In this series of blogs we will expose how fraudsters operate, we will run through several typical fraud scenarios, we will investigate where, how and why legacy detection solutions fall short, and what can be done to improve them.
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.