Through APIs, Sift Science’s systems collect a variety of identity and behavioral user data from its customer and run it through a machine learning-powered, automated risk assessment system. The major issues with a legacy fraud prevention approach that center on rigid rules and a team of human reviewers are high costs, inefficiency, and inaccuracy. Sift Science’s Digital Trust Platform leverages machine learning, an advanced technology that prevents payments fraud, account take-overs, content abuse, and more accurately and scalably.
Through its analyst console, Sift Science’s Digital Trust Platform enables organizations to review gray area cases that require additional analysis to determine whether or not they are fraudulent. Users are scored in a 0-100 range, with zero being trustworthy and 100 being extremely risky. This score is accompanied by human-friendly explanations that allow organizations to draw a bigger picture of who is visiting their site or app. “We compute in real time more than 16,000 signals on each piece of data, and we produce the most accurate risk assessment for our customers–akin to a detective solving a crime scene,” Tan explains.
Trust is the competitive advantage that sets apart winners and losers in a digital economy
Based on his firsthand experience of onboarding vendors as a software engineer, Tan sought to design a quick and seamless integration process. Integration guides and documentation are available on the company’s website, and dedicated Solutions Engineers are also available to help customers get up and running.
Once customers integrate with Sift Science’s APIs, they start reaping the benefits straight away. Sift Science has gained immense traction in the fraud prevention space, garnering wellknown customers including Airbnb, Open Table, Wayfair, and Zillow. One of Sift Science’s customers—a large European travel company—was using a conventional fraud prevention solution that was highly reactive and unscalable, making it a huge burden on the organization. This compelled the travel company to look for other options, and their search culminated after deploying Sift Science’s real-time, machine learning based systems. Post implementation, the company witnessed significant improvement in its operational efficiency and bottom-line.
Riding on this momentum, Sift has doubled its revenue each of the last three years, attracting the attention of investors and increasing marketplace credibility. Going forward, Sift Science is poised to keep transforming the fraud prevention ecosystem by empowering online businesses to trust their customers, grow safely into new markets, and protect themselves and their users from all types of fraud and abuse.