How can Quantexa networks enhance your machine learning models?
We have just published a blog post on how Quantexa networks can enhance the performance of machine learning models for a number of use cases.
From detecting traffic obstacles for self-driving cars to recommending products, Machine Learning (ML) plays a key role in most organizations’ decision-making processes. For financial services organizations, machine learning is increasing critical in identifying risk, within consumer and corporate entities.
The Importance of Context
In the world of risk modeling, the input data points (or features) are particularly important – usually even more important than the choice of model or algorithm. In an industry with significant regulatory pressure for transparency and explainability in modelling, model choice is often restricted meaning the choice of input features can be the core reason for success (or failure) of the model. So, the key question is – how can we bring as much context into our features as possible?
Network based features provide an excellent way to inject a huge amount of information into your model, whilst keeping the required transparency and explainability One way of achieving this is by using bespoke document-entity networks to generate features describing how businesses and individuals are related to each other. At Quantexa, we’ve used network features describing relationships between companies and their directors as key inputs into our ML Shell Company detection model, which has resulted in a 20% uplift in performance compared to using just record-level features.
The model output – predicted shell companies and the agents creating them – has a range of applications for enhanced risk detection across AML (Anti-Money-Laundering), KYC (Know-Your-Customer), Credit Risk and Fraud.
Read the full blog on How to Build Additional Context into Your ML Algorithm | Quantexa which includes:
- Introducing Network Features
- The Power of Networks in Machine Learning
- Unearthing More Complex Patterns
How is Quantexa adding value to your Machine Learning pipeline?