Resources
Stop emerging risk signals before they damage trust or undermine your reputation - using real-time behavioural analytics and continual monitoring
A complete, end-to-end view of risk: identify, screen and monitor behaviour. From onboarding and screening to continuous monitoring, now including live behavioural signals from social media.
Continuous fraud monitoring detects harmful activity, from fake accounts and scam rings to counterfeit goods and fraudulent reviews. Behavioural analytics and AI identify emerging risks in real-time, replacing manual reviews with automated detection that protects users, revenue and brand trust.
Identify scam activity, fake accounts and coordinated abuse before fraud spreads across your platform or customer base.
Use behavioural analytics and network clustering to uncover linked accounts and organised fraud rings.
Monitor behaviour in real time to surface emerging fraud patterns as they develop, not after damage is done.
Provide clear signals and supporting evidence to enable fast, confident enforcement and remediation decisions.
Leverage data from our repository of known bad actors to weed out familiar fraudsters fast.
Classify harmful threats putting users at risk - from fake accounts to scams and
fake reviews.
Comprehensive risk scoring together with human readable labelling enables efficient decision making.
Identify patterns of bad behaviour as they emerge to understand what’s happening on your platform.
Free up resources by maximising automation and focusing your teams’ efforts where they can have the most impact.
Providing continuous monitoring to identify patterns of bad behaviour as they emerge.
Analyse key reputational factors across IPs, emails and our repository of known bad actors. If there are fraudsters on your platform, we’re seeing them elsewhere too.
Take action with confidence
Cluster data together highlighting key patterns and suspicious behaviour to detect fraud rings and the worst offenders.
Identify connections and explore insights in your data
Analyse patterns and behaviour against our machine learning models and scoring engine. As AI tools present increasingly easy opportunities for bad actors to create harmful content to scale their efforts, we use behavioural analysis to detect patterns of non-genuine behavior.
Adapt to evolving tactics used by fraudsters
Detect scam activity, fake accounts, and coordinated abuse early, before they spread across your platform.
Identify fraud rings and linked accounts through behavioural patterns and network analysis, not isolated signals.
Automate fraud detection and prioritisation, allowing teams to focus only on high-confidence, high-impact cases.
Safeguard users, transactions, and brand reputation by removing bad actors quickly and consistently.
