Every business leader wants to command a data-driven enterprise. Considering the sheer volume of data available to us today, it seems simple enough. However, for those of us old enough to remember floppy disks, it can be staggering to consider that data is now measured in petabytes, exabytes and zettabytes (a petabyte is 1000 terabytes of data, or 343 million floppy disks). Even with the breadth of data available, the ability to turn that data into data-driven insights can be limited by how easily it is able to use and be interpreted. For asset managers, where every piece of information matters, making data simple to use, interpret, and action is made even easier with no-code artificial intelligence.
What is no-code AI?
The New York Times recently wrote about the phenomenon of no-code AI and put it simply: “Just as clickable icons have replaced obscure programming commands on home computers, new no-code platforms replace programming languages with simple and familiar web interfaces. And a wave of start-ups is bringing the power of [AI] to nontechnical people in visual, textual and audio domains.”
No-code AI removes limitations for asset managers
Data can be noisy, unstructured, and difficult to map. Think of popular types of alternative data like credit card transactions, weather, social media info, and ESG. All may be valuable in their own right – and of interest to an investment manager – but are not easily comparable to one another. To glean insights from the glut of data available, asset managers are going all-in on data scientists and machine learning engineers, competing with tech stalwarts like Google, Microsoft and Amazon, for a limited pool of talent. No-code systems allow investment managers to get the same data-driven results, without needing to know anything about programming.
Utilising a no-code system, asset managers can simply “plug-and-play”, using data to find trends in capital markets. Our machine learning platform – Boosted Insights – is no-code and ready “out-of-the-box”. In minutes, asset managers can use their finance expertise to build a machine learning equity model to test any investment hypothesis. Of course, honing these models can take more time, and our team of in-house data scientists, ML engineers and dedicated customer success team members are on hand to help institutional investors reach their portfolio goals.
Another benefit of adopting a no-code system is that investment managers can hit the ground running with AI. Without having to focus on tedious coding or data cleansing tasks, an asset manager is free to use Boosted Insights to focus on what they do best – manage portfolios. Easy wins with AI through no-code platforms can help build out an organization’s AI capacity and teams, furthering success through artificial intelligence.
No-code doesn’t mean no power
Anyone looking to adopt a no-code solution should not sacrifice performance to do so. Though Boosted Insights is simple for someone with finance expertise to intuitively understand, it also has extremely complex and powerful data cleansing, algorithms and portfolio analysis at its core.
While we have many clients that have never programmed one line of code in their life (save perhaps for some APIs our customer success team has helped them implement), we also have a group of quant power users – folks that are more than capable of creating their own machine learning programs and often have. These teams still recognize the value in a no-code solution, which works as an easy and efficient way for them to test theories and the value of alternative data. We have also seen that these quantitative users value the uncorrelated and non-orthogonal signals Boosted Insights brings to their asset management.
Takeaways
Making use of a no-code AI tool is a good way for investment managers – those with and without programming or data science experience – to implement fast wins with artificial intelligence. But no-code doesn’t mean no commitment. Using an AI tool requires time and effort spent honing models into something that matches the investment manager’s investment mandate. We have seen institutional investors get up and running, trading live money in as little as a few weeks, but there is significant back and forth between them and our customer success teams in that time period. Also, for these institutional investors to feel assured using no-code tools, they should know that they are backed by in-house teams of data scientists and machine learning engineers. If you want to learn more about how you can implement no-code AI to enhance your investment process, please reach out to us here.
