To reduce or better prevent the economical and reputational repercussions of fraud, many organizations make their traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals or individual devices. Increasingly sophisticated fraudsters have developed a variety of ways to escape detection by forming fraud rings comprised of stolen and synthetic identities.
While the discrete approach is a good way to capture fraudsters acting alone, it falls short when it comes to detecting fraud rings.
To uncover such fraud rings, it is essential to look beyond individual data points to the connections that link them together, discover integrated behaviours and relationships.
This can be done with a graph database.
Unlike most other ways of looking at data, graphs are designed to express relatedness and uncover patterns that are difficult to detect using traditional representations such as tables.
Larus chose Neo4j, the world’s leading native graph database. As the #1 Neo4j solution partner, we have integrated Neo4j to augment existing fraud detection capabilities of our customers. We detect and combat, in real-time, a variety of financial crimes including first-party bank fraud, credit card fraud, e-commerce fraud, insurance fraud and money laundering.
Neo4j is being used by multiple Fortune 100 financial services firms, major government institutions, and top-tier retailers for connected graph analysis. AIG, Citigroup, Credit Agricole, China Zheshang Bank (CZ Bank), ING, Société Générale, Thomson Reuters and UBS are among Neo4j’s numerous financial services clients, applying Neo4j’s graph technology across a wide range of risk management use cases.
Learn how to get started fighting fraud with graph technologies and request information about our free seminars. To show you the power of graph databases for your enterprise fraud detection system, we offer you two-hour free seminars and interactive sessions at your location, in English or Italian language.
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