Securing Payments With Layered Protection

Retailers are constantly adapting to customers’ demand for convenient online shopping services. Demands for a friction-free user experience leads to increased fraud losses like chargebacks and costly manual reviews.

Paygilant allows retailers to be one-step ahead of fraudsters’ – stopping the dead in their tracks without interrupting the customer journey.

Preventing Digital Retail Fraud Using Layered Security

Paygilant prevents fraud before it can take place. By utilizing a layered security methodology that includes device fingerprinting, behavioral biometrics and transaction analysis, Paygilant boosts accuracy and reliability reducing false positives and improving the user experience.

Increase customer loyalty while decreasing fraud losses
Detect sophisticated fraud before the transaction occurs
Lower cart abandonment by removing the need for step-up authentication
Reduce the need for costly manual reviews with improved accuracy
Contend multiple uses cases with one solution
Easily integration using, light SDK solution
Designed for retailers whose focus is mobile payments

How Does The Paygilant Solution Work with Retailers?

Paygilant operates in the background of the app and transparently validates the customer’s identity
Paygilant analyzes the data in real-time by using its ML algorithms, to determine a trust score:

(i)“Safe” score means a trusted user is authenticated

(ii) “Risky” score, flags a fraudulent behavior

The process is completely seamless and transparent to the user to deliver a true frictionless experience and checkout completion
This process is continuous, throughout the entire user’s journey from app. launch to checkout

Download Datasheet

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.

[contact-form-7 id="1746" title="Contact+PDF_new"]

[contact-form-7 id="1390" title="Contact+PDF_copy"]

[contact-form-7 id="1397" title="Contact_copy"]