Anti-money laundering (AML) is the set of rules and regulations with which banks and financial institutions must comply to detect and report suspicious activities related to money laundering, criminal activity, or terrorist financing. Given the fast-moving challenges of addressing illegal activities across the globe, AML regulations are complex and dynamic. Moreover, regulations in the US can be different from those in the UK or Australia.
AML regulations require banks and financial institutions to track financial transactions in an effort to detect and report suspicious activity. Specialized AML software applications help monitor transaction activity and manage workflows associated with investigating suspicious cases. Predictive analytics offer a new and productive approach to AML compliance with greater accuracy in spotting suspicious activity and lower false positives.
The risks of non-compliance with AML regulations are high. In addition to hefty fines, banks can face severe damage to their reputation and lose customer trust. At the same time, the cost of complying with AML regulations also is high and requires proactive management.
Within large financial institutions, the data necessary to identify money laundering activities are segregated across Know Your Customer (KYC), core banking, Anti-Money Laundering (AML) monitoring, case management, and several other systems. Traditional AML software approaches have relied on rules-based systems to spot suspicious activity. Such rigid systems are often unable to keep up with agile criminal organizations and unable to spot enough suspicious activity. These existing rules-based detection systems also result 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.
The C3.aiTM Anti-Money Laundering 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. The
application provides transparent, easy-to-interpret risk drivers for each 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 suspicious activity reports.
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 lists. The application also supports automated closed-loop feedback to improve predictions and augment existing KYC and monitoring workflows.
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