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National Financial Institution Identifies Increase in Average Fraud Score

Credit and debit card fraud is an expensive and never-ending reality for financial institutions. Identifying potential fraud quickly and efficiently can save financial institutions enormous amounts of time and money. However, the massive amounts of credit and debit card data that must be analyzed to monitor this fraud can potentially lead to delays in identifying fraudulent activities. Today’s story shows how Outlier helped a large financial institution to discover an upward trend in the average fraud score, as well as pinpoint the exact retail merchant that was causing the increase.  

Outlier’s Automated Business Analysis platform identified a sudden increase in the average fraud score for one of this customer’s card types. Using Outlier’s Root Cause Analysis feature, the customer was able to identify the merchant type that was causing the increased fraud score trend. Ultimately the Root Cause Analysis feature identified the exact retail merchant that was the main driver of this upward trend. Root Cause Analysis also showed that customer’s systems were mostly approving the transactions for this merchant despite the sharp increase in the fraud score for this merchant. Individually the increase in average fraud score for one merchant type was alarming for the customer, but combined with the transaction approval information, this Outlier story was immediately a top priority for this customer’s fraud team.

Outlier Discovers an Increase in the Average Fraud Score
Outlier Discovers an Increase in the Average Fraud Score

Reading How Outlier Discovers an Increase in the Average Fraud Score

Outlier identified a sudden increase in the average fraud score of a specific merchant type. The average fraud score had not wavered for several months and was consistently between two values, but then increased sharply and the increase continued for four consecutive days. The business stakeholders reacted to this story immediately due to the obvious and compelling trend above the expected range and the potential impact this trend could have on fraudulent transactions.

If Outlier had not automatically surfaced this insight, the customer would not have been aware of the unusual surges in fraud score for this specific merchant and might not have had the opportunity to immediately investigate this specific merchant for fraud and stop future fraud in real-time. This Outlier story helped the customer to know to investigate why certain transaction behaviors were occurring above the normal range. With these learnings, the customer was able to better understand new patterns of potentially fraudulent behavior for a specific merchant category.

You, too, can find meaningful trends in your business data with Outlier. Outlier empowers businesses to take a deeper look at their data and uncover unexpected trends quickly. Sign up for a custom demo to see how financial institutions rely on Outlier insights to help combat systemic fraud.