About a-team Marketing Services

A-Team Insight Blogs

Quantexa Addresses AML with Contextual Decision Intelligence

Subscribe to our newsletter

London-based data and analytics company Quantexa is addressing the challenge of 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. This approach is aimed at generating meaningful alerts to the risk of complex financial crime in capital markets like money laundering.

Quantexa argues that existing systems take a one-dimensional approach to risk detection by looking at every transaction in isolation. “Following the 2019 FCA thematic review [which identified money laundering risks particular to capital markets], many firms reviewed what they were doing,” says Ross Aubrey, global head of financial markets at Quantexa. “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 went on to become the company’s first reference customer. A 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.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Potential and pitfalls of large language models and generative AI apps

Large language models (LLMs) and Generative AI applications are a hot topic in financial services, with vendors offering solutions, financial institutions adopting the technologies, and sceptics questioning their outcomes. That said, they are here to stay, and it may be that early adopters of Generative AI apps could gain not only operational benefits, but also...

BLOG

Unlocking the Potential of AI in AML

By Steve Marshall, Director of Advisory Services, FinScan. AI has been increasingly used over the last few years to combat financial crime and money laundering. While it may not have lived up to its original billing as a silver bullet, some key lessons have been learned since those early days. Firstly, while AI has many...

EVENT

TradingTech Briefing New York

Our TradingTech Briefing in New York is aimed at senior-level decision makers in trading technology, electronic execution, trading architecture and offers a day packed with insight from practitioners and from innovative suppliers happy to share their experiences in dealing with the enterprise challenges facing our marketplace.

GUIDE

Regulatory Data Handbook 2023 – Eleventh Edition

Welcome to the eleventh edition of A-Team Group’s Regulatory Data Handbook, a popular publication that covers new regulations in capital markets, tracks regulatory change, and provides advice on the data, data management and implementation requirements of more than 30 regulations across UK, European, US and Asia-Pacific capital markets. This edition of the handbook includes new...