According to the company, the new solution helps businesses grow revenue by identifying good or trustworthy customers. In addition, it reduces friction for existing trustworthy customers by eliminating time consuming and frustrating step-up challenges.
Furthermore, iovationScore is designed to reduce fraud prevention costs by minimizing resource-intensive manual reviews and eliminating the need for expensive verification tools for trusted transactions.
“Battling with fraudsters is like a game of chess. Once you think you have them figured out, they utilize a whole new approach,” said Scott Olson, Vice President of Product, iovation.
“It is essential that you have a machine learning weapon in your arsenal that continually and automatically adapts to changing conditions seen across companies and industries worldwide. Besides stopping fraud, that solution should provide an easy way to differentiate good from bad customers in order to offer the valued ones special incentives.”
“The launch of iovationScore contains 12 years of fraud behavior insight we have assimilated from 23 billion online transactions worldwide,” said Scott Waddell, CTO, iovation.
“While machine learning is the real-time engine that powers iovationScore, the big data insights from our collective history of transactions are the fuel that makes it so powerful right off the starting line.”
The company explains that iovationScore’s analytics can examine hundreds of behavioral, contextual, device and transactional attributes from billions of transactions worldwide. Its algorithms are trained by analyzing 30 million fraud records placed by iovation’s network of over 3,500 fraud and security analysts.
The solution is available to select existing iovation customers today and the company will plans to make it more broadly available this November. iovation presented the new service at its annual Fraud Force Summit being held recently in Portland, Ore.
