Summary
"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."
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/775707 |
| Start date: | 01-06-2017 |
| End date: | 30-09-2017 |
| Total budget - Public funding: | 71 429,00 Euro - 50 000,00 Euro |
Cordis data
Original description
"The impact of cybercrime is a growing concern in a society that increasingly interacts online. In the EU the cost ofcybercrime 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."
Status
CLOSEDCall topic
SMEInst-13-2016-2017Update Date
27-10-2022
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