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| Feb. 11, 2021 | We thank Ekata for advertising with us.
Case Study: Analyzing False Credit-card Declines
Ecommerce merchants looking to reduce fraud routinely decline credit-card transactions from legitimate, honest customers.
But not with Ekata's global fraud-prevention platform.
By analyzing billions of worldwide transactions, Ekata instantly assigns risk scores at checkout based on a customer's identity and actions. The two scores, combined with real-time machine learning, dramatically reduce false declines.
In its latest case study, Ekata applied the scores — Identity Risk and Identify Network — to historical transactions of a new client. The analysis was eye-popping.
Download the case study to learn: The high cost of false credit-card declines; How Identity Risk and Identify Network scores dramatically lower false declines. Ekata's before-and-after analysis of false declines of a new client. The results of A/B testing to gauge the client's risk tolerance. DOWNLOAD THE CASE STUDY
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