Automated Risk Decisioning

Risk Decisioning Platform Built for Speed and Scale

SEON’s automated risk engine scores every transaction, login and onboarding event in real time. Combat evolving threats with adaptive AI and machine learning that powers fraud prevention and AML compliance.

Automate 95% of fraud checks
Deploy via API and go live in days, not months
Full decision transparency. No black-box models.

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Trusted by the World’s Most Ambitious Companies

UNDERSTAND RISK FASTER

Turn Raw Signals Into Clear Context

Get Started in Days, Not Months

Start detecting risk from day one with high-performing AI insights powered by 900+ first-party fraud signals
Combine AI insights with rules-based decisioning to catch known and emerging threats
See exactly which signals influenced the AI insights score to understand choices and accelerate confident reviews

Read about the AI insights score

Catch Emerging Threats With AI Rule Suggestions

Identify new risk patterns as they emerge with continuously updated AI models
Receive proactive AI rule suggestions tailored to evolving fraud and AML tactics
See how each suggested rule would perform, including the expected accuracy and transaction impact

Spot threats faster with AI rules suggestions

Get Immediate Insight Into Risky Signals

Spend less time digging for information and more time taking informed action
Distill complex data and decisioning with AI summaries for alerts and transactions
Quickly spot high, medium and low risk factors at a glance to clearly guide investigations
Reduce analyst misinterpretation with consistent, easy-to-understand AI summaries that explain core signals and suggest next steps

Explore AI summaries

Train Models On Real-World Results

Strengthen detection logic with every verified decision
Apply preset labeled outcomes, like synthetic identity or bonus abuse, to make models smarter, more accurate and better adept at spotting evolving risks
Ensure consistent labeling across teams to improve model learning and reduce bias in future decisions

Learn how to label transactions

Automate Your Next Move

Eliminate repetitive manual tasks so your team can focus on high-value work
Ensure faster, more consistent follow-ups after every fraud or AML decision to maintain clean audit trails
Instantly trigger actions like tagging users, assigning labels or updating statuses

Setup post-decision automations

Discover How Companies Win

“I couldn’t believe how fast the results came. Our chargeback rates dropped by 91% in one month. Moreover, the number of fraud catch rates continued to improve in the following 6 months, which is the benefit of deploying a machine learning algorithm to fine-tune accuracy.”

Gergely Kalman

CEO at Unbuffered

Read story

“We saw an immediate 25% drop in fraudulent transactions and when we layered on machine learning, we increased the accuracy of detecting fraud by an additional 15%.”

Lucrecia Vera

Partner, Solventa

Read story

See all customer stories

Learn More About Machine Learning & AI

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Fraud Detection with Machine Learning & AI

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Blackbox Machine Learning

Blackbox machine learning detects complex fraud patterns but lacks transparency, making it best paired with explainable tools like SEON.
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Whitebox Machine Learning

Whitebox machine learning enhances fraud prevention with transparent decisions, customizable rules, and improved detection.

Frequently Asked Questions

How does SEON make automated risk decisions in real time?

SEON evaluates each event using risk signals, rules, and AI-powered scoring to determine whether it should be approved, flagged for review, or declined. This helps teams automate decisions while still keeping control over how thresholds and actions are applied.

Can we customize decision logic for our own risk appetite?

Yes. In addition to built-in rules for common fraud patterns, teams can create custom rules that increase or decrease risk scores or trigger direct actions like approve, review, or decline.

What makes SEON’s risk scoring transparent?

SEON includes rule evaluations in responses and event details, and its AI Insights Score is designed to be explainable. That means analysts can see which signals influenced a score instead of relying on a black-box outcome.

How quickly can teams start using SEON for decisioning?

SEON is built for rapid deployment via API, and this page already leans into the “go live in days, not months” message. The strongest FAQ angle here is that teams can start operationalizing automated scoring quickly without a long implementation cycle.

Can SEON help reduce manual reviews?

Yes. SEON’s scoring engine is designed to automate low and high-risk decisions while routing only uncertain cases for manual review. That lets teams focus analyst time where it has the most impact.

How does SEON adapt to new fraud patterns?

SEON continuously improves detection using labeled transaction outcomes, and AI Rule Suggestions help surface new decision logic based on emerging patterns and accuracy signals from historical data.