Enhancing fraud investigations with data and analytics

Getting to the bottom of a fraud case requires exploring the context around the case. Data can help you gain a full understanding.

When a detection system flags a customer, transaction, or behavior as suspicious, an analyst must determine if it’s an actual fraud incident, and if the case is isolated or part of a larger fraud scheme. A fraudulent transaction may be evidence of a criminal ring, or the suspicious behavior indicative of a wider scam. Getting to the bottom of the case requires exploring what the customer or behavior is connected to.

Data and fraud investigation

Gaining a full understanding of any given fraud case requires finding evidence within your data: personal data, transaction data, public data, data about businesses, and more.

Data structure has a major impact on the speed and efficiency of a fraud investigation. Many organizations use manual fraud investigation techniques, which are time-consuming and can miss key information. Finding the information to understand if wrongdoing has occurred or not is a long process when the data is scattered across different tabs. Verifying the identity of individuals connected via financial transactions, for example, requires opening a first tab with the person’s transactions and recipients. Then you need to repeat the process for each recipient.

Graph analytics for fraud investigation

Graph analytics facilitates the dynamic exploration of relationships within a large dataset. An investigator can explore and visualize who and what a client is connected to through an address, a phone number, an email, transactions, etc. Detecting accomplices becomes much faster.

Graph algorithms quickly answer many of the questions that are relevant to fraud investigations. Is one of your clients indirectly connected to a politically exposed person? A pathfinding algorithm will reveal if they are. Is a given fraudster actually part of a bigger network? A community detection algorithm can help you understand if they are, and who their accomplices might be.

Let’s look at an example of how a solution like Linkurious, based on graph analytics, can help you gain investigative efficiency. Synthetic identity fraud is when a criminal uses a mix of real and fake or stolen information to open an account or take out a loan. Here’s what that can look like: Maxine Williams and John Leahy share a phone number. John and someone called Thomas Zhao share an address. And Thomas and Maxine share an email address. It’s likely that they are part of a fraud ring using synthetic identities. Tracking down these connections manually would be very time-consuming.

Diagram showing fraud investigation with graph technology

Fraud investigation for prevention

Doing a thorough fraud investigation that gives you a complete picture of what happened helps inform risk. And better understanding fraud risk means better fraud prevention. 

Fraud investigation is part of a virtuous circle. The intelligence gathered from an investigation helps identify new patterns and criminal methodologies. And that information can in turn be used to create countermeasures to prevent the fraud from occurring again. It can also help scale fraud detection, particularly when using a graph analytics solution, in which it’s easy to create new alerts to flag new types of suspicious activity.

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