In a society with ever-increasing levels of online interaction, the impact of cybercrime is a growing concern. In the EU, the cost of cybercrime has reached €871 billion a year, fraudulent card transactions amount to €1.27 billion and the International Monetary Fund and World Bank estimate that between €1.5-2.5 trillion is laundered globally each year, meaning public funds forego around €1 trillion in tax revenue annually.
High levels of online fraud coupled with low levels of cybersecurity deters businesses from fully exploiting the potential of e-commerce, in particular SMEs who may not be able to afford comprehensive anti-fraud services. The current cybersecurity approach to fraud isn’t flexible or efficient as it fails to recognise complex fraud, generates false positives, and relies on human intervention for final detection.
UNFRAUD is a cybersecurity software product that prevents potential online fraud scenarios by analysing both previous and current fraudulent events through deep learning artificial intelligence to tackle the new challenges posed by fraudsters. UNFRAUD’s algorithms are similar to those developed for self driving cars and facial recognition (i.e. deep AI that recognizes human errors, behaviours and surroundings) and through this is able to detect fraudulent behaviour. Compared to competitors, UNFRAUD software reduces the cost of anti-fraud services by up to 40%, reduces false positives, analyses the kind of complex data involved in money laundering and safeguards banks’ welfare, allowing companies to operate and grow safely.