Financial transactions like payments and financial asset price series are highly complex. They reflect emergent, reinforcing, adaptive properties which cannot be captured by traditional standard approaches based on regression and linear math. As a result, the interconnectedness of financial data is not taken into account sufficiently. Until now, valuable insight remains uncovered for financial service companies. Our mission is to enable financial service companies to reveal this hidden information and benefit from them.
Network and Graph Theory
In order to reach our mission we use Network and Graph Theory. The underlying Network Science behind all Firamis-applications is well known and developed in nature science for decades (e.g. in the field of cell biology). We combine the Network and Graph Theory with unsupervised machine learning approaches, such as segmentation or dimensionality reduction. Thereby we transform your financial data into a 3D Network Diagram, visualising thousands of columns and millions of rows.
Firamis Software Platform
We consequently use an open source-based software platform, which is based on the language R for mapping our financial landscapes. In connection with a state-of-the-art Graph Database we execute network graph analysis, clustering, complex information filtering and topological data analysis in order to gain the valuable new insights from your financial data sets.
In order to deploy our applications we use Docker-technology. As our applications are easy to connect with via API, we operate our applications via Software-as-a-Service worldwide.
The overall Firamis-key concept is using Explainable AI. Consequently, customers and regulators can verify and justify the results of our applications. AI is not a black box.
If you have more questions regarding our technology feel free to contact us.