Banking Supervision and the Fight Against Financial Crime
European Central Bank and Deloitte rely on relationship analysis with graph technology from Neo4j .
Graph databases and graph data science uncover relationships and provide answers to questions investigators didn’t even know they should be asking. Gartner predicts that graph technology will be used in 80 per cent of data and analytics developments by 2025 – up from 10 per cent in 2021.
New approach to data analysis in banking supervision
“We are advancing banking supervision with a versatile platform for advanced network analyses based on graph technology. The platform offers a new approach to data analysis and decision-making in banking supervision. Through the innovative synergy of Neo4j graph databases and advanced network analytics, we can uncover relevant relationships and enable more effective risk assessment in the dynamic landscape of financial institutions,” says Steven Julien F. Moons, Team Leader Technologies for Banking Supervision at the European Central Bank (ECB)
Dr Andreas Burger from Deloitte adds: “Analysing relationships using graph technology is key to detecting and preventing financial crime. Specific use cases are, for example, structural analyses in connection with money laundering prevention or combating fraud. Here, the relationship structures of the parties involved are essential.”
Uncovering relationships and patterns
Neo4j is a provider of graph databases and graph analytics. The company’s technology enables comprehensive, fast and simple discovery of relationships and patterns within billions of pieces of data. Users utilise this networked data structure not only for fraud detection, but also for many other areas, from 360-degree customer views and supply chains to network management and the Industry of Things.
