Our mission at Intrinio is to power a generation of applications that will fundamentally change the way our broken financial system works. Intrinio data feeds form the basis of everything from large enterprise business reporting applications to startup fintech apps. It’s rewarding to see our product come to life at the hands of today’s most innovative developers building powerful things.
We’re lucky to be in a business where we grow together with our customers, and we’re proud to show off their hard work. Each blog in this series will highlight a customer that has leveraged our financial data feeds to build something incredible.
Meet FinScience – a data-driven investment platform that collects, weights, and analyzes signals from the digital ecosystem to help investors better assess risk and opportunities. We talked to Angelo Ovidi, Principal at FinScience, about the inspiration behind the company, FinScience’s biggest challenges, and partnering with Intrinio.
FinScience is a data-driven fintech company founded in 2017 by Google’s former senior managers and alternative data experts, who have combined their digital and financial expertise. FinScience thus originates from this merger between the world of finance and the world of data science. It aims to discover weak signals emerging from big data and capable of influencing financial markets at a very early stage. This solves the need to identify blind spots in assessing risk and opportunities in investments.
FinScience leverages 3rdPlace’s experiences concerning data governance, data modeling, and data platforms solutions. These are further enriched through the tech role in the European consortium SSIX (Horizon 2020 program) focused on the building of a social sentiment for financial purposes. FinScience was the only Italian startup selected by Silicon Republic among the 25 European deep-tech startups to watch in 2019.
I studied computer engineering in Pisa and then graduated in business management at Colorado Technical University. I was a pioneer in Italian web development in the early 1990s and continued my activity in many European countries and in the US, covering various complex platform management roles and participating in the discovery and dissemination of hardware and software technologies such as IBM Blade Centers, distributed file systems, open-source operating systems, parallel computing, and Big Data.
My work heavily focused on the field of cybersecurity and in particular on threat intelligence systems with an AI approach. Such a large variety of experiences provided me with a holistic and trans-disciplinary vision of the IT world and pushed me to entrepreneurial activity in the field of modular data centers and nanotechnologies.
Already, in 2009, we realized how search queries on the main engines can not only provide a glimpse of what people around the world are thinking or doing at any given moment, but also predict economic trends, based on purchase intent, related to entire sectors. Hence the idea of creating proprietary indexes that could measure the diffusion of information and its impact on financial markets.
Our mission has evolved in providing investors with augmented analytics solutions to collect, weight, and analyze signals from the digital ecosystem, even weak and emerging ones, as well as integrate them into their own financial market views. Today’s digital solutions enable investors to understand in a short time how a certain phenomenon is evolving – without leaving their office chairs. The problem has always been to spot the right information in noisy data. AI is the key.
In the beginning, the biggest challenge was to propose top-notch solutions, based on a strong web and digital vision, to the financial investments sector, which is very focused on methods and activities consolidated over the years. This is why we have built a team of professionals that integrates financial expertise with new approaches related to digital mindset and skills. This choice is already paying off today, but we are sure it will pay off even more in the future.
The confidence and trust gained in the banking sector, in particular in helping manage cross-industry investment portfolios based on emerging new trends. Also, the opportunity we had to collaborate with stock markets in the US, receiving praise for our skills and professionalism.
FinScience uses a proprietary methodology to collect alternative data from the digital ecosystem in addition to human analysis on the data-driven results. We call this approach augmented analytics.
The key factor is to focus on the quality of data using machine learning and AI with the ability to integrate different data sources. We developed a large range of KPIs spanning from the popularity of an entity on the web (company, product, manager, topic, etc.), sentiment, correlation, and volatility, to end with a deep analysis of the strength and life span of a signal.
We also have a strong presence on ESG evaluation, based on both alternative and traditional data shared by the company itself. We tackle thematics as correlation between ESG scores and financial impact of ESG driven decision or the greenwashing phenomenon.
We have a strong tie with universities and pure research team efforts, including world-famous institutions such as Scuola Superiore Sant'Anna in Pisa, Politecnico di Milano, and many others.
FinScience, using alternative data to find new investment ideas, needs to compare its proprietary innovative metrics with financial data and insights to help our customers in their analysis. For that reason, FinScience decided to work with Intrinio to collect financial data on American and Italian companies. We chose Intrinio for its reliable customer support and its competitive price.
Intrinio provides a large variety of good quality financial data that is valuable for us to mix digital with quantitative datasets, at a competitive price. They are very supportive and able to collaborate on technical solutions whenever problems arise, a quality that is very rare in data providers.
Interested in working with Intrinio? Visit intrinio.com to learn more.