Daniele Grassi, CEO and co-founder, Axyon AI, explains that in today's competitive and ever-changing market, firms need to make decisions quickly and accurately.
Today’s markets are fiercely competitive and move at breakneck speeds. The digital revolution has given way to an ‘always on’ economy that demands businesses be agile and make rapid decisions if they are to stay ahead of the curve. While much has changed, decision-making remains a constant factor in the making or breaking of a successful firm. The difference is that today, those decisions come with a frequency and complexity unheard of a decade ago. A recent Gartner survey found that 65% of business decisions are more complex, involving more choices or stakeholders than two years ago.
Decisions, particularly when it comes to investments and the management of funds and assets, need to be adaptive, risk-conscious, and instantly actionable. That may have been possible for a small team of asset managers and fund selectors to handle before the ‘always on’ economy emerged, but today it involves gathering insights from an ocean of data, aligning those insights with business objectives, and trying to predict the outcome in order to assign a level of risk in line with the company’s overall risk posture. Even if this process could be achieved manually to a satisfactory level, the time it would take to game the decision and chart any potential outcomes would render the decision itself obsolete – the opportunity would have passed, competitors would have made their moves, and the market will have changed.
To gain a competitive edge in the digital economy, more hands on deck isn’t the answer.
Instead, firms are looking to artificial intelligence (AI) and the real-time analysis of market data in order to maximise their portfolio performance and alpha generation. Let’s look at the role deep learning predictive solutions will play in giving firms the competitive edge they need to not only survive but thrive in an area where time itself is a currency.
Processing data, identifying patterns and predicting market outcomesAI has come on leaps and bounds in recent years, allowing investors to make streamlined, objective decisions while removing the influence of human bias and ‘group think’ from the equation. Vast volumes of data can be interrogated, cross-referencing at a granular level to identify non-linear patterns and outcomes that even the most experienced will likely overlook.
Without AI, portfolio managers would have to spend countless hours manually researching hundreds of data sources, making the process time-consuming, prone to error, and almost impossible to optimise. AI modelling can do much of the heavy lifting, allowing asset managers and fund selectors to get on with the important work of actually making the decision.
What next for ChatGPT, Bard and other generative AI tools?The likes of ChatGPT and Bard have transformed how some businesses operate and interact with customers, but this only scratches the surface of what generative AI can achieve. As with all forms of predictive modelling, the more data that goes in, the better the results. So, high-quality data to train models is an essential element of leveraging the technology. High-performance computing (HPC) will be invaluable in this endeavour, allowing models to be trained faster and with greater accuracy.
In the future, we can expect AI to have a profound influence on personalised investment recommendations as the technology continues to evolve. For example, through leveraging advanced conversational capabilities, we can expect these AI platforms to provide more advanced, personalised investment recommendations dependent on individual financial goals, risk tolerance and market conditions. Given that these platforms are in the early stages of their development, more accurate and well-informed recommendations will be provided moving forward as the capability of AI to analyse huge datasets continues to improve through greater investment in the technology.
However, firms will need to be mindful of how they pursue AI development from a compliance and regulation perspective, ensuring information is gathered ethically and from trustworthy sources. ChatGPT, for instance, is already facing a backlash when it comes to privacy regulations and protecting users’ data and conversations. To overcome this challenge, firms will need to establish their own guidelines and standards for the development of AI and its deployment within their business while ensuring they monitor for potentially biased outcomes.
In today’s ‘always on’ economy, decision-making is more important than ever to the success of any business, but it’s also how businesses make those decisions that will be critical. AI has the potential to give firms a step up by doing the heavy lifting on market analysis and more, enabling asset managers to focus on making the right choices that can maximise portfolio performance for investors.
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