Sold by: SEON
Deployed on AWS
SEON is a fraud prevention platform that helps risk teams stop payment fraud, account takeover and new account fraud before they happen. Its API-first fraud detection software analyzes each customer's unique digital and social footprint in real time, scoring risk through a transparent machine learning model you can audit. Teams onboard users, run activity monitoring at login and transaction monitoring on every payment, scaling operations without the false positives of blackbox systems.
4.6
Overview
Fraud teams lose time and money to account takeover, payment fraud, chargebacks and fake signups that slip past stale, resold data and rigid blackbox tools.
SEON's fraud detection and prevention solution scores risk from first-party data it collects in real time across hundreds of online and social sources, not third-party data it resells. That means sharper detection and fewer false positives.
From the first signup input, SEON enriches email, phone, IP and device data to filter bots, synthetic identities and fraud rings before KYC, then keeps scoring continuously across login, in-life activity and every payment.
Every decision is transparent. Analysts see which signals drove each score, tune their own rules without engineering, and move from rules-based to AI-driven detection with no blackbox.
Deployed as SaaS on AWS through a single API, SEON goes live in days, with industry-specific configurations and an optional onboarding team to guide setup. SEON is SOC 2, ISO 27001, PCI DSS, GDPR and CCPA compliant.
Highlights
- Account takeover prevention: detect credential stuffing and takeover attempts in real time, before accounts are drained.
- Payment fraud and chargeback prevention: score device, IP and behavioral risk on every transaction, including friendly fraud.
- New account and synthetic identity fraud: enrich email, phone, IP and device data to block fake signups.
Details
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Features and programs
Buyer guide
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month | Overage cost |
|---|---|---|---|
Starter Subscription | **Only available through Private Offer** SEON Platform & Admin Panel access; 2,500 Fraud checks/month; Max 10 users & 50 custom rules | $699.00 | |
Premium Subscription | **Only available through Private Offer** SEON Platform & Admin panel access; Unlimited users & custom rules; Case Management for up to 2 users | $2,999.00 |
Dimensions summary
You choose between two subscription tiers, both billed as a contract and available only through a Private Offer. Both give you access to the SEON Platform and Admin Panel. The tiers scale by capacity limits. The Starter Subscription caps you at 2,500 fraud checks per month, up to 10 users, and 50 custom rules. The Premium Subscription removes those limits, giving you unlimited users and custom rules, plus Case Management for up to 2 users. Pricing scales with the volume and features your team needs rather than by per-unit usage.
Top-of-mind questions for buyers
A fraud check is one API call that scores a user or transaction. Each call enriches data from email, phone, IP, and device signals to return a risk decision. Under Starter, you get 2,500 such calls per month. Premium removes this cap with unlimited API calls.
Starter caps you at 2,500 fraud checks monthly, 10 users, and 50 custom rules. Growing past these means moving to the Premium Subscription, which removes those caps. The change is not automatic; you switch tiers through a Private Offer to gain unlimited users, rules, and API calls.
Yes. Premium adds Case Management for up to 2 users, plus AML compliance features. Case Management centralizes alerts, transactions, and risk signals into structured cases for investigation. Starter does not include Case Management. This feature difference, along with capacity caps, is what separates the two tiers.
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Speak with an expert to discuss your project needs. For support, contact us at support@seon.io
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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By Arkose Labs
Top
10
In Analytic Platforms, Financial Services
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Real-time Risk Scoring
Analyzes digital and social footprint data in real time across hundreds of online and social sources to score risk through a transparent machine learning model.
Multi-stage Fraud Detection
Detects and prevents account takeover, payment fraud, new account fraud, and synthetic identities across signup, login, in-life activity, and transaction monitoring stages.
Data Enrichment and Verification
Enriches and validates email, phone, IP, and device data to filter bots, synthetic identities, and fraud rings before KYC processes.
Transparent and Auditable Decision Logic
Provides visibility into which signals drove each risk score, allowing analysts to tune rules without engineering involvement and transition from rules-based to AI-driven detection.
API-first Architecture with Compliance
Deployed as SaaS on AWS through a single API with SOC 2, ISO 27001, PCI DSS, GDPR, and CCPA compliance certifications.
AI-Powered Threat Detection
Patented AI-powered platform that detects, traps, and neutralizes bots and bad actors before they can impact transactions
Real-Time Threat Intelligence
Tracks and shares real-time, global threat intelligence with customers to provide proactive threat awareness
AWS Ecosystem Integration
100% compatible with AWS Services including CloudFront and WAFv2 Classic with no-code deployment offering low latency, high stability, and scalability
Multi-Vector Attack Protection
Protects against account registration fraud, login attacks, business model abuse, in-platform abuse, and phishing attacks including credential stuffing, account takeover, web scraping, API abuse, and man-in-the-middle attacks
Service Level Agreement Guarantee
Offers 100% SLA guarantee with commercial protection against automated attacks and rapid deployment within three weeks
Device Fingerprinting and Global ID
Patented next-generation device identification creating unique fingerprints based on hardware and software parameters, tracking devices across sessions, and sharing fraud intelligence across clients globally
Behavioral Analytics
AI-powered user behavior analysis covering typing dynamics, touchscreen interactions, device motion, app usage patterns, and geoprofiling to detect anomalies in real time
Fraud Matrix Framework
Proprietary cyber-fraud kill chain mapping fraudster tools, tactics, and procedures across nine stages: Reconnaissance, Resource Development, Abuse of Trust, Faux Communications, Credential Compromise, Account Access, Defense Evasion, Fraud Event, and Monetization
Customizable Rule Engine
No-code rule builder enabling fraud teams to create, test, and deploy custom detection rules aligned with risk appetite and regulatory requirements
Explainable AI
Transparent risk scoring with clear explanations of contributing factors enabling auditability and trust in fraud detection decisions
Standard contract
No
No
Customer reviews
Gaia D.
Simple, intuitive, and with a really clear interface
Reviewed on Oct 26, 2026
Review provided by G2
What do you like best about the product?
It's easy to use and really intuitive, with a clear interface that makes everything immediate.
What do you dislike about the product?
The reporting is really very difficult to create and extract.
What problems is the product solving and how is that benefiting you?
It is helping us a lot to identify cases of ATO FRAUD
Eman A.
SEON Review: A Powerful Tool for Fraud Detection and Investigation
Reviewed on Oct 25, 2026
Review provided by G2
What do you like best about the product?
I like how SEON combines different digital signals into one place, making it easier to assess risk, identify suspicious patterns, and investigate potential fraud. I also appreciate the flexibility of its rules and the transparency of risk scores, which help us understand why transactions are flagged or declined and make more informed decisions.
What do you dislike about the product?
Some data points and customer attributes are not always available or clearly displayed, which can make investigations more challenging. Improving data visibility, simplifying navigation, and providing clearer explanations of certain risk signals and rule triggers would make the platform more efficient for daily fraud investigations.
What problems is the product solving and how is that benefiting you?
SEON helps us detect and prevent fraudulent transactions by analyzing digital signals, identifying suspicious connections between users and devices, and highlighting unusual behavior patterns. This helps me investigate cases more efficiently, identify potential fraud patterns earlier, make more informed decisions, and reduce the time spent manually reviewing transactions.
Matt H.
Powerful Trend Detection That Helps Fight Bad Actors and Cut Bonus Abuse
Reviewed on Oct 23, 2026
Review provided by G2
What do you like best about the product?
A genuine help in the fight against bad actors, recognising trends and patterns that aren’t identifiable to the human eye and offering tremendous support in reducing bonus-abuse costs.
What do you dislike about the product?
Manual elements are required when registering reviews, such as adding labels and tags. However, I believe this process could be simplified by making greater use of SEON’s AI learning capabilities to automate more of it.
What problems is the product solving and how is that benefiting you?
Reduces fraudulent and bad-actor registrations. It improves conversion metrics and lowers onboarding costs further down the funnel, while also fundamentally reducing bonus-abuse costs.
reviewer2914851
Real-time insights have reduced fraud losses and protect member accounts effectively
Reviewed on Oct 21, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for SEON Fraud Prevention is real-time fraud detection and monitoring.
A specific example of how I use SEON Fraud Prevention for real-time fraud detection and monitoring is that loyalty transactions passing through the system are monitored through SEON Fraud Prevention and scored for accuracy and suspicious activity.
I also have additional insights about how I'm using SEON Fraud Prevention, as it is focused on account information and account validation.
How has it helped my organization?
SEON Fraud Prevention has positively impacted my organization by helping control the fraud rate in a meaningful way that safeguards brand reputation and member accounts.
I can share specific outcomes or metrics, as it has helped in both reducing the fraud rate and improving productivity.
What is most valuable?
In my experience, the best features that SEON Fraud Prevention offers include excellent performance, real-time responses, and a user interface that is easy to navigate and for analysts to learn.
Regarding real-time responses, they usually average about 100 to 200 milliseconds, which I consider a very good response rate.
SEON Fraud Prevention is great in releasing new features based on available technology, such as making the tool AI ready and adding meaningful AI features within the tool itself.
I think SEON Fraud Prevention's governance and security is strong, as it has well-protected data that helps ensure the data is kept securely for the appropriate amount of time with retention policies in place.
Based on the data that SEON Fraud Prevention receives, it is able to accurately provide an output for every response.
What needs improvement?
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.
For how long have I used the solution?
I have been using SEON Fraud Prevention for about two and a half years.
What do I think about the stability of the solution?
I find SEON Fraud Prevention stable.
What do I think about the scalability of the solution?
SEON Fraud Prevention's scalability is quite good so far; we have been able to increase the load on SEON Fraud Prevention and it has not been an issue.
How are customer service and support?
The customer support for SEON Fraud Prevention varies based on the request; if there is a request that requires feature development, it gets transferred to the engineering team, which can take some time given that there is already a backlog there, but for quick requests, the support team is very fast, and I usually receive a response back within an hour.
I would rate the customer support of SEON Fraud Prevention a nine on a scale of 1 to 10.
Which solution did I use previously and why did I switch?
We were manual before this solution for fraud detection, so it was more about manual efforts from analysts to investigate known cases and suspicious cases.
What was our ROI?
I have seen a return on investment, as there have been savings on fraud loss.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing involved negotiations, but overall, I believe it has a good pricing model that can cater to different organizations.
Which other solutions did I evaluate?
I evaluated other options before choosing SEON Fraud Prevention, but I am unable to disclose the specifics.
What other advice do I have?
On a scale of 1 to 10, I would rate SEON Fraud Prevention a nine.
I gave it a nine because the basic functionality for fraud prevention is very well articulated, able to provide a seamless experience, and address organizational needs with feature developments.
My advice to others looking into using SEON Fraud Prevention is to make sure that the events that you feed into SEON Fraud Prevention are meaningful and fit for purpose; if you give it a lot of garbage data, then SEON Fraud Prevention will not be able to provide a good response back.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Marisa Levy
Fraud scoring has boosted confident decisions and has automated risk reviews for investors
Reviewed on Oct 21, 2026
Review provided by PeerSpot
What is our primary use case?
My use cases for SEON Fraud Prevention involve working at a FinTech platform that was primarily B2C, focusing on the investor side of the business, where individuals are putting money on the platform to invest in real estate. I was using SEON Fraud Prevention in order to get some additional customer data on user accounts. Most of what I was using it for was if there was an action taken by a customer that was potentially suspicious; it would give me additional data on other factors about that user.
So maybe I was not sure if this was a legitimate user, and then I would go and pull up their SEON Fraud Prevention profile and see that there was no internet history beyond the previous two weeks, or that the IP address was located somewhere other than where they were located. If there were a bunch of red flags on the SEON Fraud Prevention profile, it would give me some additional data to say, 'Okay, maybe this is an account we don't want to service,' or, 'Maybe this is an account that we put into a certain state that essentially they still have the account, but they're a little more limited.' Versus maybe there was something that was potentially suspicious, but then when I looked at the SEON Fraud Prevention profile, I saw that they had this email address for a decade, that the IP address and where they were saying they were from were the same. It could help bolster my confidence that this was a false positive.
How has it helped my organization?
Concerning how SEON Fraud Prevention has helped my fraud detection efforts, I think it really increased my confidence. There were situations where there was a lot of gray area, and it helped me feel more secure in whatever the decision was because it gave more data and more evidence to support, 'Hey, this is the decision I think we should make here and here's why.' The other metric point I could probably speak to is that I do think it decreased a lot of the lag time in terms of making a decision because it was less back and forth with stakeholders. It was more being able to present to them, 'Hey, this user had a fraud score of X, whatever it is. We saw X, Y, and Z behaviors. Here's the reason why they had that fraud score.' It cut down a lot of that back and forth and noise, and I think from that perspective, it was super useful.
What is most valuable?
Regarding favorite features of SEON Fraud Prevention, I really appreciate that it gives a numerical value to the fraud score because then it is a single value that I can dig into further and see why that was the case. It helps boil down all the noise into one factor. The other thing that I would say goes hand-in-hand with it is that SEON Fraud Prevention made it really easy for us to programmatize and automate a lot of what we were previously doing on our own. For example, if a user reached a certain threshold of a fraud score, it would automatically put them into that status on our end that we wanted. It was called Review, but it would put them into that status without requiring us to manually look through workflows. It gave us a lot of data and peace of mind, as well as being fairly straightforward to use.
Regarding the machine learning algorithms, I used some of the machine learning rules in SEON Fraud Prevention. Some of them I think were just not really helpful, but there were others that I do think made a positive difference and were helpful to have.
I have used the Advanced Reporting tools in SEON Fraud Prevention. Basically, I would look at things like fraud score or data at the user level, and also just looking and seeing how many people are being put into which status over a certain period of time.
As for the multi-factor authentication, I would say it probably benefited security in some way. We had some other in-house solutions, and we had two-factor authentication for all of our systems, so I am not sure if that really made that big of a difference.
What needs improvement?
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.
For how long have I used the solution?
I used SEON Fraud Prevention for about a year.
What do I think about the stability of the solution?
I have no stability issues with SEON Fraud Prevention that I can recall.
What do I think about the scalability of the solution?
In terms of scalability, I would say that SEON Fraud Prevention seemed very scalable with the breadth and depth of the functions and features. There was a lot of room for where I could really change things to suit my use case.
How are customer service and support?
Regarding tech support, I remember that during onboarding, we talked to the implementation team, and there were probably a couple of instances where I needed some additional support, but I do not remember any specific details.
Which solution did I use previously and why did I switch?
Regarding alternatives, that is a little bit tricky for me. The only thing I could really compare SEON Fraud Prevention to is that we had been doing a lot of this on our own. It was all in-house made, and it was not nearly as robust as the SEON Fraud Prevention solution. That is the only thing I can really compare it to, and it is not really quite an apples-to-apples comparison because, as I said, it was not as robust. That is why we had onboarded a product such as SEON Fraud Prevention, because we just did not have those tools.
How was the initial setup?
About the initial deployment of SEON Fraud Prevention, from what I can remember, it seemed pretty easy. I do not remember a whole lot of difficulty or delays with our launch.
What other advice do I have?
As for maintenance, I know that there were some tweaks and things that we definitely changed as we realized more and more what we wanted with SEON Fraud Prevention. But in terms of maintenance, I think it was pretty easy.
For an overall score for SEON Fraud Prevention, I would say probably about a nine. I think the only reason I am docking a point is that the UI overall can be a little bit difficult to navigate and parse out. If they were to boost that a little bit, I think there is potential for a ten score.