It's no secret that AI is taking over the investment industry. Investment managers themselves are acutely aware that technological change is coming: In a study by Accenture, 95% of respondents said that an asset manager's technology, data and digital capabilities will be differentiators in 2025. But how do we get from here to there?
AI has been notoriously difficult to implement within financial services, and more specifically in the realm of investing. One main reason is that compared to the technology industry, finance is famously proprietary. The world's financial AI experts tend to be deeply embedded into single organizations for years, if not decades, preventing the cross-collaboration and open-source mentality that is prevalent in other industries.
The other reason AI is so difficult to properly implement is that there are really two problems here, not one. The first problem is the one you expect: getting a signal. But the second problem is even trickier: knowing when, how and why to trust that signal and what all of that means for your trading strategy.
In another recent report on the topic, Deloitte suggests that asset managers should start by focusing on quick wins, embracing strategic collaborations and partnerships and clearly defining their AI strategy.
There's something to that. When implementing AI, the world's asset managers are tasked with crafting the right balance of in-house expertise and external support. In doing so, they need to understand what is best built by their own team and when it's best to tap into mutualized innovations.
The following are three key considerations you should take when exploring what you may need to develop internally and where you may need a new tool in your arsenal.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/05/04/three-things-to-consider-when-bringing-ai-to-asset-management/?sh=5d6d844e232b
AI has been notoriously difficult to implement within financial services, and more specifically in the realm of investing. One main reason is that compared to the technology industry, finance is famously proprietary. The world's financial AI experts tend to be deeply embedded into single organizations for years, if not decades, preventing the cross-collaboration and open-source mentality that is prevalent in other industries.
The other reason AI is so difficult to properly implement is that there are really two problems here, not one. The first problem is the one you expect: getting a signal. But the second problem is even trickier: knowing when, how and why to trust that signal and what all of that means for your trading strategy.
In another recent report on the topic, Deloitte suggests that asset managers should start by focusing on quick wins, embracing strategic collaborations and partnerships and clearly defining their AI strategy.
There's something to that. When implementing AI, the world's asset managers are tasked with crafting the right balance of in-house expertise and external support. In doing so, they need to understand what is best built by their own team and when it's best to tap into mutualized innovations.
The following are three key considerations you should take when exploring what you may need to develop internally and where you may need a new tool in your arsenal.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/05/04/three-things-to-consider-when-bringing-ai-to-asset-management/?sh=5d6d844e232b
