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

Problem Outline

Knewton was founded in 2008 by Jose Ferreira, a former executive at Kaplan, Inc, to allow schools, publishers, and developers to provide adaptive learning for every student. Knewton believes that no two students are alike in their background or learning styles and education needs to be altered to cater to every child’s strengths and weaknesses. Knewton draws on each student’s history, on interests of students with similar learning styles, and on decades of research on improving learning experiences, to recommend the next best course/activity for the student to maximise his/her learning. By doing so, Knewton has helped Arizona State University (among others) increase pass rates by 17%, reduce course withdrawal rates by 56%, and accelerated learning as 45% of the students finished a course 4 weeks early.

Solution

Knewton utilizes adaptive learning technology to create a platform that allows educational institutions and software publishers to tailor educational content for personal use. Started as a an online test prep software, Knewton now aims to identify the next best step in the user’s learning journey. By partnering with leading universities in the US and publishers like Pearson, the adaptive learning platform aims to end the one-size fits all curriculum making personalized curriculum accessible across K-12 and college education. Knewton’s solution offers a two pronged approach on curriculum recommendation guiding students on what the next best thing to learn and how they should do it. The recommendations can be used to drive the complete learning experience or can serve as tailored remediations in response to test performances.

This is achieved through data. Once a student logs in on the platform, every keypress and mouse movement is recorded as a part of the clickstream to understand their behaviour.

The adaptive learning algorithm then uses this data to understand different dimensions of the learning experience such as engagement, proficiency, boredom and frustration measured through time spent on learning modules, error rates, assessments taken etc. For instance, Knewton uses the item response theory to assess and compare proficiency based on an individual’s responses to quizzes as compared the overall test taker’s demographic.

Evaluate Effectiveness and Commercial Promise

Knewton has chosen to place itself as an adaptive learning platform that partners with educational content providers to create personalized learning experiences. Their partners include Houghton-Mifflin, Pearson, and Triumph Learning, which has given them considerable weight in the US market. In addition, they have served 13 million students worldwide through their platform, as they’ve also targeted developing markets where there are less structural education initiatives that need to be overcome. Finally, Knewton has also been working on creating partnerships with MOOC’s as well as universities. Results reported by Knewton on their partnership with Arizona University in developmental math courses show that pass rates increased by 11 percentage points while withdrawal rates decreased by 50%.

Knewton’s competitors in the adaptive learning space include Kidaptive, McGraw-Hill Education, Smart Sparrow, an Australian based company, Dreambox Learning, and Desire2Learn among others. While each competitor has its own set of results and wins, it is notable that Smart Sparrow has reported reducing failure rates from 31% to 7% in a mechanics course and they are also working with Arizona State University. So while Knewton has seen promising results from its platform and while they do have a lot of traction, competitors are able to get similar if not better results. One pseudo competitor that Knewton could think about partnering with would be alternative schools, such as AltSchool, as charter schools and alternative methods of education become increasingly popular. This would give them another avenue to leverage their platform while also giving them an edge over current competitors.

Proposed Alterations

• Where students sit in a classroom
  • Knewton is using Engagement modeling to determine how engaged virtual students are. The same methodology could be extended to the classroom.
  • Using photo sensors, Knewton could incorporate classroom seating location into their analytics. Perhaps it could be determined whether a student learning is affected by where they sit in a classroom relative to the teacher and other students.
• Integration with standardized testing
  • The Knewton adaptive ontology can be used to better understand student preparedness for standardized testing,  and the effectiveness of standardized testing. Particularly the assessment and prerequisiteness relationships, which provide a view on student understanding of concepts requiring understanding of previous concepts.
  • The Knewton tool could help standardized test developers prove that the concepts intended to be tested are indeed those being tested. It could also help student prepare for the test.
• Integration with student loan underwriters
  • Results at Arizona State University indicate significant improvements in withdrawal rates. Non-completion of degree programs is the leading cause of student loan defaults.  Knewton insights could be used as an indicator of student loan default risk.
  • Data privacy may be an issue at the individual level.

Team: Cyborbs

Members: Alisha Marfatia
, Paul Meier, 

Sakshi Jain, Scott Fullman, 

Shreeranjani Krishnamoorthy