Sift Science: Detecting Online Fraud

Online Fraud: A $16 billion Issue

Online fraud and account takeover is a growing problem for retailers, financial institutions, online merchants, and payment providers.  Estimates say the problem may cost retailers and consumers more than $16 billion annually.  Security breaches, such as the one at Yahoo, among others, have led to leakage of millions of login/password data records which can be leveraged by fraudsters to access online wallets and other accounts that are linked to stored payment information. This is extremely dangerous because it is difficult to distinguish between legitimate and fraudulent online transactions. To combat the perpetually growing problem, San Francisco-based Sift Science, launched in 2011 with a mission to ‘make online experiences faster, smoother, and safer using the smartest technology around’, is attempting to solve the problem through pattern recognition and machine intelligence. 
More and more businesses worldwide are relying on Sift Science to automate fraud prevention, grow revenues, or slash costs. Their cloud-based machine learning platform is powered by 16000+ fraud signals receiving real time updates from a global network of 6000+ websites and apps and allows them to provide 10X results compared to other solutions. For instance, Sift Science has helped Opentable, an online restaurant reservation company,  improve detection accuracy by 200%.

Sift Science: Leading Fraud Detection

Apps worldwide send records of key activities in real-time to Sift Science using javascript snippets and APIs. Key activities include when: orders are submitted, user accounts are created, users login/logout, items are added to the shopping cart, user-generated content is submitted, or when messages are sent.  Sift Science aggregates a database of these activities together with third party data such as social media profiles, email domains, or IP geolocation. Fraudulent signal features and patterns are identified, taking into account unique fraud signals by industry or business type. Algorithms factor in the relevant features and calculate a Sift Score, which is effectively a probability that the user or transaction is fraudulent. In partnership with Sift Science, clients set Sift Score thresholds to block risky users and transactions. The solution is particularly useful in instant-fulfillment ecommerce applications.

Sift currently works with many small to midsize companies in the online retail space such as Yelp, AirBnB, Wayfair, HotelTonight, etc and has demonstrated commercial viability in this market. The company currently competes with others on a variety of dimensions. There are old fraud prevention systems such as CyberSource by Visa or Accertify by American Express, other cybersecurity vendors that include credit card as one of many services including Palantir and SAS Institute, fraud prevention models through payment providers such as Braintree, and other fraud prevention startups such as Riskified and Feedzai. Traditional fraud prevention technologies are rules based which Sift Science improves upon by using real time machine learning models trained on each customer’s data to pinpoint which factors related to a transaction are most likely to be fraud. An important part of the Sift Science system is that it gets better over time with the help of a fraud analyst or other employee who helps to train the system by identifying false positives and false negatives. The company says that they have an average false positive rate of only about 20% as compared to an industry average of about 80%.

While Sift currently seems to be focusing on customers that are online only, other startup competitors such as Feedzai are able to implement multi-channel fraud solutions which means that Sift will face competition when trying to expand to markets involving physical locations. Similarly, Riskified focuses on international transactions which could be another hindrance to Sift Science’s expansion plans. It is likely that there will be consolidation in this space over time, as these companies are using similar technologies and strategies to target niche parts of the market.

Potential Improvements

One idea for improving the Sift product is to build a platform that allows both customers and non-customers to share data.  Currently, Sift customers benefit from the fraud detection algorithms built using their own customer data and other generic attributes, but they cannot identify if a customer’s identity has been compromised outside of the Sift network.  By allowing other entities such as banks or credit card companies to share information, they could develop a more comprehensive view of customers and potentially catch fraud earlier.  If the data sharing is reciprocal, the groups contributing data would also benefit by getting access to more information.  This type of agreement is already in place at many banks in the United States, and could be a competitive differentiation for Sift if structured correctly.

 

Team: Cyborbs

Members: Alisha Marfatia
, Paul Meier, 

Sakshi Jain, Scott Fullman, 

Shreeranjani Krishnamoorthy

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