Featurespace, a provider of enterprise financial crime prevention technology for fraud and anti-money laundering, has filed two global patents that support new levels of customer protection in the financial industry.
The first patent is for Featurespace’s Automated Deep Behavioural Networks for the card and payments industry. Automated Deep Behavioral Networks are a deep neural network architecture of connections and updates that recognise and prevent fraud cases. Deep neural networks have revolutionised areas such as image recognition and text understanding by creating specific architectures (connections and weights) designed to extract meaning from the underlying data presented to the network.
Automated Deep Behavioral Networks solves the problem of finding a neural net architecture that extracts meaning from transaction sequences producing a much higher distinction between genuine and fraudulent transactions, according to the official press release.
Featurespace’s second patent is for Behavioural Anomaly Score, which identifies anomalies in individual customer behaviour without having any prior knowledge of contextual high-risk behaviour. This technology amplifies the ability to identify when a person’s behaviour is out of character without any labelled data. Through a Behavioral Anomaly Score, companies and financial institutions can see the exact point at which a person’s behaviour has changed with greater precision and from there, construct more complex models for change detection, the press release adds.
Full public patent applications have been filed in the US, UK, EU, and Patent Cooperation Treaty (PCT).
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