Detecting Fraud Using Graph Algorithms

Fraud is a crime committed when someone deceives for their own personal gain. It can take many forms including identity theft, credit card fraud, securities fraud and more. It can be prosecuted as a civil wrong, criminal misdemeanor or felony. It is considered a white-collar crime since it usually involves finances and business activities.

Companies often fall victim to fraud from within their own organization, whether it is embezzlement, bribery or falsifying company records. Outsiders also defraud organizations, like vendors who demand bribes or suppliers that ship knock-off products. Technology can also be used to commit fraud, such as malware or data breaches.

A multi-layer approach to fraud detection is necessary to cover vulnerabilities in the face of smart criminals. Simple rules-based alerts can be easily bypassed by criminals, and they are constantly adapting their tactics. For instance, synthetic identities combine elements of real and false information to make it difficult to detect using traditional methods.

Graph algorithms are powerful tools that can help uncover suspicious patterns. For example, they can identify how one person is connected to another, allowing investigators to understand relationships in the network that may not be obvious. In addition, they can answer important questions such as: Are two people directly or indirectly linked? Is a specific individual a key figure in the network? By leveraging these technologies, firms can quickly identify and investigate potential fraud. This can help protect their financial assets, ensure compliance and manage loss.