Tenpo selects Paygilant to Provide Chileans Robust Digital Fraud Prevention and Enhanced Customer Experience

TEL AVIV, Israel, Sept. 30, 2020 /PRNewswire/ — In an effort to provide maximum security to its users, Tenpo has recently closed an alliance with Paygilant, a world leader in authentication and prevention of digital fraud. Paygilant will secure Tenpo’s signature protection system, while improving the user’s experience.

Tenpo is the first and only fintech prepaid card issuer in Chile, which provides its clients with bill payments, transfers and deposits, top-up prepaid services and withdrawals. Tenpo selected the Paygilant solution because it provides robust mobile fraud prevention, without impacting the user’s experience.

“We selected Paygilant because it is designed for the financial industry and we believe that they actively meet the security challenges that exist today,” said Fernando Araya, CEO of Tenpo. Araya stressed the importance of the Paygilant platform stating that it “Enables us to offer the highest security standards to our clients, maintaining ourselves as leaders in fintech financial technological development. The new forms of account fraud are a reality, so we must be prepared, without affecting the experience of our clients. Financial institutions must provide powerful protection signals to their clients against possible threats.”

Paygilant‘s solution is designed for fintech that requires fraud prevention, without compromising the user’s experience. Paygilant enables financial applications like Tenpo to attain customers’ trust, accelerate growth and prevent money loss. “We are honored to be trusted by such an innovative and forward-thinking organization,” said Ziv Cohen, CEO of Paygilant.

Paygilant supports Tenpo’s mission to redefine the LATAM financial services market, providing a secure, complete, seamless customer experience.

About Paygilant

Paygilant is a revolutionary frictionless digital banking and payments anti-fraud company. Its solution is designed to eliminate the trade-off between strong fraud prevention, frictionless authentication, and user privacy.

For more information, you can visit: https://www.paygilant.com/

About Tenpo

Tenpo was established with the notion of democratizing finance through technology. Regulated by the Commission for the Financial Market (CMF), the same entity that oversees the large traditional banks in Chile, Tenpo seeks to transform the financial environment into a more efficient and fairer system. The Tenpo application allows customers to manage their finances from their mobile devices quickly, easily, and safely.

Through the Tenpo platform, a customer can make transactions abroad, make phone recharges and pay bills for all kinds of basic services.

For more information, you can visit: https://tenpo.cl/

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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Tenpo selects Paygilant to Provide Chileans Robust Digital Fraud Prevention and Enhanced Customer Experience

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