Financial Institutions

The world has changed. Behavior has changed and banking has changed.

The Digital transformation is upon us and banking is rapidly evolving. In this fast-moving environment, where finance and fraud intersect, there are many implications for financial institutions that are trying to stable between security, compliance and a frictionless experience.

Traditional means of detecting fraud are not keeping up with fraudsters who employ sophisticated fraud MOs. At the same time, improving the customer experience and building trust is critical for banks looking to customer loyalty growth. With less friction and less fraud, Paygilant delivers significant return on investment, making it the ideal choice for financial institutions.

Preventing Financial Fraud Using Layered Security (Including Behavioral Biometrics)

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.

Prevent sophisticated fraud before the transaction occurs
Increase customer loyalty while decreasing fraud losses
Reduce the need for costly manual reviews with improved accuracy
Contend multiple financial related uses cases with one solution
Easily integration using, light SDK solution
Designed for financial institution with digital transaction focus

Why Paygilant?

Drive growth by securing mobile transactions. Paygilant secures mobile transactions using its revolutionary SmartRisk methodology that prevents erroneous mobile traffic from infiltrating your client’s accounts.
Secures customers throughout the users journey. Paygilant ensures that your customers are confidently secured throughout the users journey from app download to continuous transaction.
Significantly reduce chargebacks and fraud losses. Minimize the charageback process, which is a lengthy, costly and annoying to all parties.
Drive growth by securing mobile transactions. Paygilant secures mobile transactions from malware, trojans and bots, and prevents erroneous mobile traffic from infiltrating your client’s accounts.
Build a trusted and loyal customer base . By accurately authenticating existing and new bank clients you can build a new (mobile only) customer base.

Paygilant Use Cases for Financial Insitutions

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Account Takeover

Account Takeover has become a common attack vector that is impacting financial institutions. Fraudsters gain unauthorized account access using credentials obtained through compromised devices, malicious personas, shared passwords, and other tricks. Paygilant advanced Smart Risk Methodology combines behavioral, biometric, device and transaction data to stitch together a continious indication whether the account has been infiltrated.

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New Account Fraud

New account fraud using breached financial identity theft has become widespread. Paygilant’s solution evaluates if a new user is behaving as a legitimate user, fraudster a machine. By flagging high-risk accounts in real time Paygilant prevents fraud before they happen.

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Transaction Fraud

Bank clients conducting mobile transactions can often be the victims of transaction fraud. Fraudsters use transaction based attacks to bypass simple device intelligence solutions by launching attacks from the user’s recognized device and authenticated session. Paygilant delivers strong device identification, behavior biometric analysis, and transaction review, while evaluating the overall security posture of the mobile device.

Overview Brochure

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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Financial Institutions

Prevent sophisticated fraud before the transaction occurs

Increase customer loyalty while decreasing fraud losses

Reduce the need for costly manual reviews with improved accuracy

Contend multiple financial related uses cases with one solution

Easily integration using, light SDK solution

Designed for financial institution with digital transaction focus