Crowd finance data intelligence specialists, Crowdsurfer, today announce the global public release of their machine intelligence system, Daisy, as part of the latest updates to Crowdsurfer Pro.
Daisy, Crowdsurfer’s Digital Assistive Intelligence, has been created to provide answers and insights about the fast growing crowd finance industry, where it is unfeasible or impractical for humans to provide high-quality analysis.
Daisy utilises advanced natural language processing and sophisticated concept extraction heuristics to analyse the Crowdsurfer database. These abilities allow her to extract significant concepts from the unstructured textual data attached to millions of individual raises, to a greater extent than ever before. This objective identification of campaigns’ important concepts, based on her ‘understanding’ of what the crowd is saying, helps derive the most information possible from the text and overcomes inconsistent mapping of the industry.
Daisy’s capabilities are a first for crowd finance globally and enable users to quickly navigate the universe, explore market trends and unlock a wealth of information previously hidden in the unstructured data of the campaigns.
This is also a milestone in Crowdsurfer’s goal to use world-class data engineering and analytics to remove barriers to engagement with the fast-growing, diverse crowd finance industry.
Dr Simon Fothergill, Crowdsurfer’s Head of Data Analytics, said:
“This release of Crowdsurfer Pro includes the first features powered by Daisy, Crowdsurfer’s Digital Assistive Intelligence who is an expert in crowd finance. The current artificial intelligence (AI) revolution is based on the availability of powerful hardware, better algorithms and large amounts of data. Daisy automatically provides the diligent, objective analysis of real data that users require to confidently engage with crowd finance.
Daisy’s abilities to read crowd finance campaigns allow us to get an unprecedented feel for how the crowd finance community is campaigning. We now show the most important concepts behind the campaigns discovered by the user. This makes understanding how people are campaigning more efficient, when browsing the market or refining filters. Greater efficiency increases users’ confidence in whether they have found what they were looking for and not missed an opportunity.
Daisy will power more new features in the future, as she makes it easier for people to understand crowd finance. The high quality, personalised analysis, she generates is part of what makes Crowdsurfer an iconic and trustworthy service provider. She will help people question, discover and succeed in the challenges of fundraising, investing or reporting on the market.“
Emily Mackay, CEO of Crowdsurfer, said:
“With the introduction of features powered by Daisy we bring together two of the most powerful advances in recent technology: highly distributed online finance and machine intelligence. For the crowd finance economy to grow it needs to engender deep-rooted trust. By bringing a new level of understanding and interaction to the global crowd finance economy we can support transparency and understanding of both the concept of marketplace finance and individual platforms.”
Headquartered in Cambridge, UK. Crowdsurfer is building the world’s trusted, data intelligence service for the global crowd finance phenomenon. Crowdsurfer provides those who seek to participate in or serve the industry with the market intelligence they need to be successful. We do it because we believe that crowd finance has the power to make the world a better place.
Using big data engineering and machine learning, the company maps, augments and interprets the information provided by hundreds of crowd finance platforms spanning equity, debt (P2P), rewards, donations and other forms of crowd-based financial transactions.
Fundraisers, investors and others tracking the market are all taking advantage of the service that offers the most detailed and comprehensive view of the crowd finance markets.
Originally published on the 6th of December 2016