Industry-leading technology
The world’s largest financial institutions depend on CUBE for industry-leading technology that is setting the Global Standard in Automated Regulatory Intelligence (ARI). CUBE uses the latest Artificial Intelligence and Machine Learning techniques to provide unrivalled depth and breadth of regulatory intelligence, tried and tested by the biggest banks in the world.
A global standard
CUBE applies artificial intelligence and machine learning to provide relevant regulatory intelligence. Unique levels of granularity and classification help to drive efficient and compliant behaviour.
CUBE’s RegTransform and RegBrain modules work in tandem to automate the compliance journey. Filtering and classifying regulatory data. Linking real-time regulatory content to a firm’s internal policies, procedures and controls.
Regulatory Transformation Engine
RegTransform automatically collects, structures and enriches the world’s regulatory content in real time.
Regulatory Cognition Engine
RegBrain receives structured regulatory content from RegTransform. RegBrain then links it to create a semantic map of a customers’ regulatory universe.
RegBrain enhances every other AI-powered use case within the CUBE suite of solutions.
How we approach the use of Artificial Intelligence
At CUBE, we believe in both transparent and ethical technology. Our proprietary AI is founded upon six interconnected core principles:
Explainable
User trust and transparency are as important as model performance. We use a variety of visualisations to show how our models make decisions.
Human-in-the-loop
Our data scientists work hand in hand with our regulatory experts. As a result, our models learn from high-quality data, expert analysis and in-product user feedback.
Semantic
We use state-of-the-art NLP models and have created a CUBE language model, both trained and fine-tuned on our data.
Our models go far beyond keyword matching—they have a deep and complex understanding of syntax and legal vocabulary.
Scalable
Our models handle the volume, variety, and velocity of regulatory change. This is due to both their architecture and serverless deployment in the cloud.
Secure
Given the sensitivity of our customer data, we protect backend and frontend access to our services and our data pipeline. We also anonymize all usage data used for modelling.
Sustainable
We are well aware of the carbon footprint of AI models trained on enormous volumes of text data. We improve our models by taking the time to curate smaller, higher-quality datasets. It’s a win for accuracy and a win for the environment.
