Israeli anti-fraud technology powers digital banking at the foothills of the Inca empire

One thing positive coming out of Coronavirus fear has been the rush to digitize the world (although this maybe a ‘wait and see’ kind of thing), leading even the most established organizations to make their offering accessible through various digital channels. One of the main sectors to make the jump and highly benefit from it have been the finance and eCommerce industries, which have enjoyed the spike in online transactions. However, like with every highly profitable market, people will try to take advantage, and the online sphere in itself is incredibly vulnerable to fraudulent activity already.

So, how do companies ensure that the digitization momentum continues unharmed?

One company that aims to positively impact the movement is Israeli startup Paygilant, which provides a digital fraud prevention platform for finance and eCommerce industries. The company’s system deploys six proprietary intelligence sets that feed back data for analysis, which then the system turns into a “risky” or “safe” risk score in real-time.

This, according to the company, enhances the user’s journey, with a fraud prevention system in place to catch attempts even before they happen, giving high transaction enterprises an easily integrated system to ensure user privacy and proper identification even before the transaction is completed.

This reassuring capability has led the Israeli startup to form a partnership with an institution backed private bank called Banco de Comercio. The alliance will see Paygilant assist the South American banking enterprise with its digitization efforts.

“We are delighted to partner with Banco De Comercio to provide Peruvian customers the highest standards in fraud prevention and frictionless customer experience,” said CEO Ziv Cohen, who co-founded Paygilant in 2014 with over 20 years of experience in the fraud and cyber-security industries behind him.

Banco de Comercio looks to leverage Paygilant’s technology to spearhead remote financing offering by implementing the Israeli startup’s mobile fraud prevention to ensure the bank’s customers easily accessible, secure service and privacy, in addition to a smarter way for the bank to cover itself against fraudulent online activity.

“With Paygilant, we are confident that our new digital platform will let our clients transact with a high degree of security and simplicity,” said Aron Kizner, CEO of Banco De Comercio. Kizner emphasized the importance of the Paygilant platform as part of the new digital channel stating “Paygilant will be the backbone to our new digital platform focused on customer experience, simplicity and fraud prevention.”

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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