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Security Authors: Ambuj Kumar, Shelly Palmer, Slavik Markovich, Elizabeth White, Greg Ness

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Fraud Detection, Financial Industry and E-Commerce | Part 1

Graph databases to the rescue

Banks and Insurance companies lose billions of dollars every year to fraud. Traditional methods of fraud detection play an important role in minimizing these losses. However increasingly sophisticated fraudsters have developed a variety of ways to elude discovery, both by working together and by leveraging various other means of constructing false identities.

Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.

While no fraud prevention measures can ever be perfect, significant opportunity for improvement can be achieved by looking beyond the individual data points, to the connections that link them. Oftentimes these connections go unnoticed until it is too late- something that is unfortunate, as these connections oftentimes hold the best clues.

Understanding the connections between data, and deriving meaning from these links, doesn't necessarily mean gathering new data. Significant insights can be drawn from one's existing data, simply by reframing the problem and looking at it in a new way: as a graph.

Unlike most other ways of looking at data, graphs were designed to express relatedness. Graph databases can uncover patterns that are difficult to detect using traditional representations such as tables. An increasing number of companies are using graph databases to solve a variety of connected data problems, including fraud detection.

This series of blogs discusses some of the common patterns that appear in three of the most damaging types of fraud: first-party bank fraud, insurance fraud, and e-commerce fraud. While these are three entirely different types of fraud, they all hold one very important thing in common: the deception relies upon layers of indirection that can be uncovered through connected analysis. In each of these examples, graph databases offer a significant opportunity to augment one's existing methods of fraud detection, making evasion substantially more difficult.

(Next, we'll look at some of the ways fraudsters work to defraud billions of dollars from US banks. How do they do it? How come they are not systematically caught? And what can be done to make sure that they are stopped in their tracks??)

More Stories By Gorka Sadowski

Gorka is a natural born entrepreneur with a deep understanding of Technology, IT Security and how these create value in the Marketplace. He is today offering innovative European startups the opportunity to benefit from the Silicon Valley ecosystem accelerators. Gorka spent the last 20 years initiating, building and growing businesses that provide technology solutions to the Industry. From General Manager Spain, Italy and Portugal for LogLogic, defining Next Generation Log Management and Security Forensics, to Director Unisys France, bringing Cloud Security service offerings to the market, from Director of Emerging Technologies at NetScreen, defining Next Generation Firewall, to Director of Performance Engineering at INS, removing WAN and Internet bottlenecks, Gorka has always been involved in innovative Technology and IT Security solutions, creating successful Business Units within established Groups and helping launch breakthrough startups such as KOLA Kids OnLine America, a social network for safe computing for children, SourceFire, a leading network security solution provider, or Ibixis, a boutique European business accelerator.