Graph technology is formed around the idea of building databases using mathematical graph theory to store data and the links between data as relationships. While the underlying math is quite complex, the most important thing is that graph databases emphasize the connections between data as much as the individual data points by explicitly storing those connections as relationships.
Unlike other database management systems (DBMS), relationships take first priority in graph databases. In the graph world, connected data is equally (or more) important than individual data points.
This connections-first approach to data means relationships and connections are persisted (and not just temporarily calculated) through every part of the data lifecycle: from idea, to design in a logical model, to implementation in a physical model, to operation using a query language and to persistence within a scalable, reliable database system.
According to Gartner, the application of graph processing and graph database management systems will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.
Graph technology will grow due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using traditional database technologies.
Other important factors of graph databases growth include excellent real-time big data mining with visualizations of results and the adoption of Artificial Intelligence (AI)-based graph database tools and services.
Banks have been among the pioneers of this technology. Today 20 of the top 25 banks use graph database software, for instance, in a wide variety of use cases from fraud detection, cash flow analysis to transaction analysis, for responding to cyber threats and ensuring compliance.
Furthermore, using graph databases banks can establish a 360-degree view of their customers in order to evolve their customer engagement strategies, to personalize product offerings and to incorporate retention strategies.
Top financial firms are also using such asset graphs to perform derivatives pricing in real-time, where the pricing formula needs to take into account the many interdependencies between items and accurately reflect the risk/reward ratio.
Financial services firms are also using graph databases internally as a useful part of the way they run their own environments, by ‘graphing’ their data centres, networks and other IT architecture (creating graph maps of their topologies). Such use cases include everything from using graphs to help with dependency management, impact analysis, network management, downtime reduction, root-cause analysis and routing, and quality-of-service mapping.
The primary benefits of leveraging big data analytics in banking are:
- Improving program data governance, data-quality awareness and data lineage.
- Enhancing Detection and Prevention of Fraud & Money Laundering
- Implementing Superior Risk Assessment
- Improving Customer Experience with a 360-Degree View and increasing customer engagement
Leading banks like JPMorgan Chase, Citi and UBS rely on Neo4j for data lineage, customer 360, regulatory compliance and fraud & money laundering.
As the #1 Neo4j solution partner, Larus helps Banks redesign their data architectures in order to address their data Key Challenges.
Discover how graph technology helps your financial services firm overcome emerging challenges in the industry ➡ contact us at email@example.com