Quantexa's AI Roundup - 2023
In July 2023, Quantexa announced a significant investment into its Artificial Intelligence (AI) capabilities (Quantexa Bringing Total Investment in AI R&D to over $250M by 2027). Since this announcement, there has been significant advancement in the AI space, and growth in some of the core AI capabilities at Quantexa. Alongside the significant growth of the NLP capability, Quantexa’s Analytical Innovation team have completed the MVPs of their three flagship products which are now released under experimental. These tools use Quantexa networks to uncover insights: the Entity Resolution AI suite; Q-Knowledge Graph and Shell Company Detection. In this round up post, we introduce the three products and demonstrate how they can add value to your Quantexa deployment.
The Entity Resolution (ER) AI Suite
The ER AI suite provides a series of tools for analysing the outputs of Quantexa’s ER product and provides suggestions for improving the configurations powering the ER using AI. In particular, the tool can detect overlinking and underlinking in Quantexa Entities and their root causes.
The overlinking detection tool is powered by machine learning with features based on the qualities of the Entity’s constituent record-compound graph (read more about using the Entity Quality Overlinking tool for the first time). These graph-based features include the use of several complex graph algorithms (e.g., the Stoer-Wagner algorithm) to find shapes which are indicative of overlinking. Such shapes include ‘bridges’ in the network which incorrectly link Entities together, as well as graphs with very long paths. Statistical techniques can then be applied to determine what compounds or data points may be leading to this overlinking.
The underlinking tool uses sophisticated graph algorithms to find ‘Super-Entities’ – Entities which should be formed of several existing Entities. This helps the user to identify template changes to merge such entities together in future ER runs.
Q-Knowledge Graph is a series of tools for analysing large-scale Quantexa Entity and Document graphs. It scales to billions of nodes and edges and uses sophisticated optimisation techniques to provide extremely fast implementations of core graph transformations and algorithms (including page rank).
Not only does the tool provide access to commonly used graph algorithms out of the box (for example, PageRank) – it also provides a connection to common graph learning libraries such as PyG. This enables several use cases across Risk, KYC and MDM and has already been deployed for transactional use cases in a global bank. It will also be a core back-end component of a number of upcoming Quantexa AI products.
Shell Company Detection
The Shell company detection tool uses machine learning to identify shell companies, using characteristics of the local ego-networks of the companies. The model uses a combination of structural features (e.g., links to known shell directors); temporal features (e.g., patterns of director resignation) and static features (including the size of the corresponding corporate registry Document). For more information, see What can Network structure tell us about risk?
The current model is built specifically for the UK and Singapore and can encapsulate some behaviours specific to shells in these jurisdictions. Models focused on other jurisdictions are coming this year.
Upcoming AI releases
The NLP team at Quantexa are also developing a machine learning pipeline called Text2Networks for working with unstructured data, which will be available in the next major release of Quantexa. The Text2Networks pipeline is a highly-configurable pipeline of ML models for mapping any unstructured textual data into a graph. The pipeline detects, labels and organizes people, places, and things in the real world – the supported Entity types include People, Locations, Companies and Geo-political organizations. With text2networks integrated into the core Quantexa product, our users will be able to incorporate any textual data source that is important for their business. Concrete example could include global news, intelligence reports, and Suspicious Activity Reports (SARs).
There are several tools in development, including further tooling within the ER quality suite and Q-Knowledge Graph, as well as other risk models such as the SME detection tool which will be coming in later releases of Quantexa.
To keep up with the latest releases, be sure to follow our Release Announcements topic.