Behavioral Analytics Platform Uses Largest Library of Industry Specific Machine Learning Models to Predict, Detect, Prevent and Respond to Fraud in Real-Time
Gurucul, a leader in behavior based security and fraud analytics technology, announced it was recognized as a Representative Vendor in Gartner’s 2019 Market Guide for Online Fraud Detection1. Gartner acknowledged Gurucul as a solution for Fraud Analytics.
.@Gurucul named a representative vendor in #Gartner 2019 Market Guide for #OnlineFraud Detection for ability to predict, detect, prevent and respond to fraud in real-time
According to Gartner, one of the factors driving the continued growth of the online fraud detection market is a, “Maturing of fraud strategies among retailers and financial institutions to not simply carry out risk assessment at the point of payment, but also to assess risk throughout the customer journey. While not all institutions have achieved this level of capability maturity in their fraud detection strategies, it is being actively explored and considered. It is also driving demand in the OFD space for vendors specializing in services that can be applied across the entire customer journey, such as behavioral analytics.”
Unlike online fraud detection platforms that rely primarily on static rule-based detection, with machine learning as a separate solution component or that use machine learning to optimize rule sets and proactively suggest new, more effective rules, Gurucul relies exclusively on machine learning with open analytics to create and manage any customer specific fraud models, in addition to a large out-of-the-box models library.
Gurucul ingests huge volumes of data generated by users/entities across multiple channels from on-premise and cloud applications to identify anomalous and potentially fraudulent behavior that spans time, place, devices, access and transaction actions. The Gurucul Fraud Analytics risk engine continuously scores this activity against historical behavior, static and dynamically created peer groups, and third party intelligence to generate risk prioritized alerts for investigation.
The risk score can also be used to automate remediation responses by enforcing security policies or making real-time business decisions to prevent fraud before it occurs. For example, if identity theft or account takeover is suspected, the corresponding user account can be blocked or challenged with step-up authentication. In the case of suspicious third-party funds transfers associated with money mule fraud, a hold can be placed on the account to prevent the transaction from being executed.
“We believe being cited as a Representative Vendor in the Gartner Market Guide for Online Fraud Detection underscores the importance of behavioral analytics in detecting and preventing online and cross-channel fraud,” said Nilesh Dherange, CTO of Gurucul. “We further believe our ability to predict, detect, prevent and respond to fraud in real-time across a variety of use cases is the primary reason why Gurucul has been deployed in the financial services, payments, insurance and retail sectors.”
(1) Gartner, Inc. “Market Guide for Online Fraud Detection” by Jonathan Care and Akif Khan, 30 April 2019.
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