Make faster, more accurate decisions
Our analytics framework gives you a simple way to use the context of resolved entities and relationships in scenarios, rules and models.
It builds on open interfaces to familiar tools and allows you to leverage existing models as well as get uplift from new features using techniques you already use. It provides models that are transparent, easy to deploy and to integrate into live services.
How context improves your analytics process
- Create entities and networks to build rich customer context that describes real-world relationships – providing more than just a graph of your raw data.
- Use this rich network to easily create the analytical components you need – whether they are simple or complex.
- Benefit from the full power of AI and machine learning with our best practice methodology, whilst still maintaining open and transparent models. Make them fully accessible to your end users and easily explained in a governance process.
- Empower your data scientists with network visualization to understand the context they’ve created, explain the results and validate that their model is working.
The benefits of our analytics framework
Our framework gives you complete ownership all of your analytics.
Total flexibility to manage analytical dependencies within complex flows of logic.
Data scientists can see why models are behaving the way they are, with fully transparent and accessible models.
Investigators can easily understand the decision-making process and each decision made by the model.
Adhere to strict governance and process requirements with a systematic and auditable approach for putting models into production.
Get stronger predictions by leveraging entities and networks to create context-based models.
Use open interfaces to the machine learning libraries you are familiar with.
Leverage behavioral profiling and dynamic segmentation for additional context.
Leveraging the expert knowledge within your organization using custom rules and features describing intuitive concepts.
Supporting the journey to supervised machine learning
Supervised machine learning (also known as predictive) uses historic cases to statistically identify patterns. However, certain use cases, including many anti-money laundering, have very few previously known examples.
Our proven methodology supports the journey to a supervised model for these use cases.
Using AI and machine learning with Quantexa
Find out how to you can leverage AI and machine learning to create the context needed to make better decisions – while maintaining an explainable and transparent approach.
Enhance the performance of your analytics framework
Speak to our team and learn how our platform lets you use context to improve your analytics processes and increase the value of data science in your organization.