Director of IT and Fraud Management at a media company with 11-50 employees
Real User
Top 10
Oct 21, 2026
I believe SEON Fraud Prevention can be improved by ensuring that the database at the back is accurate for all regions, as it is currently focused on major countries like the United States and needs a bit more improvement on Canadian data. I think it would be helpful for SEON Fraud Prevention to use authenticated sources to provide the right information.
In terms of what I disliked about SEON Fraud Prevention, I will say that it did have a learning curve. As I alluded to in the previous section, there was a little bit of a learning curve in that I really appreciated that it offered a lot of customization and personalization to users, but I feel that some of that could have been a little more hidden, just to have the bare-bones features, the meat and potatoes, a little bit more visible and clean. Because it is such a powerful tool, I think when I first started using it, it was a little overwhelming. I would say there is some room for growth in terms of condensing the UI so that what the end-user needs to see is really first and foremost, and then if they need to add additional nuances and details, they can.
I recall that SEON Fraud Prevention did not allow viewing transactions, and I was aware they were going to improve this feature because I read it on the platform that they were going to add transaction analysis. On the platform, I could only see accounts and several other things with Digital Fingerprint, but I could not see the transactions themselves. Adding transaction analysis could be a huge improvement. I believe they may have already added this feature, but I have not worked with it because I changed my job. From a features perspective, apart from transaction analysis, there could be improvements in device identification. For example, SEON Fraud Prevention shows that a customer's device is an iPhone, but which iPhone model? With Samsung, for instance, it shows Samsung A56 for one customer and Samsung A57 for another, but it does not do this as effectively with iPhone devices. Perhaps this is not SEON Fraud Prevention's problem because iPhone devices have closed networks and cannot be easily identified. However, there may be a way to work around this, though I am uncertain if one exists.
SEON Fraud Prevention is not almost 100% accurate, but some areas where it can be improved are on the learning curve. It is a tech-savvy platform and requires the users to be also tech-savvy. When paired or left in the hands of people who do not understand the emerging issues in fraud detection and AI or machine learning, they are not able to run the platform well. This may expose the company to any form of fraud and lead to big losses. Furthermore, this is a platform that suits mid-sized and large enterprises who handle more customers and who have more frequency of fraud occurring. This phenomenon locks out small business users as they do not have that high risk or do not have that high frequency of being targeted by scammers or fraudsters. Those are some of the areas where it can be improved. The initial configuration is an area for improvement. To make it secure and customizable, you have to configure it to suit your organization. Whether you are in the fintech industry, in insurance, or in a money handling business, you have that configuration period and it takes quite some time. This slows down the rate of usage of the platform.
SEON Fraud Prevention offers a robust and flexible solution, empowering businesses to combat fraudulent activities efficiently through advanced technologies and customizable features.SEON Fraud Prevention stands as a comprehensive approach for businesses seeking to mitigate fraud risks. Its advanced technologies and machine learning drive real-time insights into transactional data, allowing businesses to configure strategies tailored to specific needs. Whether combating online fraud in...
I believe SEON Fraud Prevention can be improved by ensuring that the database at the back is accurate for all regions, as it is currently focused on major countries like the United States and needs a bit more improvement on Canadian data. I think it would be helpful for SEON Fraud Prevention to use authenticated sources to provide the right information.
In terms of what I disliked about SEON Fraud Prevention, I will say that it did have a learning curve. As I alluded to in the previous section, there was a little bit of a learning curve in that I really appreciated that it offered a lot of customization and personalization to users, but I feel that some of that could have been a little more hidden, just to have the bare-bones features, the meat and potatoes, a little bit more visible and clean. Because it is such a powerful tool, I think when I first started using it, it was a little overwhelming. I would say there is some room for growth in terms of condensing the UI so that what the end-user needs to see is really first and foremost, and then if they need to add additional nuances and details, they can.
I recall that SEON Fraud Prevention did not allow viewing transactions, and I was aware they were going to improve this feature because I read it on the platform that they were going to add transaction analysis. On the platform, I could only see accounts and several other things with Digital Fingerprint, but I could not see the transactions themselves. Adding transaction analysis could be a huge improvement. I believe they may have already added this feature, but I have not worked with it because I changed my job. From a features perspective, apart from transaction analysis, there could be improvements in device identification. For example, SEON Fraud Prevention shows that a customer's device is an iPhone, but which iPhone model? With Samsung, for instance, it shows Samsung A56 for one customer and Samsung A57 for another, but it does not do this as effectively with iPhone devices. Perhaps this is not SEON Fraud Prevention's problem because iPhone devices have closed networks and cannot be easily identified. However, there may be a way to work around this, though I am uncertain if one exists.
SEON Fraud Prevention is not almost 100% accurate, but some areas where it can be improved are on the learning curve. It is a tech-savvy platform and requires the users to be also tech-savvy. When paired or left in the hands of people who do not understand the emerging issues in fraud detection and AI or machine learning, they are not able to run the platform well. This may expose the company to any form of fraud and lead to big losses. Furthermore, this is a platform that suits mid-sized and large enterprises who handle more customers and who have more frequency of fraud occurring. This phenomenon locks out small business users as they do not have that high risk or do not have that high frequency of being targeted by scammers or fraudsters. Those are some of the areas where it can be improved. The initial configuration is an area for improvement. To make it secure and customizable, you have to configure it to suit your organization. Whether you are in the fintech industry, in insurance, or in a money handling business, you have that configuration period and it takes quite some time. This slows down the rate of usage of the platform.