Hudson Data and LendingPoint announced that they are partnering to create an industry solution to prevent synthetic identity fraud using powerful graph machine learning. "Synthetic fraud is an industry problem and requires an industry solution in our view," said Houman Motaharian, President of LendingPoint. "The particularity of this fraud activity is that synthetic IDs have been created seven to 15 years before the occurrence of actual default. We encourage and invite credit issuers to explore this solution and help make this platform an industry best-in-class solution." LendingPoint will leverage Hudson Data's newly released ML Graph software platform that allows users to easily create and analyze graph structures from relational databases. "We feel that Hudson Data's innovative approach to graph analytics using relational databases will allow us to significantly increase our ability to capture synthetic IDs," added Motaharian.
In an industry where many financial institutions are experiencing a dramatic increase in credit write-offs due to synthetic ID fraud, traditional underwriting models consistently fail to detect more sophisticated fraud techniques, and credit write-offs skyrocket. "We need to consider data across three dimensions: time, bureaus, and lenders," said Hudson Data CEO Menish Gupta. Adding, "Building graphs combining data across bureaus and customer's lifecycle is the most effective way to reveal emergent behavior." The companies will be showcasing their solution at LendIt Fintech USA 2019 this week in San Francisco. For more information, contact email@example.com or firstname.lastname@example.org.