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BioCatch vs Featurespace ARIC Fraud Hub comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

BioCatch
Ranking in Fraud Detection and Prevention
4th
Average Rating
8.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Featurespace ARIC Fraud Hub
Ranking in Fraud Detection and Prevention
10th
Average Rating
9.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Fraud Detection and Prevention category, the mindshare of BioCatch is 8.9%, up from 6.8% compared to the previous year. The mindshare of Featurespace ARIC Fraud Hub is 4.9%, up from 4.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Fraud Detection and Prevention
 

Featured Reviews

reviewer1464606 - PeerSpot reviewer
Stable with good behavioral biometrics and great technical support
BioCatch is one of the fraud detection tools which also has machine learning capabilities and it has what is called a machine learning model feature. It is run in the background. The consequence of those machine models is it is complex to perform data functions and the activity and programming techniques. The decision-making for determining what's happening within those models is a little bit complex and not at all transparent. It's not easy for businesses to understand how the model is using the data of the bank customers in order to come to the assumption it does. All of these things are background technologies and the business may not understand what's happening in the background. The customer will never know what tools are being used to monitor the fraud at all, however, the business manager should certainly be interested in knowing how this model is working. People in banks are very particular when it comes to approving these models, as they have to be accountable to the regulators on the other side. They need to understand and explain what customer data is being consumed, why it's being consumed and if it's consumption is endangering any privacy rights. There needs to be clarity in terms of how much anonymization of the data happens before BioCatch comes in. I might have a gap in knowledge, and the solution may have been updated since I used it in December of last year.
Luis Inclan - PeerSpot reviewer
A flexible solution with a quick to navigate interface
The rule-writing language could be improved to make it more understandable. I was familiar with the Falcon expert language to write rules, so I had to get used to the new language used in this solution. In the next release, as an additional feature, it will be good to have the capability to sort and play visual effects in the fields. This will help to distinguish each record from the other. For example, we currently have a bar that shows if a person has a high or low score. If the score is 35%, the bar is considered low, and if the score moves to 99%, the bar increases. However, we don't have the capability as interface users to make this function appear in other fields where we want it displayed. We have only the score field, which is pre-configured with this functionality.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"It can track mouse movements as well as the actual oriental moments of such as the movement of devices, how they are held, and the angles which at they are held. All these are captured for customers and a behavioral profile is built for the customer over a period of time. This would be matched against any fraudulent behavior. If, for example, suddenly a customer account seems to be accessed by our profile, which is not one particular customer account, if the movements or habits are suspect, we can catch the fraud and shut it down."
"The most valuable feature is its zero degradation model. You don't have to train the model every three to six months, and it automatically functions."
 

Cons

"BioCatch is one of the fraud detection tools which also has machine learning capabilities and it has what is called a machine learning model feature. It is run in the background. The consequence of those machine models is it is complex to perform data functions and the activity and programming techniques. The decision-making for determining what's happening within those models is a little bit complex and not at all transparent. It's not easy for businesses to understand how the model is using the data of the bank customers in order to come to the assumption it does."
"The rule-writing language could be improved to make it more understandable."
 

Pricing and Cost Advice

Information not available
"The pricing is reasonable. It is not cheap, but it is fair."
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Top Industries

By visitors reading reviews
Financial Services Firm
60%
Computer Software Company
7%
Manufacturing Company
4%
Logistics Company
3%
Financial Services Firm
45%
Computer Software Company
9%
Manufacturing Company
6%
Non Profit
4%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Also Known As

No data available
ARIC Fraud Hub, ARIC platform
 

Overview

 

Sample Customers

Information Not Available
TSYS, OpenBet, William Hill, Zapp, Credit Reference Agency, Responsible Gambling Trust, Betfair, kPMG, Camelot
Find out what your peers are saying about NICE, ThreatMetrix, FICO and others in Fraud Detection and Prevention. Updated: October 2024.
816,406 professionals have used our research since 2012.