InvestSuite is an international B2B WealthTech company operating across Europe. InvestSuite helps financial institutions modernise and extend their wealth management product range with a suite of white-label solutions. Its team of experts operate across machine learning, design, human insights and wealth management.
The company seeks to maximise the enormous growth opportunities created by a combination of changing customer expectations, technological evolutions and the emerging new ecosystem of financial institutions.
InvestSuite’s range of solutions are aimed at many financial institutions such as online brokers, retail banks, private banks, asset managers, pension funds or insurance companies.
StoryTeller, one of InvestSuite’s products, was developed in 2020 and is a worldwide first new way of telling the story of the performance of retail clients portfolios. Using five unique engines, StoryTeller helps to explain portfolio performance in an understandable and transparent way to retail investors. It provides an in-depth view of the returns over a chosen time period and illustrates events that have impacted the performance and to what degree.
InvestSuite wanted to further refine the reports by making them relevant in terms of real-time actuality by using current news stories to give the analysis more clarity. This will result in increased customer satisfaction, financial knowledge and engagement.
InvestSuite asked Radix to develop a news article ranking engine that could be embedded in the StoryTeller solution and would rank news articles about the performance of specific stocks. The ranking tool, based on a number of criteria including relevance, content and time, needed to be extremely accurate. The top ranked article was required to give the most information and therefore the most value in terms of helping to analyse portfolio performance.
The project started with a number of known challenges. Radix had to find a way to rank articles in a way that mimics how humans would be reading them, which is not only hard to define but also hard to translate into criteria for the solution.
Radix developed a first sprint of the solution based on data labelling. It allowed Radix and InvestSuite to quickly test the article rankings and to interact with the different labels.
To ensure maximum relevance of selected articles, Radix used a technique called ‘keywords clustering’, as opposed to an ‘embeddings’ approach. Embeddings use semantics to link keywords together by relevance.
With this technique, an article containing a lot of keywords has a high chance of getting picked up, but might not be entirely relevant. Having a related article was not good enough, as the solution needed to select only the most relevant articles for each reporting.
This is why Radix used the clustering method. This technique maps a number of topics through keyword clusters. Articles are then ranked based on how many specific concepts (topics) and clusters are mentioned in an article, which increases the ranking’s relevance.
During the development phase Radix also suggested going from text summarisation for the reports to the more user friendly method of question answering. Instead of the AI summarising the text of an article, it can actually answer questions in natural, flowing language. For example: ‘why did my Apple stock increase by x percent?’
If an article mentions the right keywords but isn’t closely related to the topic, the AI is asked “why did this stock go up/down?”. Based on the information, the results can be further fine tuned.
InvestSuite is currently in the process of implementing Radix’s news article selection engine in the StoryTeller solution and intends to improve the performance of the engine over time with trained datasets.