Predictive Power: Combat Fraud with Data and Analytics

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It’s easy to feel powerless in the fight against fraud. Fraudsters are more sophisticated than ever and firing on all cylinders through fraudulent activity on taxesinsurance, auto loans, credit cards and personal loans. In 2016, companies lost more than $1.6 billion due to credit card application fraud alone. This is just a fraction of the overall cost burden impacting financial institutions, government organizations, insurance companies and retailers.

Businesses are juggling several priorities: reducing fraud losses, increasing their approval rates and maintaining a frictionless customer experience. You can fight fraud rather effectively by stopping applications and account openings, but that’s not good for business. The true challenge is determining which customers are put through hurdles and which are not. It’s about identifying good customers, as well as suspicious behavior or synthetic identities. How can we solve the problem of a safe yet seamless customer experience?

Balance both sides of the equation

The key to taking your power back lies in robust data assets and predictive analytics. Data elements are the key differentiators that can empower your organization to enable a more secure and smooth customer experience.

Robust data assets include the depth and breadth of data. Depth refers to historical data, which provides insight into changes in behavior over time. The breadth of data assets is equally important–a wide range of separate data points can result in fewer false positives.

The illustration below shows the breadth of data assets spanning behaviors, real world and digital identities. Together, these elements can offer a holistic perspective.

The right data—analyzed to detect patterns of suspicious behaviors—is critical.

As illustrated, personal identity, digital identity, and transactional and reputational data, can all play an equal role in forming your fraud mitigation strategy. Based on the lifetime of data on the identity, your business can analyze consumer behaviors and establish patterns.

Only with historical, cross-channel, cross-industry and transactional data can anomalies be highlighted where they would otherwise go undetected.

Whether fraudsters employ first-party or third-party fraud methods, early detection is imperative. While today’s fraudsters may gather more data, they’re almost always unable to penetrate holistic controls, enabled by our unique combination of data elements. For example, a fraudster may have difficulty penetrating and replicating a customer’s known information, such as an IP address. Likewise, creating robust identity history is nearly impossible. Velocity patterns would be extremely difficult and time-consuming to fictitiously create without historical data.

With our mechanisms in place to view information, such as locations, devices, behaviors and scoring, you’re empowered with data across identity elements. This is your first step to gaining greater certainty and striking the right balance for your business. Historical, cross-channel data and analytics highlight discrepancies to help anticipate risks more effectively.

Detect patterns before they become your problem

Your customers are our customers. We’re actively collecting the right data to make connections and detect patterns to help you differentiate between good customers and fraudsters.

TransUnion is uniquely positioned with the right data and powerful analytics that link, analyze and interpret historical data to look for anomalies and patterns across channels and industries.

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