Paygilant – Homepage

Frictionless and Effortless Fraud Prevention for Challenger Banks

What You Get Out Of The Box ?

How The Paygilant Solution Works ?

Every Step in The Users Journey
is Protected by Paygilant

Every step in the users journey is an opportunity for a fraudster to commit fraud. Fake accounts, credential compromising, SIM-SWAP, payment fraud and other are generated before a user completes a transaction.

Paygilant protects the user at multiple checkpoints to determine if a criminal activity is occurring and stops fraud long before the actual transaction occurs.

Paygilant protects against all kind of digital fraud providing your clients a safe, frictionless transaction experience.

What Our Customers Say

“We selected Paygilant because it is designed for the financial industry and we believe that they actively meet the security challenges that exist today”

Fernando Araya, CEO of Tenpo (Financial Fintech, Chile)

“Paygilant was a perfect fit for our needs, as its solution demonstrated superb fraud prevention capabilities”.

(Yon Moreira, CEO of Surf Bank (Challenger Bank, Brazil

“The Paygilant team had a very solid idea that took minor design tuning to provide a strong solution using an adaptive model of customer behavior to detect and handle fraud risk. This is extremely valuable to Citi "

Dr. Matthew Yuschik, Global Consumer R&D group at Citi

”As a key player in India’s transformation into a cashless society, it is our responsibility to create a safe environment for our customers. Paygilant’s on-device mobile payment fraud prevention technology, with its ability to detect fraud in the pre-transaction stage, gives us this capability.“

Vinay Kalantri, The Mobile Wallet founder and CEO

Updated Resources

NEWS

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Helping Fintech Companies Stay Secure: Paygilant’s CEO interview with PIA

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New Datasheet - Paygilant for Challenger Banks

Paygilant is the first vendor to pioneer a true frictionless, layered mobile fraud prevention solution, designed for challenger banks . Learn how Paygilant can help challenger banks prevent fraud and ensure frictionless authentication, while boosting profits.

Download

New White Paper - Beyond Behavioral Biometrics

When it comes to fraud prevention and authentication, behavioral biometrics provides is a single, static form of intelligence attainment. So why not maximize your intelligence sets?

Download

Paygilant TV

A brief demo presentation of how Paygilant works. The demo shows how Paygilant distinguishes between a legitimate user and a fraudster.

ABOUT US

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SOLUTION

Paygilant Solution For Retail

Paygilant Solution For Financial Institutions

RESOURCES

Still Have Questions?

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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