Ensuring compliance with laws, regulations, and policies is crucial for many heavily regulated institutions including banks, insurance companies, and government agencies. By efficiently combining different data sources and increasing data quality, knowledge graphs surface critical, actionable insights from complex connections and patterns that naturally occur in regulatory compliance data. The extracted insights have the potential of significantly improving, simplifying, and speeding up compliance efforts in any regulated organisation.


In this presentation, we briefly introduce knowledge graphs and explain why they are relevant to many compliance issues. We then show a real-world example of a governance knowledge graph for a regulated government institution, explaining how it has been built and what value it has provided to the organization. We will discuss the challenges encountered along the way and how they have been tackled using Neo4j and GraphAware Hume.

Michal Bachman
CEO at GraphAware

Michal Bachman is the CEO of GraphAware. He holds an MSc degree in Computing from Imperial College London, where he wrote a distinguished thesis on graph databases. After finishing his studies, Michal decided to found GraphAware with the aim of helping people use and adopt graphs, and leverage graph technology to develop a competitive edge.

Prior to founding GraphAware, Michal worked in the software industry as a developer, architect, consultant, and trainer for over 10 years. The expertise he gained opened the doors to the world of natural language processing and graph-based machine learning GraphAware specialises in now.

Today, Michal focuses on the application of graph technologies for intelligence and law enforcement use cases, graph-based security models, and high-performance graph data ingestion. Michal occasionally publishes technical articles and frequently speaks at graph-related conferences around the world.

Dr. Alessandro Negro
Chief Scientist at GraphAware

Dr. Alessandro Negro is immensely passionate about computer science, data research & specializes in natural language processing, recommendation engines, fraud detection, and graph-aided search.

After pursuing Computer Engineering academically & working in various capacities in the domain, he pursued a Ph.D in Interdisciplinary Science and Technology. With his interest in graph databases peaking, he founded a company called Reco4 which was an open-source project developing a recommendation framework based on graph data sources.

Now, he is Chief Scientist at GraphAware. With clients such as LinkedIn, World Economic Forum, European Space Agency, and Bank of America, GraphAware is singularly focused on helping clients gain a competitive edge by transforming their data into searchable, understandable & actionable knowledge. He spends his time leading research for Hume - GraphAware Knowledge Graph platform -, speaking at various conferences around the world and working on the next book.

Dr. Vlastimil Kus
Lead Data Scientist at GraphAware

Dr. Vlasta Kus is a Lead Data Scientist at GraphAware. He is passionate about delivering machine learning solutions into production.

Vlasta has a solid background in mathematics and research in physics. He became passionate about combining Machine Learning and property graphs into Knowledge Graphs. Over the years he gained extensive experience with statistical data analysis, Machine Learning, Deep Learning, and building Knowledge Graphs in various domains.

Currently, Vlasta specializes in natural language processing, knowledge graphs, graph analytics, and graph ML.

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