Finance & Insurance Overview
In the past, financial institutions and insurance firms traditionally used legacy systems - like relational databases - in combination with a patchwork of other tools to process and manage the massive amount of complex data. More recently, Graph databases have begun to play an essential role in solving data-driven problems for financial service and insurance companies. These firms are finding that graph systems can more easily discover financial fraud and malfeasance, provide better coverage for compliance, and more quickly analyze their transaction networks and prevent cyber threats.
Common Use Cases
Fraud Prevention & Detection
Finding fraud in financial or insurance system requires a multi-dimensional approach using millions - often billions - of data points. Unlike legacy systems, such as relational databases, graphs are designed for large, complex, connected and varied data that can discover fraud before it happens.
Decision & Impact Analysis
As data volume grows exponentially, legacy database systems are failing to meet the challenge of delivering fast, valid decision analysis in real-time. As Gartner points out, "graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions."
Pricing models today need to go beyond simple comparison, supply, and demand to deliver the best results. Graph-based systems can easily incorporate rich metadata, such as location, time, weather and connected events, to deliver dynamic pricing models and more accurate forecasts.