AI Overlay for Transaction Monitoring

Boost existing AML transaction monitoring software with AI

Strengthen your existing AML software with AI that identifies emerging criminal behaviors while significantly reducing false positives

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Australian bank reduces false positives >47% with AI Overlay for Transaction Monitoring

AI Overlay for Transaction Monitoring identified complex criminal behavior patterns that traditional rules-based systems missed

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Go beyond rules-based detection

Rules can’t detect what they don’t know. Identify complex anomalies in transactions and expose emerging criminal behaviors targeting your business.

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Upgrade any detection system

AI Overlay for Transaction Monitoring is detection engine agnostic, meaning it can enhance any setup for immediate results. Uncover suspicious activity that your existing system has missed and immediately enhance your understanding of entities and their changing risk profiles.

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Reduce false positives by 70%

Advanced AI analysis is able to distinguish genuine risk from false positives, reducing overall alerts while increasing credible threats identified.

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Surface hidden connections in your data

Leverage your existing data to immediately start finding hidden risk patterns and ensure detection is always adapting to changing risk with AI models that learn and improve over time.

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Gain regulator confidence

Demonstrate superior detection capabilities that evidence your commitment to proactive prevention with fully explainable and transparent AI models.

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Using the AI Overlay for Transaction Monitoring offers a low-risk introduction of AI into your AML operations.

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Enhance KYC

Integrate SymphonyAI into your KYC processes for more effective ongoing risk assessments that track whether customers behave as expected. SymphonyAI is the easiest way to connect AML with CDD to take customer due diligence to the next level.

Holistic risk scoring

Meet the four AI models of AI Overlay for Transaction Monitoring that will transform your transaction monitoring capabilities.

Four connected steps in a holistic risk scoring model: Risk similarity to reduce false positives using historic data; Hotspot to identify missed risk by detecting suspicious behavior; Anomaly to expose unusual activity by analyzing behavior for hidden risks; Change in behavior to monitor deviations and assess behavioral patterns indicating higher risk.