Financial institutions and insurance firms with traditional fraud detection capabilities lose billions of dollars to fraud. Traditional approaches in detecting fraud play a critical aspect in minimizing financial losses. However, an increasing number of fraudsters have created different methods to avoid being discovered. In order to gain the upper hand again these financial institutions are need to combine the traditional subject matter expertise of an analyst with enhanced exploration and discovery capabilities enabled through a highly connected data set in a graph database.
Graph databases provide new ways of unearthing fraud rings and other high-tech scams with incredibly precision. This predictive assist, allows your company to focus on the important data necessary to uncover and halt advanced fraudulent actives in real-time. At the same time, a graph database can offer insight based on data relationships to help you create advanced fraud detection systems according to connected intelligence.
With fraudulent activity becoming more sophisticated and disconnected, enterprises have augmented their fraud-detecting capabilities with Open Native Graph Database (ONgDB) to discover fraud rings and other scams accurately and in real-time. Regardless if it’s money laundering, e-commerce fraud, or bank fraud, ONgDB aids you in detecting elements found in different fraud activities.
Not all fraud prevention schemes are perfect, but by going past data points to connections that link a network of people, places, organizations and things together, your efforts will be more focused and time valuable analyst time will be used more efficiently. ONgDB can make it possible to discover hard-to-find patterns that go beyond traditional representations. As a result, more companies have been utilizing ONgDB as the choice of database for detecting money-laundering applications and fraud.
ONgDB, unlike relational databases, stores connected data (neither linear nor hierarchical) which makes it simpler to detect rings and networks regardless of the depth and shape of data. Here are two key advantages of having ONgDB for fraud detection:
- Versatile Schema
The flexible graph model of ONgDB makes it simpler for organizations to evolve master data models that realistically represent the networks and patterns of connectedness present in the real world.
- Graph Traversal Performance
The native graph processing engine of ONgDB is optimized for high-performing graph traversals which enables sub-second network analysis of 10’s of thousands of entities to allow real-time detection of fraud activities.
Whether it’s insurance fraud, bank fraud, or other forms of fraud, two important things are clear. The first key is the importance of detecting fraud quickly so criminal activities can be halted before further damage can be done. The second is understanding the power of connected data analysis by loading all the necessary data sources into ONgDB. GraphGrid Connected Data Platform enables rapid data connection and analysis by providing all the integration, ETL and compute frameworks required to get your data sources into your connected ONgDB graph for connected data analytics and real-time recommendations for assisted fraud detection capabilities. Schedule a demo or download a fully featured freemium package today.