Cardo was selected last week as the winner of The Data Driven Competition, the first challenge launched by Fintech District and Cerved to identify new companies able to develop innovative products and services in the Fintech sector. The data and economic-financial information of Cerved on over 6 million Italian companies provided through API form the basis of the competition.
Cardo is a tech company offering institutional investors the opportunity to explore, analyze and create investment portfolios in the European markets of Invoice Trading, P2P Lending, and Real Estate Crowdfunding. Using the databases provided by Cerved, Atoka and Fintech platforms present in their machine learning and artificial intelligence algorithms, they are able to offer investors a technological platform to support investments in Alternative Finance. Cardo’s team has received a 10K euro prize and has the opportunity to collaborate with Cerved in launching their project on the market.
Together with that of Cardo, four other projects also made it to the finals, after being selected from over 45 proposals received for the call ranging from software developers, IT experts to Fintech startups and companies involved in digital transformation, interested in launching innovative and ambitious projects starting from Big Data.
The members of the selection board – Valerio Momoni (Cerved), Alessandro Geraldi (Cerved), Corrado Chiodi (Cerved), Gabriele Antonelli (SpazioDati), Filippo De Vita (Vodafone) and Alberto Adorini (Fabrick) – evaluated the projects according to three main criteria: valorization of the Cerved data assets, innovation of the Use Case and marketability of the Use Case.
The Data Driven Competition format, according to Valerio Momoni, Cerved’s Director of Marketing and Business Development, “encourages open innovation and the Corporate Venture Capital, which is growing at a rate of 20% per year and confirms its strong driving force for innovation. Today, mature companies innovate by collaborating with startups, they put their ideas and flexibility to good use and ensure their access to the markets “.
According to Alessandro Longoni, head of FD, the number of candidate projects is “the demonstration that many young Italian fintech companies are ready and want to collaborate with incumbents”. Satisfied with the success of the Data Driven Competition, Longoni hopes “to see the products born from this initiative created in partnership with Cerved arrive soon on the market”.
In addition to Cardo, Moneymour, Ecomate, Studiomapp and Two Hundred also made it to the finals.
Two Hundred Crowd, a company led by a group of experts in crowdfunding, finance, and marketing at the service of innovation, has created FAST (FundAbility Scoring Tool). This tool analyzes the ability of a company to attract investments from private companies by collecting and processing public or private data, such as the age of the company, competition, the experience and provenance of the work team and its online presence
StudioMapp, an innovative startup based in Ravenna specialized in ICT and artificial intelligence, has proposed a location intelligence service that provides an index of the quality of life of a neighborhood and displays all official information on a map. This tool is called Qirate and should help buyers find the best area in which to buy a house and sellers the best way in which to conclude the transaction faster.
The Moneymour project uses the data CERVED and Atoka (data of companies in the area of residence, real estate valuation, economic fabric, etc …) to increase the accuracy of credit scoring algorithms on individuals. Moneymour’s algorithms use machine learning techniques to measure the performance of companies.
The algorithm developed by Ecomate, a startup that helps European companies to grow using innovative digital tools, calculates the sustainability rating of companies, taking advantage of the contribution of experts and operators in the sector and the most advanced machine learning techniques. Ecomate uses the Cerved API to keep the rating updated in real time.
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