Entity resolution forms the foundation for creating accurate, meaningful graph visualizations. Messy, siloed data can obscure critical relationships. Identifying and consolidating duplicate entities in knowledge graphs is essential for uncovering hidden connections, detecting risk and gaining a clear view of your data landscape. While entity resolution may go by many names – deduplication, data matching, entity linkage, fuzzy matching – the challenge remains the same: how do we ensure that our data accurately represents unique entities? This is important when confronting innocent errors. It is an imperative when grappling with fraudsters and deliberate obfuscation.

The Linkurious Decision Intelligence platform with Senzing Inside™ brings no-code entity resolution directly into Linkurious’ user-friendly interface. This episode of Graph Power Hour explores how Senzing® entity resolution is integrated within the Linkurious Decision Intelligence platform to deliver no-code capabilities that are both intuitively accessible and explainable in the Linkurious workspace, designed for technical and business users alike.

Financial crime investigations demonstrate real-world value. Thibaut Kellam, Head of Customer Success at Linkurious and former Financial Security Department Manager at BNP Paribas, shares examples of how graph analytics excel at financial crime detection.

Key Topics:

• The entity-centric approach to resolving duplicate records and detecting intentional obfuscation.
• How organizations can dramatically reduce time-to-detection for emerging fraud patterns.
• The architecture and workflow of the Linkurious-Senzing integration for no-code entity resolution.
• A case study using the ICIJ offshore database (130K+ duplicates identified).
• Financial crime detection and investigation leveraging interactive visualization and graph analytics.
• The role of graph algorithms in uncovering complex money mule networks.
• The graph technology learning curve and how analysts develop pattern recognition skills.

Key Takeaways:

• No-code entity resolution within Linkurious allows both technical and business users to see how entities are resolved and connected.
• Graph analytics can detect fraud and money laundering patterns that rule-based systems miss, especially when suspicious activity intentionally stays within “normal” thresholds.
• The Linkurious platform with Senzing Inside™ helps organizations find duplicates and discover non-obvious connections through network enrichment.
• Beyond identifying known patterns, graph technology enables the discovery of previously unknown patterns by making clusters and relationships visible.
• Entity resolution is essential for AI and analytics success – “keep calm and clean your data” should be the mantra as organizations navigate the AI revolution.

Resources Mentioned in this Episode:

• From disparate data sources to unified knowledge graphs: Introducing entity resolution in Linkurious (Webinar)
• Mule accounts and money laundering: how to eliminate blind spots in detection and investigation (Blog)
• Money mules unmasked: How to detect and investigate complex networks in minutes (Video)
• Financial crime investigations with graph analytics: Best practices from a data scientist (Blog)
• Linkurious Days 2024 recap (Blog)

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