Applicant Type: Private Company
Headquarter City: Hod Hasharon
Headquarter Country: Israel
Innovation Name: ThetaRay SONAR AML Transaction Monitoring
Innovation Type: Product
Category: Fintech Making a Difference
Status of Innovation: In-Market
Innovation Active Since: 05/01/2021
Innovation Description: ThetaRay’s category-leading artificial intelligence (AI) engine, named SONAR, uses unsupervised machine learning that enables uncovering of unknown and known money laundering cases for financial institutions.
ThetaRay’s AI and machine learning technology, a methodology called “artificial intelligence intuition,” uses proprietary unsupervised and semi-supervised machine learning algorithms that replace human bias. This empowers the system with an unmatched ability to recognize anomalies and find unknowns outside of normal behavior, compared with rules-based or supervised machine learning solutions.
The ThetaRay machine-learning (ML) methodology and algorithms are applied to dozens of risk indicators associated with financial crimes. This risk-based AI approach paints a clear picture for compliance teams that enables them to detect abnormal activity within large sets of data and to effectively calculate and pinpoint transactions indicating suspicious activities.
ThetaRay’s ML process also provides insights into customer identity in cases with limited visibility where KYC information is lacking, creating risk profiles of non-customers and transparency across complex paths.
Using 19 patented algorithms, the system identifies the abnormal customer or transactional behavior patterns which deviate from the expected or “normal” behaviors from an AML risk perspective. In this way, differs from rules-based approach by enabling a multi-dimensional view of the data, meaning, not relying on specific scenario or threshold to generate an alert but measuring several risk indicators and data points to find the unusual events.
This enables the rapid discovery of both known and unknown money laundering threats, with a peerless 95% detection rate and a 99% reduction in false positives compared to rules-based solutions.
Role: Director of Sales
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Full Name: Nina Gilbert
Role: PR and Content Manager