A quick note before we start: everything below about SEON comes from public sources (their website, press coverage, and product documentation) as of July 2026. Vendors update pricing, features, and positioning often, so check with SEON directly for their current details.
Sanction Scanner is the stronger fit if you need a full AML program on one AI-native platform: sanctions and PEP screening, transaction monitoring, fraud detection, and customer risk scoring sharing the same models and case data. SEON makes sense mainly when the leading problem is fraud at signup and during transactions, where its digital footprint and device intelligence do a job that sits before and outside a traditional AML program.
Sanction Scanner and SEON both sell software that helps regulated businesses fight financial crime, but they come at the problem from different starting points. We built our platform around screening, transaction monitoring, and customer risk scoring as one connected system, with AI running underneath all of it. SEON started in fraud prevention, built its engine around digital footprints and device signals, and has been extending into AML and transaction monitoring from that base, now positioning itself as an "AI command center" for fraud and AML.
The comparison comes down to scope. Do you need one platform that covers a full regulatory AML program, or is your primary problem fraud and account risk at onboarding, with AML as a secondary layer? This page walks through both.
What is Sanction Scanner?
We're an AI-native AML and financial crime platform built for medium and enterprise banks, neobanks, payment and e-money institutions, fintechs, crypto businesses, insurers, and investment firms. "AI-native" here just means the AI isn't sitting on top of one feature. It runs through the whole system: screening, monitoring, fraud, and risk scoring all pull from the same models and the same case data, instead of being separate tools stitched together.
Our platform, Fusion, brings AML and name screening, transaction monitoring, transaction screening, fraud detection, and customer risk assessment into a single workspace, so an analyst working a case doesn't have to jump between systems to see the full picture on a customer. We also cover know your business checks and ongoing monitoring for customers whose risk profile can change after onboarding.
On the data side, we screen against sanctions, PEP, and adverse media lists pulled from more than 3,000 sources across 220+ countries, refreshed roughly every 15 minutes. Our AI-assisted matching is built to cut down false positives, which matters because false positives are what usually drown compliance teams. One neobank using our platform cut false positives by 70%, detailed in our case studies. Beyond matching, we use AI agents to summarize cases, analyze customer profiles, and support analyst decisions, so the AI isn't just scoring a transaction, it's helping write up why.
Integration typically takes hours rather than weeks, our API averages around 250ms response time with 99.95%+ uptime, and we support two-way webhooks. The platform includes no-code dashboards so compliance teams can adjust rules without waiting on engineering. We hold ISO 27001 and ISO 9001 certifications, are GDPR compliant, run on Azure, and were named a G2 Leader for Summer 2026. Over 800 clients use the platform today, including BMW, Stellantis, Generali, Zurich, Delivery Hero, QNB, Kuveyt Türk, iyzico, and UNOPS.
What is SEON?
SEON was founded in Budapest in 2017 by Tamás Kádár and Bence Jendruszák, who started the company after dealing with fraud at their own crypto business. That origin shows in the product. The company now operates from Austin, London, Budapest, and Singapore, and says thousands of companies worldwide use its platform.
SEON's specialty is digital footprint analysis. The moment someone types an email, phone number, or IP address into a form, SEON pulls together a picture of that identity across the open web, often before any formal KYC step starts. Paired with device intelligence and fingerprinting, that gives a fraud detection layer aimed at account takeovers, bonus abuse, and synthetic identities at a stage where AML tools aren't the relevant instrument yet. SEON reports more than 900 real-time, first-party risk signals feeding its scoring engine.
On AI, SEON has been active. Its portfolio includes AI-assisted rule creation, an AML screening analysis capability, automated case summaries, and regulatory report generation, and in June 2026 it launched an MCP server that lets analysts connect external AI tools like Claude, ChatGPT, or Gemini directly to SEON's data layer, alongside Network Detection and an AI chart builder. The direction is clear from their own framing: SEON wants to be the data and signals layer that any AI tool can sit on top of, in their CEO's words a "headless" model where analysts work in whichever AI tool they prefer.
The compliance side is the newer part. SEON's AML transaction monitoring and screening modules exist and support real regulatory obligations like BSA, FinCEN, and AMLD filings, but they're an extension of a fraud-first platform, not the foundation it was built on. For a team whose mandate is a full-scope AML program, that distinction matters. For a team whose problem is fraud at the door, it may not.
Feature comparison
| Area | Sanction Scanner | SEON |
| AI architecture | AI-native across every module: screening, monitoring, fraud, risk scoring share the same engine and case data | AI core to fraud scoring and digital footprint analysis; AML/screening capabilities are a newer layer; MCP server opens data to external AI tools |
| Sanctions, PEP, adverse media screening | Core, built first: 3,000+ sources, 220+ countries, ~15 minute refresh | Available as part of the AML suite; screening sits alongside fraud tools rather than as the original core |
| Transaction monitoring | Full AML transaction monitoring and transaction screening built for regulatory reporting | Real-time transaction monitoring unifying fraud and AML signals, with SAR/CTR filing support |
| Fraud detection | Included as one module among several, sharing data with screening and risk scoring | Primary strength; real-time fraud scoring using 900+ first-party signals |
| Digital footprint / device intelligence | Not a specialty; risk scoring focuses on entity and transaction data | Core specialty: email, phone, IP, and device fingerprinting analysis, often pre-KYC |
| KYB / customer risk assessment | Dedicated KYB and customer risk assessment modules | Customer risk factored into fraud and AML scoring, less emphasis on standalone KYB |
| Automation | No-code dashboards, AI case summaries and decision support built into the platform's own decisioning | AI-assisted rule creation, case summaries, report generation; agentic workflows via MCP server (June 2026) |
| API and integration time | Typically hours; ~250ms average response, 99.95%+ uptime | Documented API and SDKs; timelines vary by deployment, confirm with SEON |
| Data coverage and refresh | 3,000+ watchlist and sanctions sources, refreshed roughly every 15 minutes, figures published | 900+ real-time signals across identity, device, behavioral, and AML data; AML data refresh cadence not independently published |
| No-code usability | No-code dashboards for rule and workflow changes | Natural-language rule and chart building for analysts |
| Certifications | ISO 27001, ISO 9001, GDPR compliant, runs on Azure | Security and compliance certifications listed on their site; confirm current scope with SEON |
| Pricing | Custom quote based on modules and volume; no public price list | Plan-based pricing; confirm current tiers with SEON |
| Target segment | Medium to enterprise banks, neobanks, payments, fintech, crypto, insurance, investment firms | Fintechs, iGaming, e-commerce, and digital businesses where fraud and onboarding risk lead |
| Support | Dedicated account and implementation support | Support through standard channels; tier and SLA details vary by plan |
Which one when?
If your organization needs one platform to run sanctions, PEP, and adverse media screening, transaction monitoring, fraud detection, and customer risk scoring together, with AI shared across all of it instead of living in one corner of the product, we're the stronger fit. That's especially true if you're a bank, payment institution, or fintech under real regulatory obligation to show a coherent AML program, not just fraud controls. Having screening, monitoring, and case management in one place also matters more as headcount and transaction volume grow, since analysts stop losing time moving between systems. And the AI works inside the platform's own decisioning; you don't need to route case data out to an external AI tool to get analysis back.
SEON is the practical choice for a different job. If your most pressing daily problem is stopping fraud at signup and during transactions, and the signals you need are digital footprints, device fingerprints, and behavioral risk before a customer is even fully onboarded, that pre-KYC layer is the job SEON was built for, and it's a job an AML platform doesn't do. E-commerce platforms, iGaming operators, and consumer fintechs where onboarding fraud is the biggest headache, and full AML program depth is a secondary concern, fit that profile.
Some teams end up needing both layers at different points as they scale: fraud signals at the front door, a full AML program behind it. The honest starting question is which problem is more urgent for you right now.
The bottom line
The two platforms answer different questions. SEON answers "who is this person typing into my form, and are they about to defraud me," using digital footprints and device signals at a stage before AML tools engage. We answer "how do I run a complete AML program in one place," with screening, transaction monitoring, fraud detection, and customer risk scoring on one AI-native platform. If your question is the second one, request a demo or talk to our sales team and we'll walk through it against your own use case.
