Digital Transactions Authoritative, insightful and timely information for payments professionals
DataVisor Inc., a fraud- and risk-management solutions provider, early Monday announced the launch of an artificial intelligence-based platform that measures hot spot, distinct count, and high-frequency data in real time.
The ability to measure these three data points in real time helps speeds the identification of potential fraud threats, which are becoming harder to detect as criminals’ attack vectors become more sophisticated.
Historically, tracking these data sets in real time has been a challenge because it requires advanced analytics that can process vast volumes of data without latency, which can compromise analytical accuracy, DataVisor says.
Failing to measure real-time data, such as the the distinct number of transactions at a specific merchant over the past twelve months, for example, compromises the accuracy of fraud detection and can inconvenience customers, DataVisor argues.
Hot spots are areas of unusually high activity, such as an unexpected surge in transactions from a specific region or device. Distinct counts measure the number of unique items in a dataset, such as customer identification or device fingerprints, and help identify anomalies within transaction data, as well as maintain data integrity.
Real time, granular data processing for fraud detection is essential in high-volume environments, such as large merchants or processors that handle millions of transactions per day, the Mountain View. Calif-based company says.
DataVisor’s clients include card issuers and card networks, buy now, pay later providers, crypto exchanges, and online marketplaces.
