• 2 global patents enable new levels of model performance in the industry
• Patent 1: Brings significant uplift in transaction fraud use cases
• Patent 2: Identifies behavioral anomalies within individual customer activity
• Both are unique weapons in the fight against fraud and financial crime


Cambridge, Atlanta, Singapore, July 12, 2021 – Featurespace, the leading provider of Enterprise Financial Crime prevention technology for fraud and anti-money laundering, has filed two global patents that will enable new levels of customer protection in the financial industry.

The first patent is for Featurespace’s Automated Deep Behavioral Networks for the card and payments industry. Automated Deep Behavioral Networks are a deep neural network architecture of connections and updates that recognize and prevent significantly more fraud cases. Deep neural networks have revolutionized 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.

“Financial institutions around the world are experiencing fraud and account takeover at unprecedented levels, with some reports estimating this number at more than $40 billion last year,” said Dave Excell, founder of Featurespace. “The Automated Deep Behavioral Networks patent and associated technologies deliver the right levels of model performance the industry needs to decrease fraud and protect consumer accounts before attacks happen.”

Featurespace’s second patent is for Behavioral Anomaly Score, which identifies anomalies in individual customer behavior without having any prior knowledge of contextual high-risk behavior. This technology appreciably amplifies the ability to identify when a person’s behavior 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 behavior has changed with greater precision and from there, construct more complex models for change detection; further reducing the incidences of financial crime.

“The fight against fraudsters and the organizations that commit financial crime on a large scale is challenging and ever-evolving,” Excell added. “Technology, specifically machine learning will continue to be central in this fight and these two patents from Featurespace advance our leading market position and our capacity to help progressive financial institutions protect the consumer.”

Identity fraud costs US citizens a total of around US$56 billion last year, with about 49 million consumers falling victim, according to a study by Javelin Strategy & Research. A UK Citizens Advice report states one in three adults in the UK were targeted by a pandemic-related scam last year.

Full public patent applications have been filed in the US, UK, EU and Patent Cooperation Treaty (PCT).


About Featurespace – www.featurespace.com
Featurespace™ is the world leader in Enterprise Financial Crime prevention for fraud and Anti-Money Laundering. Featurespace invented Adaptive Behavioral Analytics and Automated Deep Behavioral Networks, both of which are available through the ARIC™ platform, a real-time machine learning software that risk scores events in more than 180 countries to prevent fraud and financial crime.

ARIC™ Risk Hub uses advanced, explainable anomaly detection to enable financial institutions to automatically identify risk, catch new fraud attacks and identify suspicious activity in real-time. More than 30 major global financial institutions are using ARIC to protect their business and their customers. Publicly announced customers include HSBC, TSYS, Worldpay, NatWest Group, Contis, Danske Bank, ClearBank, AK Bank and Permanent TSB.

Media contact
Michael Touchton, Featurespace
PR and Communications Manager
[email protected]
+1 (423) 364-5491