This post explores the types of investigations you can conduct with graph analysis, showcasing the agility, speed, and capability enabled by GraphAware Hume, the graph data integration and investigation technology used in Neo4j GraphAware solutions.
Graph schema: The foundation of investigation
At the heart of GraphAware Hume is a robust graph schema that provides a logical representation of entities and their relationships. For our demonstration, we’ve crafted a simple schema encompassing persons, organizations, sanction authorities, and values, providing a structured framework for analyzing complex ownership networks.
Data ingestion and integration
The effectiveness of UBO investigations heavily relies on the quality and diversity of data sources. Leveraging Open Ownership Statements and subsets of Open Sanctions data, we illustrate the seamless ingestion process into our graph database, Neo4j. This ingestion capability ensures that disparate datasets conforming to standardized formats like BODS (Beneficial Ownership Data Standard) can be efficiently integrated, laying the groundwork for comprehensive analysis.
Investigative agility: entity resolution and grouping
One of the key challenges in UBO investigations is entity resolution, especially when entity names and attributes vary across datasets. With GraphAware Hume’s entity resolution capabilities powered by Senzing, we unify entities, identify potential duplicates, and consolidate them into cohesive entity groups. Moreover, through advanced grouping techniques, we uncover familial relationships within ownership structures, enhancing our understanding of complex networks.
Uncovering insights: advanced querying and visualization
GraphAware Hume leverages graph technology to empower analysts to navigate complex ownership structures with unparalleled ease. By executing simple queries, such as identifying ownership paths spanning multiple levels or pinpointing entities linked to sanctioned jurisdictions, we demonstrate the agility and depth of analysis afforded by graph-based approaches. Visualizing these insights in graph format provides a comprehensive view of interconnected entities, transcending the limitations of traditional tabular representations.
Automation and monitoring for proactive insights
Lastly, we will highlight the importance of proactive monitoring in UBO investigations. Through GraphAware Hume’s monitoring capabilities, analysts can define patterns of interest, such as identifying newly sanctioned entities or detecting secretive ownership structures. By automating these detection processes and receiving real-time alerts, analysts can stay ahead of emerging risks and swiftly respond to evolving scenarios, ensuring robust compliance and risk management practices.
Case 1: Identifying complex ownership structures
We explore a database containing over 10 million individuals and organizations, along with approximately 11 million relationships. We demonstrate how to uncover complex ownership structures involving at least five levels of relationships using GraphAware Hume’s advanced expand feature, a visual query-building tool. This allows us to identify matches in the database without writing queries, displaying the results directly on the screen. Traditional data structures, like tables or Excel sheets, often fail to represent such intricate networks effectively. Instead, we visualize these relationships as graphs, providing a clearer and more intuitive understanding of ownership structures.
Case 2: Familial relationships and grouping
Delving deeper into ownership networks, we uncover familial relationships within ownership structures. Leveraging advanced grouping techniques, we identify clusters of entities linked by familial ties, enhancing our understanding of familial wealth distribution and ownership dynamics. Through visual representations of familial groups, analysts gain clarity on interconnected relationships, facilitating more nuanced analysis and decision-making.
Case 3: Identifying circular ownership structures
Circular ownerships, which involve entities owning shares in each other in a closed loop, are illegal in many countries because they can obscure true ownership, facilitate fraud, and undermine financial transparency and market integrity. They pose a unique challenge in UBO investigations due to their secretive nature. Here, we showcase how GraphAware Hume’s advanced querying capabilities enable analysts to probe complex ownership paths and uncover circular ownership arrangements. By automating the detection of entities with incoming and outgoing ownership relationships, analysts can identify secretive ownership structures with ease, fostering transparency and accountability.
Case 4: Cross-data source analysis
Cross-data source analysis is essential for gaining a holistic understanding of ownership networks. Here, we show how GraphAware Hume facilitates traversing interconnected entities across diverse datasets. By leveraging entity resolution techniques, analysts can seamlessly navigate ownership paths across multiple data sources, uncover hidden connections, and support comprehensive analysis. Through visual representations of cross-data source relationships, analysts gain valuable insights into complex ownership networks.
Case 5: Identifying sanctioned entities using entity resolution
We focus on identifying sanctioned entities within ownership networks. We explore the challenge of unifying ownership structures across disparate datasets. By ingesting Open Ownership Statements and subsets of Open Sanctions data, we highlight the importance of detecting entities sanctioned by regulatory authorities. GraphAware Hume’s entity resolution capabilities streamline identifying and consolidating entities with varying attributes. By unifying ownership structures, analysts gain a comprehensive view of complex networks, enabling deeper insights into beneficial ownership relationships.
Case 6: Automation for proactive monitoring of sanctioned individuals
Finally, we explore the importance of automation for proactive insights in UBO investigations. Through GraphAware Hume’s monitoring capabilities, analysts can define and automate detection patterns to identify emerging risks and trends in ownership networks. By receiving real-time alerts on newly detected patterns, analysts can stay ahead of evolving threats and take proactive measures to mitigate compliance risks. Automation helps analysts monitor ownership networks efficiently, supporting robust risk management and regulatory compliance.
Each section highlights a distinct aspect of UBO investigations, showcasing the agility, speed, and capability enabled by graph technology and tools like GraphAware Hume. Through data integration, entity resolution, advanced querying, and proactive monitoring, analysts can unlock deeper insights into ownership networks, mitigate risks, and foster transparency in the fight against financial crime.
Conclusion
Knowledge graph technology revolutionizes UBO investigations by offering unparalleled agility, speed, and capability. From data integration and entity resolution to advanced querying and proactive monitoring, tools like GraphAware Hume empower analysts to navigate complex ownership networks with confidence and precision. By harnessing the power of graphs, organizations can unlock deeper insights, mitigate risks, and foster transparency in the global fight against financial crime.
Unlocking Complex Ultimate Beneficial Ownership Investigation
