Compare Guardian Analytics Fraud Detection vs. Splunk User Behavior Analytics

Guardian Analytics Fraud Detection is ranked 14th in Fraud Detection and Prevention while Splunk User Behavior Analytics is ranked 4th in Intrusion Detection and Prevention Software with 10 reviews. Guardian Analytics Fraud Detection is rated 0, while Splunk User Behavior Analytics is rated 8.2. On the other hand, the top reviewer of Splunk User Behavior Analytics writes "Easy to configure and easy to use solution that integrates with many applications and scripts ". Guardian Analytics Fraud Detection is most compared with NICE Actimize, Splunk User Behavior Analytics and IBM Trusteer, whereas Splunk User Behavior Analytics is most compared with Darktrace, Cisco Stealthwatch and Microsoft ATA.
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Most Helpful Review
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Pricing and Cost Advice
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Pricing varies based on the packages you choose and the volume of your usage.The licensing costs is around 10,000 dollars.There are additional costs associated with the integrator.My biggest complaint is the way they do pricing... You can never know the pricing for next year. Every single time you adjust to something new, the price goes up. It's impossible to truly budget for it. It goes up constantly.I hope we can increase the free license to be more than 5 gig a day. This would help people who want to introduce a POC or a demo license for the solution.

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Ranking
Views
396
Comparisons
287
Reviews
0
Average Words per Review
0
Avg. Rating
N/A
Views
6,949
Comparisons
4,888
Reviews
10
Average Words per Review
524
Avg. Rating
8.1
Top Comparisons
Also Known As
FraudMAPCaspida, Splunk UBA
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Guardian Analytics
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Splunk
Overview

Guardian Analytics Fraud Detection is the first fraud prevention solution to use behavior-based anomaly detection. To complement our expertise in behavioral analytics, we actively study fraudsters’ techniques to make sure we’re always one step ahead. We are also the only solution that automatically and immediately protects 100 percent of users and account holders against all types of threats and schemes, and dynamically adapts to new attacks and changes in legitimate user behavior. In addition to fraudulent payments, we detect account takeover, reconnaissance, and fraud set-up activity that indicate possible criminal activity in the early stages of an attack so our customers can proactively intervene before there’s any money movement. And we do it all without rules and without disrupting users’ normal activity or process.

Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics – for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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Sample Customers
Illinois Community Bank, NEFCU8 Securities, AAA Western, AdvancedMD, Amaya, Cerner Corporation, CJ O Shopping, CloudShare, Crossroads Foundation, 7-Eleven Indonesia
Top Industries
No Data Available
VISITORS READING REVIEWS
Financial Services Firm27%
Software R&D Company16%
Insurance Company10%
Comms Service Provider10%
We monitor all Fraud Detection and Prevention reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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