Opendata, web and dolomites

UNFRAUD

An advanced online anti-fraud software equipped with deep learning Artificial Intelligence thatcan face and detect, current fraudulent techniques and their continued evolution in a cost effective man

Total Cost €

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EC-Contrib. €

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Partnership

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Project "UNFRAUD" data sheet

The following table provides information about the project.

Coordinator
TXN SRL 

Organization address
address: VIA CALVARIO 5
city: ARIANO IRPINO
postcode: 83031
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Project website http://www.unfraud.com
 Total cost 71˙429 €
 EC max contribution 50˙000 € (70%)
 Programme 1. H2020-EU.3.7. (Secure societies - Protecting freedom and security of Europe and its citizens)
2. H2020-EU.2.3.1. (Mainstreaming SME support, especially through a dedicated instrument)
 Code Call H2020-SMEINST-1-2016-2017
 Funding Scheme SME-1
 Starting year 2017
 Duration (year-month-day) from 2017-06-01   to  2017-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TXN SRL IT (ARIANO IRPINO) coordinator 50˙000.00

Map

 Project objective

'The impact of cybercrime is a growing concern in a society that increasingly interacts online. In the EU the cost of cybercrime has reached €871 billion a year and fraudulent card transactions amounted to €1.27 billion. The high number of online frauds coupled with the low level of cybersecurity deters businesses, and in particular SMEs who may not be able to afford comprehensive anti-fraud services, from fully exploiting the potential of e-commerce. UNFRAUD is a software product that prevents potential online fraud scenarios by analysing previous and current fraudulent invents through deep learning artificial intelligence to tackle the new challenges that fraudsters devise. UNFRAUD’s algorithms are similar to one’s used by Google for self driving cars and facial recognition (i.e. deep AI that recognizes human errors, behaviours and surroundings) and through this deep learning it is able to detect 'fraudulent' behaviour. This makes UNFRAUD much more reliable as well as greatly reducing the cost of anti-fraud services, allowing companies to operate and grow safely. During the Phase 1 feasibility study the project will focus on identifying and securing the key partners required for commercialisation, establishing a sound business model and commercialization strategy, and planning a pilot test with a bank, big e-commerce, enterprise, telecommunication company and public administration in order to fully demonstrate and assess the products capabilities.'

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The information about "UNFRAUD" are provided by the European Opendata Portal: CORDIS opendata.

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