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The Kount Difference

Kount’s award-winning payments fraud prevention solution protects digital commerce in hyper-competitive, high-risk environments, while enabling businesses to deliver a frictionless experience to legitimate customers. Kount does not over rely on any particular aspect of fraud prevention.

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Data Network

Universal Identity Network

With comprehensive transaction and identity data, Kount enables real-time decisioning on the level of trust appropriate for the level of risk presented.

This data crosses different transaction complexities, different verticals, and different geographies so machine learning models can be properly trained to accurately predict risk. That analytical richness includes data on physical real-world and digital identities creating an integrated picture of customer behavior.

This provides merchants—regardless of industry, customer base, or geography—insights to protect against fraudulent activities.

Machine Learning

Advanced Machine Learning

Kount employs unsupervised as well as supervised machine learning models. These models lead the market in predictive ability because they are infused with 12 years of deep domain expertise and are trained on data from Kount’s vast Universal Identity Network.

To get the most out of machine learning, one has to know how to define the problems to be solved. This is where Kount’s fraud expertise comes into play, as a team of data scientists determine the most meaningful machine learning features for even the most sophisticated types of attacks and use those features to identify behavioral anomalies as well as common good behavior for a given identity or identity attribute.

Control Center

Control Center

Kount’s Control Center provides the ability to fine-tune fraud prevention decisions, conduct investigations, and monitor performance.

It enables customers to create rules and policies that meet their unique business needs (from promotions and policy abuse to non-fraud chargebacks) and customize their risk thresholds to address emerging attack methods, new use cases, and issues such as bad marketing affiliates and SKU-specific policies.

Critical tools are also available for investigation, rescoring, and reporting.

Self Service Analytics

Datamart

Self-service analytics allows for in-depth investigation into suspicious behavior as well as business performance. That learning can inform future rules and policies created within the Kount solution, but it can also provide a breadth and depth of customer knowledge.

That knowledge can lead to improved marketing activities, the introduction of new use cases, or the expansion of sales channels. The analysis possible with Datamart goes far beyond preventing fraud behaviors to providing insights into business performance.

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The Combination Is Everything

The cumulative effect of all elements makes Kount the leader in payments fraud prevention, new account fraud prevention, and account takeover protection. Kount enables over 6,500 leading brands to confidently innovate (via new digital channels and geographic markets) while precisely controlling their fraud strategy.

Payments Fraud Prevention

Learn how Kount prevents digital payment transaction fraud.

New Account Fraud Prevention

Learn how Kount prevents new account creation fraud.

Account Takeover Protection

Learn how Kount protects against account takeovers.

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The Pitfalls Of A Non-Balanced Approach

Machine Learning Alone
Machine Learning Alone

Machine learning alone doesn’t offer the flexibility and control required to successfully combat fraud while increasing sales. It often has a long time-to-value as highly-customized models can take months to reach full efficacy, and general fraud models can miss merchant-specific attack types and malicious activity that doesn’t correlate to chargebacks.

Heavy Rule And Manual Review Approaches
Heavy Rule And Manual Review Approaches

Heavy rule and manual review approaches are dependent on highly-customized rules and can take months to make an impact.  The burden to maintain these rules and update them as conditions change (whether fraud attacks or business policies) is high, and tends to result in high false positive rates, high rates of manual review and a reactive, rather than proactive strategy.