Device fraud scoring enables financial institutions to accurately evaluate the probability of a customer committing a fraudulent transaction. With this approach, financial institutions can prevent fraudulent use of credit cards and other types of loans without compromising the personal details of the customer.
The device fraud detection model analyzes dozens of data points such as IP address, email address, and device configuration to determine if a user is likely to commit fraudulent activity. These factors are given a specific weight and are added together to determine the total score for each user.
This system is extremely useful in the banking industry as it helps to identify and prevent fraudulent applications and payments before they are processed. However, vetting and screening a large number of applications is a time-consuming process and can be expensive.
Alternatively, banks can use a tool that automatically detects and classifies fraud in real-time by analyzing a user’s IP address and other device configuration data. This method is faster, less expensive and more effective than manual analysis.
Fraudsters have become increasingly sophisticated and creative in their efforts to steal money from businesses. They often resort to a variety of tactics including spoofing GPS location, identifying emulators, and other methods that hide their true identity.
A device fingerprinting solution can effectively identify these threats and help reduce cart abandonment, streamline application processes, and confidently onboard new users with low-risk devices and behaviors. The platform captures unique device information to generate a device ID that is used to track duplicate accounts and returning fraudsters.
This solution also provides a high-level fraud score that reflects a combination of multiple risk factors including IP address, device details, & a history of confirmed fraud attacks and botnet abuse. This robust risk score can help you to decrease chargebacks, carding attacks, and credential stuffing while detecting bots and automated behavior.
In addition, this solution can also identify a user’s location and proxy servers with geolocation spoofing to detect fraudulent activity in a mobile app or web site. It can also uncover device spoofing, emulators, & browser spoofing to detect fraudulent activities in the desktop environment.
With this technology, banks can automatically detect and classify fraud in real-time by analyzing dozens of data points such as IP address, emails, and device configurations to determine if a user is likely committing a fraudulent activity. These factors are given a certain weight and are added together to determine the total sum for each user.
The risk score is highly accurate and can help you to prevent fake accounts, chargebacks, credential stuffing, & bots that engage in abusive behavior. This solution is extremely easy to deploy & integrate into your system in a matter of minutes, and can help you increase audience quality by up to 15%-20%.
Similarly, this method can eliminate so-called noise due to irregular cases which are preferably not reflected in the score based on prescribed rules for removing the influence of noise. Specifically, in order to reduce the influence of noise, a number of scores for each case can be calculated using a calculation formula, and a final result of the calculation is specified from the plurality of the scores. This final result is then transmitted to the card management system 200.