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Digital Identity, Security & Online Fraud

Maxpay rolls out fraud prevention solution Covery

Thursday 9 March 2017 | 12:39 PM CET

Maxpay has announced the launch of their fraud prevention solution Covery to help merchants protect their online business. 

Covery brings together event chain analysis, feature engineering, rule management and machine learning to obtain accurate results. The solution is fully customizable and suitable for any industry, merchant business model or traffic source, claims the company.

Covery currently analyzes over 5 000 000 actions daily in both high and low-risk segment. The system only needs 0.5 seconds to complete an analysis of a customer and make a decision. Covery is scalable, so it can easily increase capacity as needed.

Covery starts gathering information about a customer’s actions as early as their first registration on the merchant’s website and analyzes the whole event chain. The analytical scheme which is applied by default to each website consists of the following steps: 1) Registration (to reveal fake accounts) – 2) Confirmation (fake accounts prevention) – 3) Login (to reveal hacked accounts) – 4) Payment (fraud prevention) – 5) Pay out - (fraud prevention). Moreover, the chain is easily customizable so the merchant can add any event or parameter in it, required by the type of their business.

More than that, the system collects and then analyzes not only the customer’s basic parameters such as e.g the card type and geolocation, but also any other aspect, including custom, aggregated and complex characteristics. Covery is able to use any information and logics used by merchants, even historical data, that can be easily uploaded right into the system and come into account during the decision-making process.

As a first step, the system verifies users through Trust list, a global database of fraudulent and reliable identifiers. Next step is to the check uses rule-based scenarios. At the end of the process, the system evaluates each customer and assigns them a score. Finally, decision-making agent checks the data obtained from all the system fundamentals, including risk policies, and makes an automated decision whether an event should be accepted, rejected or whether it requires individual attention.

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