Today’s Top Fraud Fighting Solutions Are Not Created Equal: Kount vs. CyberSource
As a part of its ongoing analysis of the competitive market, Kount created a side-by-side comparison of CyberSource to Kount’s own fraud and risk management solution based on The Paypers recently published “Web Fraud Prevention & Online Authentication Market Guide” for 2017-2018. The Paypers’ report breaks down the players based on several categories, including:
- Technology Platform
- Digital Identity Verification
- Online Authentication
While each company has specific strengths and weaknesses, the chart illustrates how Kount and CyberSource compare from a feature perspective.
CyberSource is a payment management company that operates as a wholly owned subsidiary of Visa, Inc. According to The Paypers, CyberSource falls short of features in the Intelligence and Digital Identity Verification categories. Specifically, under the Intelligence category, The Paypers highlights that CyberSource does not provide its customers access to Information Sharing Network and Recovery features. The Paypers’ report also highlights that under the Digital Identify Verification Category, CyberSource was not able to offer recommended identified service features.
Outside of features, a key differential between the two solutions surrounds the ability to customize and apply domain expertise. Kount believes presenting merchants with an intuitive and customizable interface from which to make decisions is critical. Kount is focused on providing merchants with the ability to analyze fields of data associated with a merchant’s business, drill down into the pertinent data, as well as add new data (i.e., chargeback data). Another key differential surrounds the application of business rules or policies in conjunction with the analytic horsepower afforded by Kount’s patented machine learning (ML) solution. Business rules are a critical element to allowing companies to apply domain expertise and should be easy to develop, implement and manage.
Kount’s use of both supervised and unsupervised machine learningare critical technologies for identifying and mitigating fraud.
- Supervised machine learning.Supervised learning systems require training data sets to learn and use techniques like neural networks, bayesian models, regression models, decision trees, or a combination.
- Unsupervised machine learning.Unsupervised machine learning does not require outcomes, so it can learn without waiting for the three-month chargeback reporting cycle.Unsupervised learning systems rely on techniques like clustering, peer group analysis, breakpoint analysis or a combination.
The introduction of customization, domain expertise and machine learning within an enterprise fraud solution plays a large role in other parts of the merchant’s business, including the reductions of chargebacks and manual reviews.
This is illustrated in Kount’s customer Webjet, Australia and New Zealand’s leading online travel agency. Webjet’s adoption of Kount significantly reduced its chargebacks and manual reviews. Kount has allowed Webjet’s chargeback percentage to drop 98.4% lower than the industry average. This drop-in number of chargebacks and manual reviews has allowed Webjet to auto-accept a much higher number of orders and reduce the number of bookings flagged for review.
Kount’s comprehensive approach and solution set has been recognized by merchants around the world as easy to implement and easy to use. Kount’s core stack of technology was built from the ground up to maximize response time, accuracy, and information available to the merchant while minimizing downtime and integration of unique merchant data and 3rdparty data service providers.
As published in The Payper’s Web Fraud Prevention and Online Authentication Market Guide.
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