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Simon Brown: I'm chatting with Chetan Ramlall, director of quantitative research at Stanlib Systematic Solutions. Chetan, thank you for the time. The world has been around AI since ChatGpt came out two and a half years ago. Honestly, we had different kinds long before that. But let's look at machine learning and why it's important for fund managers to understand and engage in it.
Chetan Ramlal: Yes, Simon. First of all, thank you for welcoming me. It's a really fascinating topic and I think AI has really taken the world into a storm in the last few years. There is a lot of confusion and misunderstanding between AI and machine learning, so I think it's important to understand what AI is. Artificial intelligence can be considered a broad family, like the ability of computers and machines to learn, reason and self-correct.
Within this broad family of AI, machine learning is a subset. It's like the name “learning,” the ability for the system to actually automatically learn and improve without being explicitly programmed. What really matters is that what it does, fund managers and basically everyone in multiple fields is unlocking a whole new set of tools.
Simon Brown: A field that will definitely touch you left, right and center. Can they enhance decision-making in the investment process, and perhaps even more importantly, will it help reduce the risk of investment?
Chetan Ramlal: Yes, totally. Like I said, it takes us to this new frontier. There, for the first time in decades and for the first time in history, there is no need to rely on traditional data sets, for example.
Therefore, in the investment cases of a company's balance sheet or profit statements, or more broadly, in financial statements, all kinds of machine learning can use something like an alternative data set. So, something interesting like satellite images, or drone footage, for example, would inform certain decisions that could not be possible ten years ago.
It is only through these new tools that we can take advantage of. This allows us to do more of the available data from both an opportunity perspective and risk management.
Simon Brown: It's fair to say that there's been a lot of talk about jobs at risk of artificial intelligence over the last few years. I imagine that there are sectors in the investment manager's space that may lose some jobs. I don't know if that's an investment manager or not. As you explain it, it's a tool rather than a complete takeover.
Chetan Ramlal: that's it. I think the ideas and discussions about job displacement are fair. I think it's gaining more traction. You can't keep your head in the sand. This technology will certainly disrupt the industry, and it has this disruptive effect, especially in the short term.
But I think what happens when you wear a long-term hat from the medium is to rethink, job display, rethink, and rethink jobs where new types of roles are at the forefront, as mentioned specifically in the field of investment management.
These are really tools, tools to increase what we bring to the table as human beings and as managers. I don't think that's that much of a problem, as wherever our creativity and skills are applied as humans, the optimal outcome is achieved only by combining both humans and machines. So I think we've come a very long way from getting the machines to run shows.
Simon Brown: I hope it's a very long journey. But I like your point. It means someone was at work. My personal experience with artificial intelligence lifts up some of those more repetitive, perhaps managed tasks, frankly, in the case of an investment manager, freeing me to make those decisions and quantify the risks. It's actually strengthened. There are some job changes, but overall the process is strengthened.
Chetan Ramlal: Yes, that's all. If you take anything, everyone in financial services will understand the power of Excel. I think the world's systems run Excel and Spreadsheets. However, given the risks and inherent risks when running some of these spreadsheet models, these new tools now incorporate artificial intelligence, allowing machine learning to automate many tasks, which in itself plays a major role in risk mitigation.
As you correctly point out, it allows humans to express their creativity and not worry about repeated simple tasks, but now they actually focus on vision and larger picture-type perspectives.
Simon Brown: Yes, AI is in a way doing the grunt work and hasn't made the final investment decision.
I imagine this as a stunlib, especially a systematic solution, that must be incorporated and seeped into the process. It's like it's in every respect, sometimes even in the foreground.
Chetan Ramlal: We all in this industry believe this technology is still at the bottom of the relatively new J curve. But with every passing month, development is almost rapid. Like everything, care must be taken when it comes to how new technology is deployed. And especially with our team, we are very careful about how we use it.
At this point, it can help you unlock values such as work automation, summarise research reports, for example coding and programming aspects, and explore ways to use AI to optimize coding. So there are many ways to do it – even deploying a specific investment process into [the] Cloud that enables better calculations.
There are so many different paths that you can see. But approach carefully while everything takes it into your path. The important thing is that people think along these lines and are open to embracing these technologies. The world we are in is very different, even ten years ago. The amount of data you can use at your disposal is important, and that means you need to change and evolve the tools you use. That's really the most important point.
Simon Brown: It's a hug. But I also like your point about this being J-Curve. We hardly understand this very early on and think about where it will end up in the end, 5, 10, certainly not sure what will happen 20/30 years from now.
I'll leave it there. Chetan Ramlall, head of quantitative research at Stanlib Systematic Solutions, thanked the time.
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