One of the most common reasons organizations fail to realize significant improvements in risk management after implementing custom modeling solutions can be described with one phrase: Junk in. Junk out. This article discusses best practices as it relates to data management and other factors that are shown to improve performance when it comes to custom modeling and machine learning or artificial intelligence.
It’s not just breadth of data, but also quality of data, that is important. One of the biggest misconceptions about machine learning (ML) and artificial intelligence (AI) is that you can just flip a switch and let the technology work its magic.
PayPal completes their fifth acquisition in the past 12 months, this time purchasing machine learning fraud prevention provider Simility for $120 million. PayPal COO Bill Ready says each of these five recent acquisitions are part of the company’s effort to strengthen the services they provide to merchants.
Simility was founded in 2014, they are based in Palo Alto and provide advanced risk analytic and modeling solutions for fraud prevention in the Customer Not Present (CNP) channel. Major clients include eBay/StubHub, Dick’s Sporting Goods and OfferUp. The fraud prevention provider had previously raised $25 million, including PayPal as one of their early investors.