Cookie Policy - Paygilant

Paygilant Cookie Policy

Last updated on Dec. 10, 2018.

This Cookie Policy explains how Paygilant, Ltd. uses cookies and similar technologies to recognize you when you visit our website (“Website”) and use our services (“Services”). It explains what these technologies are and why we use them, as well as your rights to control our use of them.

WHAT

A cookie is a small file containing a string of characters that is sent to your computer when you visit a website. When you visit the site again, the cookie allows that site to recognize your browser. Cookies may store user preferences and other information.

Cookies provide a convenience feature to save you time, or tell the Web server that you have returned to a specific page. For example, if you personalize pages on our Website, or register for our Services, a cookie helps us to recall your specific information on subsequent visits. When you return to the same Website, the information you previously provided can be retrieved, so you can easily use the customized features.

Cookies set by the website (in this case, Paygilant) are called “first party cookies”. Cookies set by parties other than the website owner are called “third party cookies”. Third party cookies enable third party features or functionality to be provided on or through the website (e.g. analytics). The parties that set these third party cookies can recognise your computer both when it visits the website in question and also when it visits certain other websites.

WHY

We use first party and third party cookies for several reasons. Some cookies are required for technical reasons in order for our Website to operate, and we refer to these as “essential” or “strictly necessary” cookies. Other cookies also enable us to track and target the interests of our users to enhance the experience on our Website and Services. For example, Paygilant keeps track of the Website and pages you visit within Paygilant, in order to determine what portion of the Paygilant Website or Services is the most popular or most used. This data is used to deliver customized content and promotions within the Paygilant Website and Services to customers whose behavior indicates that they are interested in a particular subject area. Third parties serve cookies through our Website for advertising, analytics and other purposes.

CONTROL

You have the right to decide whether to accept or reject cookies. You can set or amend your web browser controls to accept or refuse cookies. If you choose to reject cookies you may still use our website, though your access to some functionality and areas of our website may be restricted. Since the means by which you are able to refuse cookies through your web browser controls can vary from browser-to-browser, we recommend that you visit your browser’s help menu for more information.

Essential website cookies

Because these cookies are strictly necessary to deliver the Website to you, you cannot refuse them. You can, however, block or delete them by changing your browser settings.

CHANGES

We may update this Cookie Policy from time to time in order to reflect changes to the cookies we use or for other operational, legal or regulatory reasons. Please re-visit this Cookie Policy regularly to stay informed about our use of cookies and related technologies.

The date at the top of this Cookie Policy indicates when it was last updated.

CONTACT

Have questions or concerns about our Cookie Policy? Contact us at info@Paygilant.com.

Device Attributes

Various attributes observed on the device can contribute to the detection of fraudulent behavior and to the derivation of a device ID. Device model, screen, memory, UUID, OS, IP, geolocation, app permissions, and more are observed. Geolocation is probably the best example for a device attribute used for detecting fraud - if a transaction is attempted from Moscow a short while after the preceding transaction was carried out in New York, then that is a strong indicator of fraud. Restricted app permissions is another indicator that the user might be hiding something. Another example is app permissions that are restricted by the user – that, in combination with other things, night suggest that the user might be hiding something.

Transaction Data

Paygilant employs propriety transaction behavioral maps. The Behavioral Maps represent the purchasing patterns/behavior of a specific customer and her nearest neighbors and are created using Paygilant's proprietary machine learning algorithms. The behavioral maps typically comprise a large amount of information but must be compact 9 enough since they are securely transmitted to the mobile device. To achieve this Paygilant utilizes its depth of field (DOF) approach from digital photography to compress the information so that complex calculations that do not require work intensive CPU and memory. A Behavioral Map shows a clear, high resolution picture of the different risk zones and is a key factor in determining the risk of a specific transaction and has the following key characteristics: - User specific: each map is unique, calculated and maintained on a per user basis, therefore representing a transaction risk level for each customer’s transaction. - Lightweight: Resolution variations enable maintaining only the necessary data, reducing the map's weight to a bare minimum. - Dynamic: As the purchase behavior changes, the map will be modified.

Device DNA

Bio Markers

Paygilant observes bio markers to passively identify the user behind the transaction. Common bio markers Paygilant observes include touch time, time between touches, size of touch inputs, finger velocity, scrolling pace and drag length, typing biometrics, and more. Paygilant’s robust bio markers are just one of the several intelligence sets that make up the broader solution and is designed to augment the fraud/no-fraud decision that precedes any step-up authentication request.

App Interactions

Paygilant looks at how the user interacts with the mobile application to determine if the interactions are consistent with a legitimate user. For example, if a user navigates directly to a high-ticket item and immediately proceeds to check-out, then that suggests something fraudulent might be happening. If a user inputs his name and address on the payment form in a manner that is not consistent with how normal users would do it (i.e. slower than expected because typing-in unfamiliar strings), then that provides another clue that something fraudulent might be happening.

User Data

Intelligent, privacy preserving analysis of user data on the mobile device provides valuable insights into fraudulent activities. User data analysis is especially helpful in hard-to-analyze scenarios like new account origination, where there is no established history for the user/account. Some examples for how device data can be used include comparing user accounts on the device with the payment cardholder identity, or the identity disclosed on a new account registration form – a mismatch provides a strong indicator for fraud. No media on the device, empty contacts list, and sparse call logs are also examples of fraud indicators that can be collected from user data on the device.

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