Detecting Fraud
from Every Angle

With Paygilant there’s no need to wait for the money to disappear. Thanks to our patented 6 Intelligence Sets we can detect potential fraud from every possible angle before the transaction occurs throughout the user’s journey, reducing friction in your customer’s experience.

Paygilant’s Technology

Device DNA

The next generation of device fingerprinting, overcoming the constant policy hardening applied by Google and Apple, creating a unique ID for each device, indicating on a suspicious device, observing whether it has been manipulated. Thanks to this we have the upper hand to fight fraud even before we consider who the user is.

User Space

Observing the device’s environment gives important insights indicating on a suspicious device and allowing us to recognize a genuine returning customer, avoiding adding friction to their user experience.

Activity Map

A unique flow of the user’s navigation is mapped determining a pattern of consistent interaction with the app, allowing us to observe whether the actions taken are aligned with those of the legitimate user.

Bio Markers

There are no two people alike. With this intelligence layer, unique behavioral biometric attributes are observed, such as touch velocity, intervals, finger size, scrolling pace, gestures and more.

App Insights

By correlating internal and external information we can validate user’s identities as data becomes available by the application.

Transaction View

Paygilant’s patented transaction behavioral maps analyze the transaction spending habit of each user, viewing unique details and how it relates to the user’s personal patterns, allowing us to have a deep understanding of whether a certain action is risky or safe.

Find out why Paygilant is the best fraud prevention solution for you

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Paygilant’s Protection Throughout the User’s Journey

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

Paygilant continuously reviews the user activity at multiple checkpoints throughout the user’s journey, to determine if fraudulent activity is taking place. Thus preventing fraud long before the actual transaction is completed.

Paygilant Performance Insights Dashboard

Fraud Monitoring Analytics

Apply a risk-based approach to your fraud programs.
Easily connect to internal and external data sources and automate analysis for continuous monitoring.
Apply advanced analytics and machine learning techniques to identify trends and high-risk activities.

Advanced Fraud Analysis

Full and detailed view of each transaction analyzed
Meaningful overall insights and flexible reporting
Transparency of the risk calculation

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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Detecting Fraudfrom Every Angle

Paygilant’s Technology

Device DNA

User Space

Activity Map

Bio Markers

App Insights

Transaction View

Find out why Paygilant is the best fraud prevention solution for you

Paygilant’s Protection Throughout the User’s Journey

Paygilant Performance Insights Dashboard

Fraud Monitoring Analytics

Advanced Fraud Analysis

Device Attributes

Transaction Data

Device DNA

Bio Markers

App Interactions

User Data

Detecting Fraud
from Every Angle