C3 AI - Anti-Money Laundering Software

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. 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.

The C3 AI 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 SAR.

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.

Prioritize investigations
Identify priority cases using field-tested AI algorithms, optimized for risk-based prioritization of suspicious activity.

Review risk factors
Review risk factors for each entity based on the typology for classifying risks.

Access internal and external data
Access correlated internal and external data from numerous source systems in one interface.

Expand and modify risk analytics
Extend and modify over 5,000 out-of-the-box transaction risk analytics, with additional customization options.

Create evidence packages
Create evidence packages for transparency and enhanced auditability.

Configure sophisticated UI
Visualize money movement among entities and track suspicious counter-parties.

View granular client detail
Track clients across multiple source feeds in a single view to aid investigation.

Identify associated parties
Use internal and transactional relationships to identify all known associated parties.

Perform ad hoc analyses
Assess an entity and export the findings.

Augment investigations
Incorporate recommendations from contextual information, such as typology, counter-parties, and beneficial ownership.

Integrated escalation
Escalate and route cases to the correct team(s) using case management software.

Create SARs
Generate and submit Suspicious Activity Reports (SARs).

Reduce
Reduce false positive alerts by as much as 85% and save corresponding time and effort.

Increase
Increase timely Suspicious Activity Report identification by as much as 200%.

Reveal
Reveal transparent, easy-to-interpret drivers for each risk score, using AI models that are easily configurable and flexible.

Combine
Combine data from banking systems,  transaction monitoring systems, KYC systems, and external sources.

Improve
Improve quality by reducing false positive alerts and focusing on highest-risk cases.

Maximize
Maximize productivity with integrated case-investigation workflows and rich contextual visualizations.

Enhance
Enhance precision with a closed-loop system that incorporates feedback from investigations into algorithms.

Empower
Empower investigators with interpretable key risk drivers and an audit trail for reporting requirements.

Integrate
Integrate data from the application within your enterprise systems and workflow using RESTful APIs.

Anticipate
Anticipate clients better with segmentation models that are AI-driven and behavior-based.

Detect
Detect emerging money laundering typologies and configure new analytics by scenario.

Update
Update client risk scores and keep them current with every transaction and account activity.

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 transaction monitoring workflows.

C3 AI Anti-Money Laundering provides sophisticated analysis tools and visualizations to track movement of funds through complicated networks of accounts, so more cases can be resolved faster.

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.