Customer Story

Transparent corporate structures

With the Knowledge Graphs used by Trivadis, a banking association can now, for the first time, automatically make financial and other connections withingroups and their affiliated companies transparent.

In Short


Consolidation of large amounts of information, automated recognition and transparent presentation of dependencies.


Harmonisation of all available data across various internal and external data sources and integration into a knowledge graph.


Transparency in the quality of data sources and complex networks as well as possibilities of forecast presentation.

Our Solution

The growing complexity of global corporations, tax-optimized corporate structures and the systemic relevance of multinational corporate groups pose major challenges for the finance world. A concrete example of this is issuing credits that often range in the 100 millions or even billions. Securities for these credits can take the form of guaranties issued by other companies, for example.

It gets problematic, however, if the company offering the guaranty is an invisible subsidiary or even a partnership through a complex network. In these cases, securities in the form of guaranties need to be assessed in a different risk class. Consolidating information about global corporate networks so that dependencies of this type can be recognized reliably and in adequate quality required great effort from banks until now. This was why an international banking conglomerate called on Trivadis to develop a solution that would recognize these interdependencies automatically and display them in a transparent and comprehensible way.

The Knowledge Graph shows corporate connections that were not previously visible

Although the banking conglomerate already had an IT partner with expertise in the financial sector, Trivadis was tasked with developing a Knowledge Graph for the challenging issues faced by analysts. The customer, who had already had positive experience with Trivadis from a number of other SLA contracts, was drawn in by the company’s experience and expertise in terms of data preparation, data analysis and the beneficial provision of data. Trivadis harmonizes all available company data across various internal and external data sources and integrates them in one Knowledge Graph. Each company in the Knowledge Graph is clearly marked as a data point, no matter which dataset it comes from. The gathered data can then be put in relation with one another, regardless of the source.This allows financial and other connections within corporate groups and associated companies to be analysed automatically for the first time. Banks are therefore able to reduce the credit risk and can be sure that they are only issuing credits that are adequately secured. Bringing this data together makes it possible to connect it with additional data from other sources.

Reliable foundations reduce financial crises

It was not previously possible to gothrough the vast amounts and formats of data manually in such a way that made it possible to see the information that was needed. In addition to the question of which companies belong together, forecasts can now be made about the financial risk in certain industries as well as about the state of the market in certain countries at different points in time. For the first time, the quality of the various (sometimes commercial) data sources can also be evaluated.

New opportunities with increased insight

The intelligent linking of data aids analysts in their daily work. After the experiences of the financial crisis of 2008 and 2009, banks can use this to reduce the risk of credit losses and prevent a possible weakening of their own financial position. In addition, they gain increased insight into the financial relationships of entire industries, allowing them to optimize their business models and develop new products based on these findings.

Information from a broad range of data sources is needed to show company relationships and debt capital from corporate conglomerates. Trivadis has unified the foundation of data in such a way that it could be depicted in a Knowledge Graph. In addition, the Knowledge Graphs can also be used to make a statement about the financial stability of various industries and to answer other economic questions.

Martin Ursprung, Principal Account Manager, Trivadis

Technologies Used

  • Dataiku (ETL)
  • Microsoft Azure (Cloud Infrastructure)
  • GraphDB (KnowledgeGraph – Triplestore)
  • Python/Jupyter (Data Science Tools und Frontend)
  • Graph Embeddings (Knowledge Graph Machine Learning)

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