Quantexa, a London-based data and analytics company, is innovating Anti-Money Laundering (AML) monitoring and investigation with the use of contextual decision intelligence (CDI), a means of enriching internal data with external data and building networks of relationships to create a contextual view of a customer.
The company says complex financial crime in capital markets, including money laundering, can only be detected by using CDI to generate meaningful alerts and effectively detect risk. Existing systems, it argues, take a one-dimensional approach to risk detection by looking at every transaction in isolation.
Ross Aubrey, global head of financial markets at Quantexa, says: “Following the 2019 FCA thematic review [which identified money laundering risks particular to capital markets], many firms reviewed what they were doing. They were using old tools that raised a lot of alerts and far too many false positives.”
Quantexa’s use of CDI is designed to cut down false positives and identify real money laundering issues by building context around customers using entity resolution, where necessary based on fuzzy matching, and network generation to uncover hidden relationships. Analytical techniques are used to create behavioural profiles and understand what different peer groups should look like, and automatically generate contextual alerts to identify high-risk relationships and analyse those who deviate from usual behaviour.
Aubrey says: “CDI allows organisations to make faster, accurate decisions using vast quantities of data.”
Quantexa’s capital markets clients – it also works in the insurance and government sectors – include HSBC, which integrated the company’s technology into its systems in 2018, and Danske Bank, which deployed the company’s platform for financial crime detection late last year.
The platform is based on entity resolution that provides a single dynamic view of each entity. Data is sourced both internally and from third parties such as client registry databases and data partners including Dun & Bradstreet, Dow Jones, and Bureau von Dijk (now part of Moody’s). It is open source, making it interoperable with clients’ existing solutions, and the technology stack includes open source tools such as TensorFlow for machine learning, Spark for large-scale analytics, Hadoop for distributed processing of large data sets, Elastic for data interrogation, and Scala and Python languages to write models. The platform can be installed on premise or in the cloud.
As well as addressing AML, Quantexa integrates trade surveillance to provide a complete picture of all suspicious activity. Based on its entity resolution, the platform is also well suited as a solution for Know your Customer (KYC) and customer intelligence.
Aubrey says: “We are moving towards dynamic KYC that triggers reviews of customers rather than waiting for a planned review. Reviews across the client lifecycle can identify risk more quickly, serve customers that do not present risk better, provide an exit process, and allow better decisions based on all the data a company has.”
Quantexa was founded five years ago in March 2016, and in March 2017 raised $3.3 million of Series A funding led by Albion Ventures and HSBC, which, as above, went on to become the company’s first published customer. Series B funding of $20 million was led by Dawn Capital with continued support from HSBC and Albion Capital Group in August 2018.
Series C funding of $64.7 million was raised in July 2020, the latest round led by Evolution Equity Partners, with major participation from existing investors Dawn Capital, Albion and HSBC, and bringing total funds raised to $90 million.
The funding will be used to support further regional growth, Quantexa already operates in more than 70 countries, but plans to expand further into North America, Asia Pacific and Europe, as well as for development of more platform applications across financial services.
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