Challenger Banks

Across the globe, challenger banks, neo banks, virtual banks, digital banks are mushrooming rapidly chipping away market share from traditional banks. Offering the latest in innovation and access, these new financial entities are meeting the needs of niche market segments such as immigrants, freelancers and independent contractors, women, and small businesses.

Legacy fraud prevention methods, such as user ID and password, are simply no longer effective — they are simply unreliable and uncomfortable as most challenger banks relay on mobile banking as the main transacting methodology.

Paygilant was specifically designed for mobile banking. The Paygilant solution Combines big data analytics, smart rules and machine learning that validates mobile payments instantly and transparently. Paygilant’s layered security methodology provides superior fraud protection while dramatically reduces customer friction.

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 Challenger Banks/Neo Banks

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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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