CUBE’s Industry Leading Technology
The world’s biggest banks turn to 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 largest institutions in the world.
The global standard in Automated Regulatory Intelligence
CUBE’s AI transforms the world of unstructured and highly complex regulatory data into customer specific, actionable regulatory intelligence.
We do this through two engines, RegTransform and RegBrain, which work in tandem to automate the compliance journey – from filtering and classifying regulatory data all the way through to linking real-time relevant content to internal infrastructure.
CUBE’s technology-first approach
At CUBE, we believe in both transparent and ethical technology. Our proprietary AI is founded upon six interconnected core principles:
User trust and transparency are as important as model performance. We use a variety of visualisation methods to illuminate how our models make decisions.
Our data scientists work hand in hand with our regulatory experts. This synergy ensures that our models continuously learn from the highest quality data. Just as importantly, users directly influence the performance of our models through in-product feedback mechanisms.
Customers help to steer the performance of our models through in-product feedback mechanisms. Our models are automatically retrained on, and continuously learn from, this feedback.
We use state-of-the-art NLP models and have created a CUBE language model, both fully 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.
Our models are designed to handle the volume, variety, and velocity of regulatory change. This is due to both their architecture and serverless deployment in the cloud.
We improve our models by taking the time to carefully curate smaller, higher-quality datasets. It’s a win for accuracy and a win for the environment.