Use Cases: Financial Services
Graph Technology in Financial Services
From risk management to securities recommendations, context is key. Find out why top banks across the globe are using Neo4j to solve their connected data challenges.
Use Cases
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Use Cases
Financial services firms today face increased regulations when it comes to risk and compliance reporting. Rather than update data manually across silos, today's leading financial organizations use Neo4j to unite data silos into a federated metadata model, enabling them to trace and analyze their data across the enterprise as well as update their models in real time. The bottom line: timeliness and accuracy in risk analytics and compliance reporting.
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Simple fraud is straightforward to detect with yesterday's traditional, discrete technologies, but today's fraudsters use sophisticated strategies that require connected link analysis and complex pattern matching. Neo4j graph technology makes such connected data analysis simple and efficient, reducing expensive false positives and adapting to new criminal patterns as they emerge.
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Increase your data's rate of return when you coalesce internal silos and external data feeds into an organizational knowledge graph. A dynamically updating knowledge graph increases productivity and revenue by serving up real-time recommendations for both analysts and customer-facing representatives. Using Neo4j's relationship-first approach to data, your financial services firm is empowered to respond to market-moving news, manage risk, analyze opportunities, predict impacts and much more.
Empower your sell-side staff and analysts with a comprehensive view of clients and their portfolios when you use a graph database. With Neo4j, you create a unified view of the customer and their journey map by drawing upon data from your product, support and sales silos. Using this 360° view, you can dynamically segment customers to provide hyper-targeted service, offerings and content.
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Being able to quickly understand relationships between data gives us the ability to rapidly interpret corporate structures and any dilution of ownership of a business. The Neo4j stack, its network of nodes and connections, mean data can be surfaced for an individual in milliseconds – that’s a very quick return of information and was the ideal fit for our needs.Paul Westcott, Program Director, Dun and Bradstreet -
The transition to Neo4j technology has at least doubled the level of service for identification of the actual owner of businesses. This allowed us to extend its use and improve the precision of the algorithm at the same time.Stefano Gatti, Innovation & Data Sources Manager, Cerved
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With a relational database, fundamental changes to your data model are highly risky and highly costly in certain instances. Ultimately, [Relational databases] just simply cannot keep up with the business. Changes come quick, and you simply cannot change quickly enough to keep up with them.Citibank
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