71% Of Employers Prefer AI Skills Above Experience In 2024

AI adoption by finance professionals has increased 21 percentage points in the past year with 58% using the technology in 2024, according to a Gartner survey. “In the first phase of deploying agents, you need to put humans in the loop all the time,” says UiPath CEO Daniel Dines. During a recent webinar on AI agents hosted by my company, Centric Consulting, we asked attendees what they thought AI agents were. Nearly 20% responded with “chatbots.” Chatbots are reliant on user input, whereas agents use AI and natural language processing. AI agents can have a conversational interface—just like a chatbot—but it’s not a requirement.

AI is changing the work of finance professionals by automating repetitive operations, improving fraud detection, offering real-time insights and modernizing audit processes. And beyond the automation of routine tasks, AI is transforming the way finance professionals work, allowing them to focus on more strategic, impactful work. Like any tool, AI agents aren’t going to magically solve every business problem.

The stakes are high—both in terms of the opportunities presented by AI adoption and the risks of inaction. While employers are actively seeking professionals who can bring their AI expertise to enable greater ROI, streamline processes, and remain competitive, this is your opportunity to future-proof your career and be part of the innovation. Earlier this year, we offered advice for where to begin applying generative AI within your organization. But no matter where you start, we believe there are two foundationally critical steps to take before you can calculate revenue from generative AI. You must get your data in order, and you must modernize your infrastructure. When our senior finance manager, Nicole Houts, saw a live presentation of a customer using the SnapLogic Agent Creator to automate manual data processes, a proverbial light bulb appeared.

How to get to revenue with generative AI

They must anticipate compliance challenges in AI deployments and prepare today for new regulatory headwinds. The future of AI is potentially boundless, as it was noted that today’s AI models “are the worst you’ll ever see” when compared with what’s to come. One rough benchmark to strive for is AI freeing up 90% of human trader and technologist time, so they can focus on the most important 10% of their work.

The network will replace Elevandi – the company limited by guarantee set up by MAS four years ago to organise the Singapore FinTech Festival. Mr Menon previously described the new entity as “Elevandi on steroids”, with an expanded reach beyond the forums business. GFTN forums will aim to address the pros and cons of various AI models and strengthen governance frameworks around AI, among other areas. If quantum technologies take off, the coupling of AI and quantum computing could unlock huge opportunities, as well as unprecedented security challenges, said Mr Menon. There is also a need to minimise the “black box syndrome”, where the massive amount of data, complexity of algorithms and dynamic nature of AI systems make results difficult to interpret and explain, he added.

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

Flexible data architecture enables the seamless connection of data and systems that don’t easily connect (e.g., on-premises and cloud deployments). This is critical not only for adding new genAI tools to your technology stack, but also for accessing and combining data from a diverse range of inputs. This coordination can significantly lower the total cost of ownership of AI tools, speed up the development process, and provide the ability to scale. Modern organizations manage mountains of data from a variety of disparate applications (CRM, ERP, etc.) and data sources (web servers, databases, APIs, etc.). Centralizing this information is critical to controlling how it flows, how it’s transformed, and how to keep it secure. The generative AI application allowed the finance department to reduce the time spent on month-end closing by 30% and decrease manual data review and reconciliation by 90%.

“Innovation is happening faster than you can imagine or adapt to, and large organizations are racing against time to move from data to value to insights to action,” notes Abhas Ricky, chief strategy officer at Cloudera, a hybrid data platform. You can foun additiona information about ai customer service and artificial intelligence and NLP. Let’s consider an AI-powered security solution that can detect and respond to cyberattacks in real time. Senior executives, especially those in ChatGPT App business units or with skill sets outside of cybersecurity, might not understand AI’s critical role in security teams. Emphasize the financial benefits of AI, including its potential to drive increased revenue, reduce costs and enhance operational efficiency. To strengthen the case, it’s essential to quantify the ROI of AI initiatives by using concrete data and performance metrics.

It is possible that an AI miscalculates the risk of a position and the end client is erroneously over-exposed to the market. In our previous alert we mentioned a joint letter from the Prudential Regulation Authority (PRA) and Financial Conduct Authority (FCA) to the UK Government on their strategic approach to artificial intelligence (AI) and machine learning. The letter followed the UK Government’s publication of its pro-innovation strategy, in February of this year. The adoption of multi-sig wallets has seen significant growth, particularly with platforms like Safe. Initially designed as a multi-sig wallet, Safe has evolved into a comprehensive smart contract wallet, offering enhanced security and flexibility. This transition allows for more complex transaction logic and integration with decentralized applications, making it a robust solution for managing crypto assets.

“AI is really good for generalizing our directions,” she said, “but at the end of the day, we have to make sure that we are very clear with our assumptions.” She went on to note, however, that many firms are also using AI to mitigate the external risks they face from cyber-attack (37%), fraud (33%) and money laundering (20%). For example, payment systems have long used machine learning automatically to block suspicious payments – and one card scheme is this year upgrading its fraud detection system using a foundation model trained on a purported one trillion data points.

The next statistic states that 71% of business leaders would give preference to a candidate with less experience, as long as they had AI skills. This essentially means that AI literacy ai in finance examples is the new level of digital literacy we should all be aspiring to. Listing Word or Excel on your resume within your skills section, although useful, is becoming outdated.

16% of respondents are using AI for credit risk assessment, and a further 19% are planning to do so over the next three years. Meanwhile, 11% are using it for algorithmic trading, with a further 9% planning to do so in the next three years. And 4% of firms are already using AI for capital management, and a further 10% are planning to use it in the next three years. As the potential of autonomous agents becomes more tangible, crypto is emerging as a promising infrastructure to enable AI agents to securely and independently manage funds, potentially overcoming the limitations of traditional finance systems.

Additionally, sharing success stories from other companies that have achieved substantial financial gains through AI can further demonstrate its value. Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience.

ROI-Focused Executives

As the finance profession embraces these AI technologies, adjustments must be made. Professionals must focus on developing the necessary skills to use AI properly, and organizations and finance leaders must ensure they are providing the proper road maps, tools and opportunities for their professionals. While integrating AI agents into your organization can be challenging—there’s a lot of strategy to consider, important governance to put in place and team members to involve—the potential benefits are enormous.

Once the agent is live, actively monitor inputs and outputs during the initial use phase. This helps provide transparency and explainability, creating an audit trail so you can have confidence in the technology. As you scale, you can transition out to passive monitoring to flag anomalies.

For example, Walmart’s senior vice president and head of investor relations, Stephanie Wissink, recently shared how the retail giant has used large language models to automate data transformation projects related to supply chain operations. Walmart calculated that this shift alone made transformations 100 times more productive. Much like our San Jose event last month, the venue was packed to the rafters with Ars readers eager for knowledge (and perhaps some free drinks, which is definitely why I was there!). A bit over 200 people were eventually herded into one of the conference spaces in the venue’s upper floors, and Ars Editor-in-Chief Ken Fisher hopped on stage to take us in. She observed that potentially more significant use cases from a financial stability perspective are emerging.

Still, there is reason to be cautious about any software provider claiming to have a proprietary code when most wealthtech firms have access to the same data, leading tech providers said. “You should be raising a hedge fund and seeing if you can beat Ray Dalio.” Let’s all remember what happened with the Crowdstrike outage earlier this year, crashing millions of Windows PCs — including systems run by every major airline. According to Microsoft, the lagging response from a particular airline was caused by its failure to modernize its IT infrastructure.

• “In the first phase of deploying agents, you need to put humans in the loop all the time,” says UiPath CEO Daniel Dines.
• Joe Ariganello is the VP of Product Marketing at MixMode, where he works with cutting-edge AI technology.
• 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.
• 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.

InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Streamlining data and tackling technical debt can ensure that your organization is ready to harness the full potential of generative AI, enabling you to unlock efficiencies, reduce costs, and ultimately drive revenue growth. By taking strategic steps now, your organization can position itself not only to participate in the benefits of generative AI, but to lead the charge in this new era of AI-driven innovation. Climate technology is another area the financial industry is focusing on. Gprnt, MAS’ digital platform for environmental, social and governance reporting and data, released tools on Nov 6 to help businesses with their sustainability reporting and enable them to navigate related solutions.

Indeed the productivity implications of generative AI are huge, prompting McKinsey to assert that the technology could add trillions of dollars in value to the global economy. Conferences are one of the network’s four business lines, along with advisory and research services, digital platform services for firms, and an investment fund for technology start-ups. To address these challenges, several approaches to key management for AI agents have emerged, each with its own strengths and trade-offs. In conventional finance, regulations like Know Your Customer (KYC) and Anti-Money Laundering (AML) laws are critical to ensure transparency, accountability, and ethical use of funds. These regulations, however, assume that a human is responsible for any financial account and has passed relevant identity and background checks. But in the case of AI agents, no single individual or legal entity may actually control the account directly, creating regulatory gray areas.

From online banking systems to investment accounts, each financial service is built on the assumption that there’s an accountable, legally recognized human or corporate entity behind every transaction. An AI agent operating independently doesn’t easily fit into these frameworks, making compliance both technically challenging and legally uncertain. Thus, for AI-driven finance to work on a practical level, a solution that sidesteps the limitations of traditional finance while addressing security and regulatory concerns is necessary. Emphasizing the role of AI in mitigating risks is crucial, as it can help address challenges like cybersecurity threats, fraud and supply chain disruptions. AI-powered risk management solutions are proactive, enabling businesses to stay ahead of potential issues. Demonstrate how, by leveraging AI, organizations can identify and address risks early, preventing them from escalating into more serious problems.

While these approaches make AI agents more viable in finance, regulatory questions remain. Agencies will need assurances of accountability and transparency, and the crypto industry will need to provide frameworks that protect against both security risks and misuse. For those interested in pioneering this space, exploring hybrid strategies and collaborating with regulatory bodies will be essential to bring autonomous AI agents to maturity. Furthermore, blockchain transparency and immutability offer a unique advantage. Every transaction executed by the AI is recorded on-chain, creating an auditable trail of activity that provides transparency and accountability—features highly valued by both investors and regulators. This makes crypto wallets a suitable infrastructure for autonomous agents in the finance world, provided that certain security and control measures are in place.

Finally, as with any change management project, finance leaders will know that open and transparent communication is essential to create and maintain trust. But leaders can instead choose to position the technology as a tool for accelerating market growth or super augmenting your most valuable asset—your ChatGPT people. But it will also create new opportunities—although these new jobs will take some time to emerge. Leaders must figure out how to create workers of the future who are adept at using AI to solve problems and innovate. The tool reduced manual labor by 82% and increased accuracy to nearly 100%.