AI and Financial Stability: Questioning Tech-Agnostic Regulation in the UK? Goodwin

GFTN will also sharpen the focus of Elevandi’s five existing forums, which include the Singapore FinTech Festival, and expand into new geographies to double its global footprint over the next five years. “We found that these advisors do not have access to AI or any kind of sophisticated quantitative technologies,” he said. “They’re independent, and they’re feeling the pressure from passive index funds, and so they’re getting marginalized.” • Demonstrate how AI can provide a more comprehensive view of value creation by working with business partners to develop metrics that capture the impact of intangible assets.

The next step is moving from vision to action by creating a plan outlining key milestones and resources needed to implement AI initiatives. Within their plan, finance leaders should also include successful use cases, address data governance concerns and establish clear operating frameworks. The artificial intelligence revolution is in full swing, and AI adoption by finance leaders and organizations is advancing quickly. By crafting strategic narratives that align with key roles and executive priorities, organizations can more effectively secure buy-in for AI initiatives and unlock the full potential of this transformative technology. The travel industry is embracing generative AI to improve the customer experience.

Making the business case for generative AI

AI is being looked at where appropriate, but what the IRS needs from AI more than anything else is transparency, and that can sometimes be lacking. “We’re challenged with ensuring ethical AI and transparency to the taxpayer, which requires a different approach than private sector solutions.” The DG noted the surveys undertaken by the PRA and FCA, noting that early use cases within financial services firms for AI have been fairly low risk from a financial stability standpoint. 41% of respondents are using AI to optimise internal processes, while 26% are using AI to enhance customer support, helping to improve efficiency and productivity. Leading companies like Fireblocks have driven significant advancements in MPC infrastructure. Their platforms offer tools specifically designed for secure key management at an institutional scale, providing the speed and scalability needed for high-frequency transactions.

AuditBoard’s Dam emphasized how quickly AI is shifting things around and pointed out the need for organizations to be proactive—to be mindful of regulatory changes before they happen and to have plans in place. “If you want to stay compliant,” Dam said, “you have to be proactive and not wait for, say, agency guidance.” For example, Ant International uses such models to assess a loan applicant’s credit-worthiness by analysing thousands of data points from its online behaviour and digital footprint. How can firms navigate these internal and external pressures with clarity and confidence?

The future of generative AI is bright, and the opportunities for return on investment are within reach — if you’re ready to seize them. IBM’s Ortiz closed out the panel by reminding us that threats don’t just come in through the proverbial front door—one of the areas where companies can have significant vulnerabilities is via their backups. As attackers increasingly target backups, Ortiz advocated broad use of predictive analytics and real-time anomaly detection in order to spy out any oddness attackers might be up to. Next, we shifted to an infosec outlook, bringing on a four-person panel that included former Ars Technica senior security editor Sean Gallagher, who is currently keeping the world safe at Sophos X-Ops.

Alaska Airlines, Expedia, and IHG Hotels and Resorts have all deployed genAI-powered travel assistants to streamline and personalize the booking process. A survey of 5,000 customer service agents from varying industries using generative AI uncovered that issue resolution increased by 14% an hour, and time spent handling issues decreased by 9%. There has been a lot of speculation about what generative AI can do for businesses. The possibilities are endless — streamlined creative processes, automated business operations, self-service for customers, and more.

Crypto wallets are a compelling solution to the challenges of autonomous money management by AI. Unlike traditional banking accounts, which often require personal identification and human intermediaries, crypto wallets can be created and managed by software without direct human involvement. This independence makes crypto wallets an attractive choice for AI agents that need to manage funds autonomously. The successful implementation of AI solutions often hinges on securing the buy-in of C-suite executives. These strategic decision-makers, typically focused on bottom-line results and long-term business objectives, require compelling narratives that clearly articulate the value and potential impact of AI initiatives.

Building A Future-Ready Workforce

By effectively utilizing AI, organizations can prevent and respond to cyberattacks more efficiently, enhancing their overall security posture. It’s always fascinating to get to ask the IRS anything, and Natarajan gave insightful answers. He opened by contrasting the goals and challenges of the IRS’s IT strategy as a government service organization to the goals of a typical enterprise, and there are obvious significant differences. Fancy features don’t count as much as stability, security, and integration with legacy systems.

Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space. • Showcase successful use cases and how AI is already driving tangible results within the finance team, such as providing strategic insights and enhancing decision making.

AI now presents these leaders with a new slate of concerns and level of complexity as they work to balance compliance and innovation. For example, what data are models trained on, and what are the implications of using customer data in model training? CFOs

and finance leaders are extremely excited about the cost savings and opportunities with AI – but they are also concerned about the risks. Some forward-thinking organizations have already deployed AI agents successfully. The technology is making inroads across many industries, including insurance, marketing, manufacturing, customer service, financial services, supply chain and healthcare. Moreover, financial tools and protocols in traditional banking are designed to serve human users.

The beauty of these grand hypotheses is that, right now, we don’t know for sure what’s going to happen with this still-new technology. And while concerns about the technology’s future and what it means for the world are valid, I’m here to tell you that the AI bubble has not burst. Take, for example, Wall Street questioning whether AI can actually make companies money. Or surveys reporting that a mere 15% of respondents have a line of sight into earning improvements from generative AI initiatives, or that 48% of organizations do not expect to see a transformation from generative AI for one to three years. FPF’s John Verdi dwelled for a bit on the challenge of doing just that and balancing innovation against the need to comply with regs. First-party data and first-party software development, concluded Fisher, will be critically important when paired with generative AI—”table stakes,” Fisher called them, for participating in the future of business.

As a bonus, the foundation is now in place to identify and pursue new revenue opportunities with existing customers, creating a tangible and ongoing return on investment. And the finance department’s success has further evangelized the use of generative AI across the organization. Now we’re using genAI to scale marketing projects, provide a search assistant to our user community, and create valuable use cases that we can share with our customers. As an integration company, we at SnapLogic could see both that generative AI had great potential to accelerate workflows and that building generative AI applications and services was inherently an integration problem. SnapLogic worked quickly to include a generative integration copilot and to enable companies to create LLM-powered applications, assistants, and agents. There are many emerging stories of use cases of generative AI that are advancing automation and productivity in impactful ways.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Risk management is fertile ground for applying AI, including the newer generative AI, the panel noted. AI won’t itself solve a risk problem, but “it will give a human expert a head start on where it’s best to apply efforts” to solve the problem. At the forefront of AI invention and integration, the inaugural Innovation Award winners use wealth management technology to benefit their clients — and their bottom lines. Since those remarks, some software providers who offer AI-backed portfolio analysis and management tools have pushed back, arguing that launching a hedge fund is instead the greater risk.

“Pig butchering” was at the top of his list—that is, a shockingly common romance scam where victims are tricked into an emotional connection with a scammer, who then extorts them for money. Up first were Anton Dam, an engineering VP with Auditboard; John ChatGPT App Verdi of the Future of Privacy Forum; and Jim Comstock, a cloud storage program director at IBM. The main concern of this panel was how companies will keep up with shifting compliance requirements as the pace of advancement continues to increase.

In addition, a byproduct of this effort was an immediate positive impact on revenue. Upon going live, the genAI app enabled the finance department to immediately recover around 2% of revenue, translating to millions of dollars in recouped cash that may have gone uncollected. Our final panel had a deceptively simple title and an impossible task, because there is no “best” infrastructure solution. But there might be a best infrastructure solution for you, and that’s what we wanted to look at. Joining me on stage were Daniel Fenton, head of AI platforms at JLL; Arun Natarajan, director of AI innovation at the IRS; Amy Hirst, VP of site reliability engineering and user experience at IBM; and Matt Klos, an IBM senior solutions architect. It has also signed memorandums of understanding with two central banks on fintech advisory services.

Improving compliance on the AI front requires validation, testing, and tight feedback loops, in addition to transparency, disclaimers, and circuit breakers. The key to success lies in establishing clear policies, embracing strategic foresight,

and committing to responsible AI utilization to usher in a future where AI and compliance converge to redefine the norms of our industry. One study predicts that agentic AI will achieve 60% productivity gains for organizations. Compared to single, one-off AI agents, agentic workflows can tackle more complex tasks, solve more complex problems and achieve greater boosts in efficiency and productivity.

Finance leaders cannot afford to stand on the sidelines as AI rapidly redefines the role of the finance team and of its business partners, such as IT, marketing and HR. Embracing AI is essential to keep finance at the forefront of innovation. Ricky cautions leaders that AI and agentic systems—done correctly—is a capital-intensive game. “We’re looking for a relatively larger than usual capital investment into AI technologies today with the expectation that it will yield results many times bigger than what you’re investing in,” he says.

The Speech is useful in that it highlights specific AI issues which financial services firms and fintech providers should note when thinking about when deploying or developing AI. It is also useful for those thinking about policy, confirming much of what we have said in our previous alerts but showing also that thinking on how government approaches the regulation of AI can and will likely evolve. An AI agent can interact with a wallet’s ChatGPT API, setting rules for transactions, managing permissions, and even linking to decentralized finance (DeFi) protocols, allowing it to perform a variety of financial operations. This programmability empowers the AI to act as a fully autonomous agent, capable of managing assets without manual intervention, a capability rarely available in traditional finance. There is a greater need for more diversified portfolio modeling services.

Amy Hirst pointed out that when building one’s own AI/ML setup, traditional performance metrics still apply—and they apply across multiple stacks, including both storage and networking. Her advice sounds somewhat traditional but holds absolutely true, even now. “Customers assume it’s everywhere, but it’s often a limiting factor, especially in high-demand AI infrastructure.” For this panel, we wanted to look at the landscape around us, and Sean kicked the session off with a sobering description of the most profligate cyber threats as they currently exist today.

The DG refers to the regime for critical third parties (CTPs), which we discuss further below, and the use of stress tests to understand how AI models used for trading whether by banks or non-banks could interact with each other. The DG notes that, even if the PRA can deal with an individual firm, interconnectedness – where the actions of one firm can affect others – remains a concern. Firms can become critical nodes and be exposed to common weaknesses and AI could both increase interconnectedness and increase the probability that existing levels of interconnectedness threaten financial stability. “It’s more about driving scale and efficiency right now than actually using it as a tool to improve the way that they do financial planning or the way that they manage assets,” he said. Matrisian said most of the advisors who use AssetMark are testing out AI tools more so for drafting client communications and sentiment and summarizing meetings, for example.

Future financial technology controlled by AI robot using machine learning and artificial … [+] intelligence to analyze business data and give advice on investment and trading decision. “Because everybody’s going to have access to the same data and systems.” One of the keys to achieving this goal will be to create a ai in finance examples strong learning culture within your team, one that values curiosity and gives teams access to learning resources. This involves strategically investing in team development by prioritizing resources that will equip them for success in an AI-driven world—think data analytics, machine learning and business intelligence.

Focus instead on including baseline AI skills, for example AI tools and use cases for your work, or prompt engineering skills. Since its major launch into publicity literally two years ago, AI (artificial intelligence) has rapidly increased in importance to become among the most non-negotiable and fastest growing skills in today’s workforce. Generative AI is pushing many organizations to orient their business around data. According to a 2024 survey report from IT leaders, nearly half of the respondents (48%) indicated they had “created a data-driven organization,” double the percentage who reported doing so from the year prior (24%).

AI agents are advanced AI systems that can complete complex tasks and make decisions on their own. They can analyze data, make predictions, offer insights, converse, solve problems, create strategies and more. They learn over time and adjust to real-time data, offering a high level of accuracy, efficiency and agility. Technical debt, in the form of workarounds and added point solutions stemming from outdated systems, is significantly impacting data stacks and preventing forward motion with generative AI. IT teams spend over 16 hours per week updating or patching legacy systems, time that could be better spent on strategic genAI initiatives. Consequently, 57% of organizations plan to update up to 50% of their legacy technology to utilize generative AI technology.

“Today’s event about privacy, compliance, and making infrastructure smarter, I think, could not be more perfectly timed,” said Fisher. “I don’t know about your orgs, but I know Ars Technica and our parent company, Condé Nast, are currently thinking about generative AI and how it touches almost every aspect or could touch almost every aspect of our business.” The PRA, which is charged mainly with oversight of the stability of the banking system and financial position of banks and large investment banks in the UK, had welcomed the Government’s principles-based, sector-led approach to AI regulation.

Joining Sean were Kate Highnam, an ML engineer at Booz-Allen Hamilton; Dr. Scott White, director of cybersecurity at George Washington University; and Elisa Ortiz, a storage and product marketing director at IBM. Cross-border compliance also came up—with big cloud providers and data that perhaps resides in different countries, different laws apply. Making sure you’re doing what all of those laws say is hugely complex, and IBM’s Comstock pointed out that customers need to both work with vendors and also hold those vendors accountable for where one’s data resides.

AI goes hand-in-hand with data, and the panel noted that AI is making strides in giving traders more useful knowledge while reducing the noise that comes from data overload. For example, an AI-powered cluster model can screen stocks for characteristics such as capitalization, liquidity, and spread, telling the trader whether a given stock is relatively easy or difficult to trade. In order to do so, please follow the posting rules in our site’s Terms of Service. “Without a doubt … what we’ll see is more and more end clients getting comfortable with that experience,” he said. “At the same time, they [the advisors] are the ones that are ultimately responsible for intuitively making that final decision as what’s going to be most important.” Still, Matrisian said there will come a day when advisors begin using AI-backed software to help with decision-making in portfolio planning.

Joe Ariganello is the VP of Product Marketing at MixMode, where he works with cutting-edge AI technology. The group dispersed, with some folks heading downstairs for a private tour of the museum’s Bond in Motion exhibit, which featured the various on-screen rides of 007. Then there was a convergence on the bar and about an hour of fun conversations.

Koka said StockSnips, which ingests about 50,000 media articles a day in real time to construct portfolio modeling, does not claim to offer any novel approach to cracking the markets. For example, to measure the value of innovation and digital transformation, companies could look at R&D investment as a percentage of revenue, tracking how much is invested in research and development compared to revenue. The percentage of digital transactions or automation tools used within processes is a good indicator of the organization’s digital transformation progress.

“The Best Infrastructure Solution for Your AI/ML Strategy”

The first is to support the Bank of Namibia’s efforts to build its fintech ecosystem and digital public infrastructure. The network will also help the National Bank of Georgia grow the country’s fintech industry. Mr Menon said Gprnt will focus on piloting the use of these tools with financial institutions, corporates, trade associations and government agencies.

But they are extremely powerful—especially when you combine agents together to create agentic workflows, which allows them to accomplish complex tasks. The potential for generative AI to deliver a significant return on investment is not just a theory — it’s a reality being demonstrated by early adopters across various industries. While the road to revenue may seem uncertain, the stories of success are emerging, showing that with the right approach, generative AI can indeed make a measurable impact on your bottom line. Dr. Scott White of GWU took us from scams to national security, pointing out how AI can and is transforming intelligence gathering in addition to romance scams. Booz-Allen Hamilton’s Kate Highnam continued this line of discussion, walking us through several ways that machine learning helps with detecting cyber-espionage activities. As good as the tools are, she emphasized that—at least for the foreseeable future—there will continue to need to be a human in the loop when AI is used for detection of crimes.

Twenty public and private sector organisations in Singapore have already registered their interest. Gen AI can track transactions based on location, device and operating system, flagging any anomaly or behaviour that does not fit expected patterns, noted Mr Menon. Gen AI can also be used to provide personalised financial advice based on customers’ goals, risk profiles, income levels and spending habits. Large language models – a specific tool within generative AI (gen AI) – can process massive amounts of text data to predict human language patterns and create content. JP Morgan’s large language model can, for instance, review 12,000 commercial credit agreements in seconds, a task which previously consumed 360,000 hours of work each year. “AI models trained on incomplete or biased data can generate seemingly plausible but unsound predictions.

AI agents work independently, following instructions to use a variety of tools to complete tasks. ChatGPT doesn’t do anything on its own—humans must enter a question or prompt to get a response. The final highlight from Microsoft’s study is that 77% of leaders state that with AI skills, entry-level professionals will be given greater responsibilities. This clearly evidences that AI can give you the upper hand in your career, and actually propels you forward and enables faster professional development and growth than would be the case otherwise. It can unlock insights, automate processes and even anticipate cybersecurity threats.

• The DG refers to the regime for critical third parties (CTPs), which we discuss further below, and the use of stress tests to understand how AI models used for trading whether by banks or non-banks could interact with each other.
• IT teams spend over 16 hours per week updating or patching legacy systems, time that could be better spent on strategic genAI initiatives.
• It is also useful for those thinking about policy, confirming much of what we have said in our previous alerts but showing also that thinking on how government approaches the regulation of AI can and will likely evolve.

BlackRock’s latest 2024 Global Insurance Survey found that 91% of 410 respondents said they intend to increase their investments in private assets during the next two years. This has opened the door for emerging tech providers, such as Opto Investments, to develop a private markets platform for independent advisors. • Champion the integration of AI into business planning, including continuous forecasting, scenario planning and real-time performance monitoring, to enable their organizations to become more agile and data-driven. We all have our own views on how AI will impact the future of work and how organizations should adapt to this technology. However, we can all agree on the fact that if we want to make the most of AI, we cannot solely rely on our teams’ existing skill sets. Finance leaders need to ensure their teams are empowered to embrace AI and adopt a forward-looking learning mindset.

Finance leaders have a key role to play when it comes to promoting the potential benefits of AI and encouraging its integration. But to be successful, they need to embrace a new mandate, one that requires visionary thinking; a broader skill set; a more strategic, data-driven mindset; and a deep commitment to building a future-ready finance function. By fully embracing this new challenge, finance leaders can shape a future where finance is not merely a steward of resources but a strategic driver of business success. Many of us are excited about the incredible potential of AI, while also having some reservations about its real-world applications, limitations and risks. To address this discrepancy, finance leaders need to take concrete steps to define the role AI will play and strategically integrate it into their activities to help their teams maximize its benefits while mitigating risks.