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Jul 23rd, 2016 Comments Off on How Artificial Intelligence Predicts And Prevents Online Fraud

How Artificial Intelligence Predicts And Prevents Online Fraud

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There are certain types of sites and apps like digital gift card business, money transfer services and on-demand market places that are frequent victims of fraud, phishing and identity theft. The online crimes including personal and corporate data breaches, credit card fraud, etc. are reported to the US Internet Crime Complaint Center (IC3). Victims of online crimes have lost an aggregate amount of $1.07 billion but IC3 believes the amount goes beyond what is reported.

It seems that cybercriminals have adopted new tactics that is why Sift Science has made the effort to raise $30 million to fund machine learning systems and artificial intelligence. Sift’s machine learning systems will automatically determine whether the attempted transaction or interaction with an online business is authentic or problematic. The system constantly tracks normal behavior online as well aberrant behavior that is strongly connected to criminal activity. Most of the early clients of Sift Science are companies involved in ecommerce and retail that want to avoid charge backs resulting from fraud.

According to Sift CEO Jason Tan, ecommerce companies used to rely on rules and rigid systems that attempt to flag potential fraudsters but sometimes it flags even good customers and users. Flagged cases often require a manual review before a company authorizes or denies a purchase, redemption of a gift card or other transactions.

The goal of Sift Science is to ensure that every corner of the internet is safe without becoming annoying and inefficient like those security checks in the airport. Sift Science wants to protect more than 6,000 online sites and apps that include Airbnb, Yelp, Indeed, Zillow, Match.com, Twilio, OpenTable and Wayfair.
Furthermore, Sift Science aims to use its capital to support expansion beyond online retail and ecommerce. At present Sift Science employs 60 full time staff and plans to add 30 more by next year so that fraud can be predicted and prevented.

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