Create complex AML compliance and fraud detection rules in 60 seconds with an AI-native no-code rules engine.
Our compliance team can now implement new detection rules in minutes instead of weeks. That speed is critical when you're processing payments across six different regulatory jurisdictions and need to respond to emerging fraud patterns immediately
“The ability to configure rules without relying on engineering support has been a big win. We are also able to monitor and test within Flagright itself, without requiring any sophisticated data or QA work to develop metrics outside the platform.”
Describe transaction patterns in plain English. Automatically converts intent into rule logic, thresholds, and typologies.
60 seconds Validated rule creation time
Not every threat looks like a predefined rule. Flagright’s ML engine continuously detects anomalies, peer deviations, irregular activity, and counterparty clustering alongside configured rules.
Model updates: Continuous no scheduled retraining
Flagright’s AI analyzes alert history and disposition outcomes to recommend optimized rule thresholds with one-click application.
Up to 83% false positive reduction, validated
Shadow mode tests rules silently on live transactions, while simulations validate performance against historical transaction data before deployment.
zero production disruptions from rule testing
Flagright reads live risk scores and automatically adjust thresholds for different customer risk levels, without manual rule segmentation.
Model updates: Continuous no scheduled retraining
“By integrating Flagright’s AI-native compliance platform, we have enhanced our fraud detection and AML monitoring capabilities. This allows us to proactively identify and mitigate risks, ensuring a safe and secure environment for our customers while upholding the highest standards of compliance.”
“With Flagright, I have the tools and expertise to set up a rule in just under a few minutes. We combine that with simulations and shadow rules to measure the impact before making changes to our live setup. We test it, tweak it, and deploy it.”
Detect suspicious activity instantly as transactions occur across live payment flows.
Process completed transactions after execution using the same detection logic and risk controls.
Monitor large transaction volumes through scheduled processing and high-volume bulk execution.
Every month, Flagright surfaces the most effective rules being run across its network of 100+ regulated institutions.
New typologies. Emerging patterns. Regulatory shifts. Delivered as in-app recommendations - reviewed, curated, and ready to deploy.
Delivered in-app as a dashboard card, once per month
Categorised by typology, jurisdiction relevance, and observed catch rate.
One-click to preview, one-click to deploy into shadow mode
You see the pattern intelligence. You don't see other institutions' data.
For every rule that has processed sufficient alert volume, Flagright's threshold recommender analyses the full alert disposition history - true positives, false positives, users hit, transactions hit - and surfaces the optimal threshold.
No manual analysis. No spreadsheets.
Works across rules with sufficient alert history (ideally 100+ dispositioned alerts)
Recommendations generated automatically inside the rule management console.
Apply with one click; previous threshold saved for rollback
Validated impact: up to 83% false positive reduction
A regular intervals you can configure, Flagright's AI system does a performance analysis of all your rules and provides recommendations for tuning your rule sit.
No manual analysis. No spreadsheets.
Continuously improves rule performance across the monitoring environment
Reduces rising false positives and unnecessary analyst reviews
Identifies underperforming rules before detection quality declines
Eliminates manual rule analysis and spreadsheet-based reviews
Flagright reads a customer's live risk score and automatically applies the appropriate rule thresholds. No manual segmentation. No engineering overhead.
Risk score computed
Flagright's risk scoring engine continuously evaluates each customer's risk profile based on behaviour, KYC data, and transaction history.
Threshold applied automatically
When a transaction is processed, the TM engine reads the customer's current risk score and applies the threshold configured for that risk band.
Alert generated at the right level
A high-risk customer triggers an alert at a lower threshold. A low-risk customer requires a higher volume to trigger. The same rule. Calibrated automatically.
Flagright's transaction monitoring surfaces the alert. AI Forensics for Monitoring investigates it - automatically pulling transaction history, mapping counterparty relationships, matching typologies, and drafting the SAR narrative. All before a human analyst opens the case.
Their platform is built specifically for financial crime compliance, so it covers AML, CFT, and sanctions screening thoroughly. Automation of routine tasks (like transaction monitoring) saves significant manual effort.
Built to sustain peak volumes, absorb traffic surges, and continue operating through partial failures without service degradation.
Rules evolve. Flagright saves every version automatically for total alert traceability and one-click rollback.
Production-grade availability with real-time visibility and monitored performance at all times.
Every rule change logged. Every version saved. Every deployment approved. Built for auditable monitoring infrastructure.
A complete record of every action, always
Every rule change, update, and deployment is written to an immutable, timestamped audit log.
Every version of every rule, preserved
Rules evolve. Flagright saves every version automatically for total alert traceability and one-click rollback.
No rule goes live without the right sign-off
Built-in maker-checker workflows separate rule creation from approval with configurable, role-based review chains
