development, and in-depth market analysis, directly impacting the development of new products and services. Analysts spend less time on value-added activities, such as model building, strategy development, and in-depth market analysis, directly impacting the development of new products and services. Missed Trading Opportunities: The Cost of Delay: Delayed insights and slower decision-making can result in missed opportunities to capitalize on market fluctuations, impacting revenue generation. Business Use Case: A high-frequency trading firm relies on milliseconds to gain an edge. Delays in data processing and analysis, even by fractions of a second, can mean the difference between a profitable trade and a missed opportunity. Delayed insights and slower decision-making can result in missed opportunities to capitalize on market fluctuations, impacting revenue generation. Increased Risk Exposure: Vulnerability to the Unseen: Delays in risk assessments and anomaly detection can leave firms vulnerable to unforeseen risks, potentially leading to substantial financial losses. Furthermore, inadequate data security practices can expose sensitive financial data to breaches, leading to financial and reputational damage. Robust security protocols are crucial for mitigating this risk. Business Use Case: A derivatives trading desk struggles to identify and mitigate potential flash crashes or "fat finger" errors. The inability to process data quickly enough can expose them to significant downside risk. Delays in risk assessments and anomaly detection can leave firms vulnerable to unforeseen risks, potentially leading to substantial financial losses. Furthermore, inadequate data security practices can expose sensitive financial data to breaches, leading to financial and reputational damage. Robust security protocols are crucial for mitigating this risk. AI as a Strategic Asset: Automating the Mundane, Empowering the Strategic AI offers a compelling solution by automating the most time-consuming and repetitive aspects of the data analysis workflow, freeing up analysts to focus on higher-level tasks that directly impact profitability, innovation, and competitive differentiation. Key applications of AI in this context include: Automated Data Extraction: Streamlining the Intake: Intelligent connectors can ingest data from diverse sources, automatically formatting and integrating market data, internal data, and external feeds. Business Use Case: An investment bank can use AI-powered data ingestion to automatically pull data from multiple sources (market data feeds, internal order management systems, etc.) and standardize it for use in their trading algorithms, reducing manual effort and improving data consistency. Crucially, these connectors must incorporate robust security measures, including encryption, access controls, and secure data transfer protocols, to prevent unauthorized access and data breaches. Data privacy must also be considered during extraction, ensuring compliance with regulations. Intelligent connectors can ingest data from diverse sources, automatically formatting and integrating market data, internal data, and external feeds. Automated Data Cleansing and Transformation: Ensuring Data Integrity: AI-powered anomaly detection and data validation tools can automatically handle missing values, outliers, and inconsistencies, ensuring data quality. This process must be conducted within a secure environment, protecting the data from unauthorized access or modification. Data masking and anonymization techniques can be employed to protect sensitive information during cleansing and transformation. Business Use Case: A broker-dealer can use AI to automatically cleanse and validate trade data, eliminating errors and inconsistencies that could lead to regulatory violations or financial losses. AI-powered anomaly detection and data validation tools can automatically handle missing values, outliers, and inconsistencies, ensuring data quality. This process must be conducted within a secure environment, protecting the data from unauthorized access or modification. Data masking and anonymization techniques can be employed to protect sensitive information during cleansing and transformation. Automated Reporting & Intelligent Insights: From Data to Decisions: AI can generate real-time reports on portfolio risk, trading performance, and regulatory compliance, freeing up analysts from manual reporting. The generation and distribution of these reports must adhere to strict security protocols, including access controls, encryption, and secure transmission channels. Data privacy considerations must be integrated, ensuring that only authorized individuals have access to sensitive information. Business Use Case: A private equity firm can use AI to automatically generate reports on the performance of its portfolio companies, providing insights into key metrics and identifying potential problems early on. This allows them to make faster, more informed investment decisions and improve returns. AI can also be used to analyze historical data and identify patterns that can be used to predict future performance. AI can generate real-time reports on portfolio risk, trading performance, and regulatory compliance, freeing up analysts from manual reporting. The generation and distribution of these reports must adhere to strict security protocols, including access controls, encryption, and secure transmission channels. Data privacy considerations must be integrated, ensuring that only authorized individuals have access to sensitive information. Predictive Analytics for Proactive Strategies: AI can analyze historical and real-time data to predict market trends, identify arbitrage opportunities, and optimize trading strategies. The models used for predictive analytics must be developed and deployed with robust security and privacy measures. This includes secure model storage, access controls, and regular security audits to prevent unauthorized access or model manipulation. Business Use Case: A quantitative hedge fund can use AI to build predictive models that identify high-probability trading opportunities, enabling them to generate alpha more efficiently. AI can analyze historical and real-time data to predict market trends, identify arbitrage opportunities, and optimize trading strategies. The models used for predictive analytics must be developed and deployed with robust security and privacy measures. This includes secure model storage, access controls, and regular security audits to prevent unauthorized access or model manipulation. This automation translates into tangible, measurable benefits: Increased Efficiency & Optimized Operations: Analysts can process more data, analyze more opportunities, and respond to market changes more rapidly, leading to greater throughput and reduced operational costs. Analysts can process more data, analyze more opportunities, and respond to market changes more rapidly, leading to greater throughput and reduced operational costs. Improved Decision-Making & Enhanced Returns: AI-driven insights can provide a deeper understanding of market dynamics, leading to more informed trading decisions and ultimately, improved investment returns. AI-driven insights can provide a deeper understanding of market dynamics, leading to more informed trading decisions and ultimately, improved investment returns. Enhanced Risk Management & Mitigation of Losses: Faster and more accurate risk assessments can help firms mitigate potential losses, safeguarding capital and preserving reputation. Faster and more accurate risk assessments can help firms mitigate potential losses, safeguarding capital and preserving reputation. Improved Compliance & Reduced Regulatory Risk: Automated reporting and data validation can streamline compliance efforts, reducing the risk of regulatory penalties and ensuring operational resilience. Crucially, AI systems must be designed to comply with all relevant data security and privacy regulations, ensuring that data is handled securely and in accordance with legal requirements. Key Considerations for Successful Implementation: While the benefits of AI are undeniable, successful implementation in the financial sector demands careful planning and execution: Security and Compliance: Data Integrity as a Cornerstone: Solutions must prioritize data security, adhere to stringent regulatory standards (GDPR, CCPA, etc.), and provide auditable processes to maintain trust and meet regulatory requirements. This includes robust encryption, access controls, regular security audits, and data loss prevention measures. Compliance with industry-specific regulations like PCI DSS is also critical. Solutions must prioritize data security, adhere to stringent regulatory standards (GDPR, CCPA, etc.), and provide auditable processes to maintain trust and meet regulatory requirements. This includes robust encryption, access controls, regular security audits, and data loss prevention measures. Compliance with industry-specific regulations like PCI DSS is also critical. Transparency and Explainability: Building Trust Through Understanding: AI-driven insights should be traceable and explainable to maintain trust with regulators, clients, and internal stakeholders. "Black box" AI models are often unacceptable in regulated environments. AI-driven insights should be traceable and explainable to maintain trust with regulators, clients, and internal stakeholders. "Black box" AI models are often unacceptable in regulated environments. Seamless Integration & Minimizing Disruption: Tools should integrate seamlessly with existing trading platforms, risk management systems, and data infrastructure to minimize disruption and ensure a smooth transition. Tools should integrate seamlessly with existing trading platforms, risk management systems, and data infrastructure to minimize disruption and ensure a smooth transition. Skill Development & Talent Acquisition: Successful AI implementation requires a skilled workforce with expertise in data science, machine learning, and financial markets. Investment in training and talent acquisition is crucial. The Path Forward: Embracing AI for a Competitive Edge By strategically embracing AI-powered solutions, financial institutions can empower their data analysts and quants to reclaim their time and focus on what matters most: generating profitable trades, driving innovation, and navigating the complexities of the financial landscape. The shift from data preparation to strategic analysis is crucial for staying competitive in today's fast-paced financial markets. The future of financial data analysis is undeniably shaped by AI, offering a powerful path to unlock the full potential of data and fuel sustained business success. However, the integration of AI must be coupled with a strong commitment to data security and privacy. This includes implementing robust security measures, complying with relevant regulations, and building a culture of data protection. Only then can financial institutions fully realize the benefits of AI while mitigating the risks. Those who embrace this transformation will be best positioned to thrive in the years to come.
Finextra
24 minutes ago
- Finextra
Archax and Stellar collaborate on tokenised RWAs
Archax, the UK regulated digital asset exchange, broker and custodian, today announced a strategic partnership with the Stellar Development Foundation (SDF), that supports the layer-one blockchain network Stellar. 0 This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author. Under the terms of this partnership, SDF has made a direct investment into Archax Group to support Archax's mission to bridge traditional finance and blockchain. Archax has recently completed a series of milestones on the Stellar network; including tokenising an Aberdeen Money Market Fund and integrating Stellar into Archax's tokenisation engine and platform. Archax's extensive network of financial institutions will be able to bring more tokenised real-world assets (RWAs) to the Stellar network. The strategic rationale is the rapid scaling of the tokenisation market, as TradFi organisations see the benefits of adopting blockchain technology, tokenisation and moving assets 'onchain.' RWA tokenisation growth has been little short of explosive; expanding from $15.2 billion in December 2024 to over $24 billion by June 2025, representing an 85% year-on-year increase**. Archax is at the forefront of this growth, with its unique and expanding international regulatory moat, as well as its focus on bridging traditional markets into the digital/crypto/DeFi space. Graham Rodford, CEO and co-founder of Archax, comments: 'The Archax vison has always been that all financial instruments will move onchain, and we find ourselves at a pivotal point right now, because institutional adoption of digital assets is vastly accelerating. 86%* of institutions now have digital assets allocations or are planning to by the end of 2025. That's huge. Having established, credible partners and investors from the crypto world is a fundamental part of our strategy, and we are excited to welcome Stellar into that family. We look forward to bringing even more institutions and real-world assets onto the platform too.' Raja Chakravorti, Chief Business Officer at the Stellar Development Foundation comments: "Real-world assets are moving onchain because costs are lower and transactions can move anywhere around the globe in seconds. The Stellar network was purpose built to enable fast settlement times, low costs, and the tokenisation of real-world assets that is the future of finance. The Stellar Development Foundation is proud to have invested in Archax and excited about where this collaboration can go from here." Archax also recently announced working with Lloyds Bank and Aberdeen Asset Management to use tokenised money-market funds as an acceptable form of collateral, to post as margin across the Archax Nest collateral transfer network, for FX trades. 'The project with Lloyds Bank and Aberdeen is the perfect example of the innovation and benefits that can come from tokenising RWAs - as a result of this new partnership, this could now be done using the Stellar blockchain', adds Rodford. 'We have over 100 funds now available in token form from many leading asset managers. These are all available on Stellar now too.'
