Transform Financial Crime Detection with Machine Learning
Money laundering and illegal activity financing strategies are dynamic, fast moving challenges for compliance organizations across the financial crimes sector. The rising risks associated with money laundering, combined with constantly evolving regulatory requirements, have defined the need for intelligent software solutions that can accurately identify, prioritize and report suspicious activity, while simultaneously reducing the number of false positives.
Within large financial institutions, the data necessary to identify money laundering activities are segregated across KYC, core banking, AML monitoring, case management and several other systems. The existing paradigm of rules-based detection systems has resulted in an excessive stream of false positives that require costly and inefficient manual data enrichment and review. This drives compliance expense and lowers analyst productivity— diverting analyst resources while increasing the risk of missed investigations.
Drive Business Value Through Compliance Productivity
C3 AI Anti-Money Laundering is an AI-enabled, workflow-centric application that uses comprehensive machine learning techniques to reduce false positive alerts by as much as 85%, while increasing true suspicious activity (SAR) identification by as much as 200%.
The application improves investigator productivity with intelligent case recommendations, automated evidence packages and advanced visualizations of key contextual case data, such as alerts, parties, accounts, transactions, counter-parties and risk drivers.
Monitor Transactions with Interpretable Machine Learning
C3 AI Anti-Money Laundering provides transparent, easy-to-interpret risk drivers for each machine learning money laundering risk score. Unlike rigid rules-based systems, C3 AI Anti-Money Laundering models are easily configurable and flexible, enabling intelligent adjustment to changing regulations and money laundering strategies. The application uses sophisticated machine learning techniques, including self learning based on investigator output, to identify known and new typologies. Further, enhanced auditability features allow investigators and regulators to follow the lineage of suspicious behavior from source to SAR.
Incorporate Internal and External Data
In addition to integrating traditional core banking and transaction monitoring data, C3 AI Anti-Money Laundering delivers a universal view of the customer by integrating data from internal KYC systems and external sources like adverse media search results, sanctions and PEP list. The application also supports automated closed-loop feedback to improve predictions and augment existing KYC and monitoring workflows.