Velocity checks in eCommerce fraud prevention are a crucial strategy used to detect and mitigate fraudulent activities.
These checks involve monitoring and analyzing the frequency and rate of transactions or specific behaviors performed by a user within a given timeframe. By establishing thresholds for various activities, such as the number of transactions a single account or IP address can perform within an hour, velocity checks help identify anomalous patterns that might indicate fraudulent behavior.
For example, if a user tries to make multiple high-value purchases in rapid succession, this could trigger a velocity check alert. This method allows merchants to quickly respond to potential fraud, preventing the completion of suspicious transactions and protecting both the consumer and the business against potential losses and fraudulent exploitation.
How Do Velocity Checks Work?
Velocity check technology operates by utilizing complex algorithms and machine learning models to continuously monitor transaction data in real-time.
As transactions occur, the system evaluates the speed and frequency against a pre-set criterion or threshold designed to flag unusual behavior. These criteria can include parameters like the number of transactions per user in a minute or the total amount spent by an account within a short period. When a transaction rate exceeds these predefined limits, the technology generates alerts for further investigation.
This automation allows businesses to efficiently detect potential fraud patterns without manual intervention, significantly enhancing their ability to react swiftly and effectively to block or verify transactions before any damage occurs. The integration of real-time data analytics with adaptive learning models ensures the velocity check systems remain robust and responsive to evolving fraud tactics.
How to Deploy Velocity Checks
Fraud detection systems with velocity checks usually follow a set procedure:
- Identify Relevant Data: Providers can gather historical transaction data in various ways, but leveraging machine learning is most effective for velocity checking. This technology enables rapid identification and presentation of crucial information, forming the basis for developing your velocity rules.
- Setting Up Velocity Rules: A broad range of data points, time periods, and user information can be utilized based on the insights obtained in the initial step.
- Implementing Velocity Rules: Once the rules are devised by the merchant or provider, the system conducts ongoing velocity checks to evaluate user activities.
- Responding to Triggers: When the circumstances detailed in the first and second steps necessitate a velocity response, a predefined action is initiated. For example, declining a transaction, marking the user as high risk, or requesting identity verification.
Are Velocity Checks Susceptible to False Positives?
While velocity checks are an effective tool for detecting fraud, they may also generate false positives. False positives occur when a legitimate transaction is flagged as fraudulent and declined or blocked. This can be frustrating for both the customer and the business, potentially resulting in loss of revenue and damaged customer relationships.
To minimize false positives, it is important to continuously monitor and adjust velocity rules. Utilizing machine learning algorithms that adapt to changing fraud patterns can also help reduce the number of false positives generated by velocity checks.
Additionally, businesses should have a clear process in place for handling false positives. This could include providing customers with a way to quickly verify their identity or contact support to resolve the issue.