AI at Quantexa

AI is at the heart of the Quantexa Decision Intelligence Platform, helping users unify data and create context to make accurate and reliable decisions.

Machine Learning (ML) and AI are applied in four distinct areas within the Quantexa Platform:

Composite AI within Quantexa

The Quantexa Platform uses multiple AI techniques to solve a wide range of business problems more efficiently. These include AI-driven production code, models, and development tools to help users deploy AI appropriate to their use case. Quantexa’s Composite AI Stack combines subject matter expertise and domain knowledge with various machine learning, Natural Language Processing (NLP), and deep learning techniques.

The diagram below summarizes AI techniques available across Quantexa-based workflows, with flexibility and modularity, to help identify risks and opportunities.

Key Platform Components Driving Composite AI

Entity Resolution with Machine Learning

AI models enable users to identify opportunities to optimize their Entity Resolution configurations to drive more accurate entities

Read more: Entity Resolution General Playbook (login required)

Overlinking Root Cause Analyzer (ORCA)

This is for analysts to determine the root cause of any potential overlinking issues during Entity Resolution. ORCA uses advanced statistical techniques to approximate match-rate distributions across data sources. These distributions are fed into sophisticated anomaly detection algorithms to:

Reveal bad input data (e.g. default values) adversely impacting Entity Resolution.
Indicate which compounds should be tweaked by adding fine-tuned exclusions to improve their performance.
Provide interactive graphical outputs such as heat maps that quickly alert users to problem areas of their data.

Read more: Entity Quality ORCA (login required)

Quantexa’s Pre-Built AI Models for Risk Identification

Shell Company and Mini Umbrella Company Detection (login required)
Calculation of Ultimate Beneficiary Owners (login required)
Identification of SMEs (Small Medium Enterprises) (login required)
Identification of related high-risk entities through multi-hop risk detection

The QKnowledge Graph Python Library

The QKnowledge Graph Python Library includes a suite of tools to work with large Entity-based networks in Python. Some of the features are:

Large-scale analytics for Quantexa Networks, notebook-friendly visualizations, and extensible by data scientists:

Enables easy application of graph analytics to Quantexa networks (raw bi-partite, heterogenous graphs are unsuitable for most algorithms)
Provides multiple perspectives on the same graph data, utilizing MetaGraphs
Enables efficient application of global algorithms, e.g. PageRank

Fast & easy neighborhood creation:

Cypher-like path-based queries
Traverse billions of edges in minutes
Efficiently analyse highly linked nodes through techniques such as sampling and filtering

State-of-the-art Graph Learning at scale, facilitating rapid iteration for exploration and modelling:

Seamless interfaces to Quantexa platform for data ingest and scoring (Quantexa Batch Resolver and Assess)
Open interface to leading open-source graph libraries
Graph learning with libraries such as PyG and Graph Neural Networks (GNNs)

Read more: QKnowledgeGraph (login required)

Customisable Scoring with Quantexa Assess

Quantexa Assess is a framework to enable the production of complex network and Entity-based detection logic, with scores working as business rules, triggers or features within ML.

Data Science teams can build/maintain their own models as needed with Assess, using Scala and Python interfaces to access the widest set of ML algorithms, and providing version-controlled parameterization to allow business users to run and maintain use case models. The framework:

Enables write-once scores, servicing both batch and dynamic infrastructures
Manages score dependencies (DAG) and facilitates rapid iteration of individual scores
Supports rich score descriptions & node highlighting in UI
Provides flexible Alerting and Re-alerting logic
Supports config deployable prebuilt scoring models for use with multiple data models
Records all scoring output for audit and model retraining purposes

Assess understands entities and networks and enables multiple scoring levels:

Read more: Using Assess Dynamic Scoring for the First Time

The QPython Library

The QPython Library allows data scientists and Python developers to Pythonically interact with Quantexa, for example:

For ad hoc analytics

Explore data and test hypotheses
Allows Data Scientists to discover additional insights over existing detection logic
Directly share comprehensive findings with Business Users within the UI, raising 'Tasks’ containing networks and detailed scoring data.

As a development tool

Productionize Python detection logic within Assess – combining Python with Scala as desired.
Combine with the full range of out-of-the-box scores and Assess scoring features to raise alerts including custom Python scores and logic.

For tuning and improvement

Analyze scoring outcomes utilizing Python tooling
Quantexa Tools compare both alerts and scoring outcomes
Simulate threshold changes on historical data.
Utilize Python-based ML to optimize changes
Record and evidence changes with notebook functionality

Read more: Introducing QPython

Product Documentation for Composite AI Components within Project Example

Note: All Documentation site links require login.

Rule based standardization, cleansing and parsing, NLP based parsing models:
- Quantexa Parsers
Automated field identification:
- Data Source fields - Content Predictions
Transparent Entity Resolution configuration, optimised configuration:
- Configuring Batch Resolver - Configuration examples
Entity Quality Scoring Models:
- Using Entity Quality Scores
Graph Script network definitions:
- Graph Scripting
Use case specific risk indicators and mitigators, Expert Scorecard, Alerting and re-alerting:
- Detection Packs - Scores
ML based risk detection, Model optimisation
- Constructing an ML-based Score
Model monitoring and tuning
- QPython - Performing Assess parameter tuning

Visualization and Exploration of Risk

Top-down thematic filtering (Explorer)
- Explorer overview
Temporal and geospatial based presentation of risk
- Geospatial Search
- Timeline
Contextual description & visualization
- Context Panel
Graph Perspectives / Model based Perspectives (e.g., Entity to Entity in investigations)
- Perspectives