Anti-Fraud Systems

A profit-driven cybercrime industry that uses different tools to meet its goals. Whatever, cybersecurity has remained a vital issue to our technology team. To against cybercriminals, a layered approach to security has been consistently recommended and we plan to build a security environment for our customers in the Know3here Community.To achieve this goal, seven key initiatives we have taken are as follows:

1.GEO Location Detect

Geolocation is the first fraud detection component on our feature list. When a user submits a new interact, geolocation technology pinpoints the user based on their physical location at the time of behaviors. Comparing this with the information the issuer has on file lets us verify whether the user interacted from a reasonable location. Technology is not infallible, as a user could take action while traveling outside the country. However, geolocation-based fraud detection components are helpful data nodes in the fraud scoring process.

2.IP Proxy Detect

Fraudsters can fool geolocation by using proxies to try to disguise their IP addresses. This makes it harder to flag interacts for review based on IP address or trace a fraudster’s location. One way to contend with this threat is proxy piercing technology. Proxy piercing is designed to see through a proxy address and identify the user’s actual location. This is a valuable component, but it is not foolproof. Many forms of proxy piercing can be disabled if the fraudster uses specific browser extensions to block sites from running JavaScript.

3.Device Fingerprinting

Device fingerprinting is a forensic technique used to identify each user on the device in question. This module gathers unique information based on the hardware and software installed on a device that visits the Know3here’s website or mobile App. This allows us to block devices associated with bad actors in past behaviors and keep track of trusted devices. This module can help pinpoint suspicious activity that could suggest fraud tactics like account takeover. If an established user accesses an account from an unfamiliar device, this should flag the interact for further examination.

4. Biometrics

As you know, we can take advantage of biometric technology, even in the Know3here community? It’s possible with mobile payments platforms like Apple Pay and Google Pay. These payment apps employ two-factor authentication. The user needs to use a passcode/thumbprint to unlock the device, then provide another positive ID to authorize a purchase. Plus, transactions made over mobile payments are tokenized.


Wouldn't it be great if Know3here administrator could create lists of users who are — and who aren’t — allowed to do bounty tasks in the Know3here Metaverse? As a matter of fact: Know3here can. A “blacklist” enables Know3here team to block traffic from specific users based on a variety of variables. For example, we can block all transactions from specific countries or regions known for seeing high volumes of online fraud, or those which use specific behaviors or tracks. Conversely, a “whitelist” will block all traffic except for those outlined in the parameters of the list. If we only do bounty tasks in the US and Canada, for instance, we can exclude all IP addresses based outside those territories from making tasks complete.

6.Machine Learning

With machine learning, real-time insights are fed into models based on common fraud red flags. These interacts can be rejected outright or set aside for manual review. The more data that Know3here have, the more accurate the system’s decisioning becomes over time. The system becomes more adept at detecting fraud warning signs. That means that these systems will “learn” to detect fraud faster over time, and with greater accuracy.

7.Fraud Scoring

In the end, we’re going with fraud scoring. This fraud detection component examines each interact based on multiple anti-fraud indicators and then generates a composite “score” indicating the level of risk that the exception behavior represents. Fraud scoring makes Know3here engage in very simple “up-or-down” decisioning. We can reject rewards or withdrawals that raise too many red flags automatically, or subject them to manual review. We’ll need to add dynamic rules to the process for best results.

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