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Scaling Data Integration and building an AML foundation

Denne idé er en del af Anti Money Laundering (AML) Challenge 2019

Tim
Tim
21. november 2019

The primary purpose of our platform is to take data from wherever it may come, clean it and deduplicate it, and connected it internally and across systems.

When this is done, we can make it available to a consumer, which can then perform their analysis of the data, or perform machine learning with the clean data. At the same time, we perform quality analysis of the data, so the recipients can know the level of data they're working with.Our platform builds upon a graph database, which makes it possible to very accurately model the real world.

This is because we can model any kind of relationship between entities, no matter their source, and thus get a maximum amount of information from the data. Adding to this, we can perform searches externally, to increase the amount and quality of data, we have on each entity.

Using this, we can create even more connections between entities, and more closely depict the reality, in which we're operating.Using these methods, we have successfully modeled the world of very large corporations, which has 100+ terabytes of data, coming from a lot of different systems

 

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