Partners

Paygilant partners with industry leaders to provide best-in-breed solutions that offer

great user experience while stopping fraudsters before they can do harm. We work jointly

with our partners to enhance customer value, loyalty, and bottom line results. By joining

Paygilant’s Partner’s Program, as a referral or technology partner, we offer our mutual

customers a scaleable and seamless value-added collaboration.

Partners:

A young spirit in a mature mind. A new approach to the cyber security services.
Our philosophy is “Cyber security as a business opportunity and not as a reaction to threats”. This helps customers to upgrade their business through the improvement of the Company’s reputation and resiliency.

Our teams work every day to bring the topics of Cyber Security not only into IT rooms but also in boardrooms.

Our Anti Fraud team constantly works on innovative projects to support clients in implementing effective and efficient solutions to stop frauds and guarantee a comfortable customer mobile payments experience.

We are the reference partner for Risk Analysis, Fraudulent Pattern Analysis and Policy Implementation.

www.cyberpartners.it

DataGroupIT specialize in the distribution of leading-edge IT products in the African continent for over 15 years. DataGroup IT provides vendors with one focal point for sales and presale , as well as financial transactions across Africa.

Alfa Group is a software house that operates in the market of services and solutions since 1996 and today is one of the most dynamic and innovative in the ICT sector, specializing in solutions for enterprise management and security.

United Pay Ltd is a business facilitator and innovative mobile Apps, Wallets, card/cardless and multi channel digital payment solutions enabler driving the shift from transactions to consumer interactions.

Scanovate is the Enterprise Identity and Compliance platform with a proprietary identity management solution mainly offered to top tier financial institutions and governments globally. Scanovate’s solution helps guide the flow of any digital transaction in the most secure and regulatory compliant way, ultimately orchestrating the “perfect transaction”.

Unlike other solutions on the market, Scanovate’s all-encompassing platform is a one-stop-shop for all Digital Identity and Compliance needs for enterprises looking to orchestrate simple and intuitive flows to help push business faster and more accurately. Our technology is modular and can easily adapt to preexisting ecosystems with significant time reduction.

Scanovate’s technology authenticates the integrity of the transaction by ensuring the user is who they say they are, accurately validating their identification credentials and conducting all regulatory background checks using an easy flow, giving the business the ability to get straight to work.

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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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