Use case
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.
Financial services
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.
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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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Customers
Powering customers all over the globe
“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
“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
As new financial regulations loomed, UBS turned to Neo4j to help solve their data lineage challenges. With a data governance platform powered by Neo4j, they improved risk management by tracking information as it flows through the enterprise, monitoring data quality and discovering errors.
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The Citi Private Banking (CPB) Data, Reporting & Analytics group, with the help of Neo4j, reengineered the data flows from front-to-back to support the business strategy focusing on their global clients and enabling rapid innovation. The graph structure supports the full range of private banking operations, including security requirements, demanding party-related data.
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Leveraging Neo4j to connect their disparate data sources and manage 1 trillion data relationships, a Global 50 Bank in Latin America was able to gain real-time insights into the bank’s data, to reduce credit risk, empower decision making and identify new business opportunities.
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Keep learning
Resources
On-Demand Webinar
Graphs in banking, with AI and machine learning
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Ebook
Free eBook: Beginner’s guide to graph databases
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On-Demand Webinar
How to model financial risk with a graph database
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Visual Overview
Why intelligent applications need a graph database with granular security
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Solution Guide
How to combat money laundering using graph technology
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Developer Resource
Explore financial services developer use cases
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Developer Resource
Investment banking use cases for developers
Explore investment banking
Developer Resource
Retail banking use cases for developers
Explore retail banking
ebook
Free eBook: Beginner’s guide to graph databases
Read the ebook