Improved Money Laundering Detection with Predictive Analytics

C3 AI Anti-Money Laundering

C3 AI Anti-Money Laundering uses comprehensive machine learning techniques to increase the accurate detection of suspicious activity while significantly reducing false positive alerts. The application improves AML investigator productivity with intelligent case recommendations, flexible analysis tools, automated model evidence packages and advanced visualizations of key contextual case data, including alert insights, network link analysis, transaction patterns, and KYC profile information.

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Demonstrated Benefits


Reduction in false-positive AML alerts requiring investigator review


Increase in AI-detected money laundering activity


Increase in analytical signals used to detect suspicious activity

A detailed step-by-step guide on how to use AI for AML Operations.


Transforming AML with Machine Learning

C3 AI Anti-Money Laundering Solution

Existing systems create an excessive number of false positive alerts

AI-based early warning signals reduce false-positive alerts by up to 85%

Existing systems miss certain suspicious activity and emerging risks

AI-based AML risk detection improves accuracy of suspicious activity identification by up to 200%

Siloed data leads to highly manual and inefficient investigations

360-degree customer view unifies all client activity data, including transactions, balances, credit history, news, relationships and more, in a single user interface

Evidence package creation is time-consuming and investigation evidence is inconsistently captured

AI recommendations and interpretability allow intelligent investigations with easy-to-interpret risk drivers and seamless evidence package creation

Difficult to measure operational performance and improve investigation outcomes

Flexible and configurable dashboards enable optimized investigation throughput and accuracy

Processes and systems are not flexible enough to accommodate changing regulatory requirements

End-to-end AI lifecycle management and full data lineage ensure sustained model performance and full model auditability

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