AML/Compliance: Anti-Money Laundry/CFT Compliance

ANTI-MONEY LAUNDRY /CFT SOLUTIONS:

• DATA MANAGEMENT

  

  Manage various data challenges on one platform, from big data processing to accessing and integrating legacy sources, along with in-memory and in-database performance enhancements. Our data management model supports numerous data types such as non-monetary event data, geographic data, risk lists, third-party data, associate data, a variety of customer information data, and transaction data.
  Key Features:

  • One platform with in-memory and in-database performance enhancements.
  • Transaction recording, map transaction, account, customer, and household dimensions.
  • Integrate a core schema for preparing data for nightly batch analysis.
  • Have a knowledge center data schema supporting data retention and investigation.
  • Analyze various data types (nonmonetary event, geographic, risk lists, third-party, associate, various customer information, and transaction data).

• High-performance analytics and visualization

  

  Gain economies of scale and make better and faster decisions by building a flexible infrastructure to test hypotheses, ask new questions and mimic scenarios in the analytical process. Empowering you to build, test, and edit scenarios in seconds to improve scenario adequacy while abiding by the model governance best practices.
  Key features:

  • Fast access to customer, account, and transaction data.
  • Visualize emerging risks.
  • Monitor customer behaviors by creating visual customer segments.

• Suspicious activity monitoring and reporting

  

  Utilize the easy-to-use, point-and-click interface to create and modify scenarios and risk factors. Users can create personalized routing rules for workload distribution. Scenarios can be simulated and validated so that the results in the "sandbox" can be placed into the production alert generation process.
  Key features:

  • Host an open box environment for end-user authoring and modification of scenarios.
  • Combine scenario conditions with risk factors to provide greater context.
  • Integrate status scenarios that produce list matches alerts.

• Watch-list matching

  

  Utilize the watch list matching to reduce the regulatory risk and increase the high-quality hits with a low false positives rate by leveraging unique fuzzy matching algorithms, intelligent scoring, and alert consolidation. Have the freedom to import sanctions and other watch lists to identify persons, organizations, or high-risk jurisdictions that represent regulatory risks.
  Key features:

  • Have an enterprise-level coverage with a multijurisdictional screening across business units.
  • Have an individual and entity-party linking and integrated investigations.
  • Import sanctions/watch lists to find persons, organizations, or high-risk jurisdictions.
  • Integrate fuzzy matching logic for accurate entity matches.
  • Choose to work transaction, party, or counterparty matching lists as alerts or cases.
  • Combines functionality with Dow Jones and World-Check services.

• Investigation and alert management

  

  Through a web-based interface, investigators gain a holistic view of work items; by utilizing entity triage to collect all of the alerts for a subject into one work item so the investigator can make faster and more accurate decisions. To increase triage efficiency and mitigate risks faster, users can distribute groups of alerts centered on an entity.
  The solution is equipped with an accessible knowledge center database that serves as the record system for regulatory and auditing purposes.
  Key features:

  • Support for "four eyes" approval of case decisions.
  • Have a web-based interface.
  • Efficient access to profile information.
  • Specific and consistent user-data views.
  • Collect alerts at the entity level for a holistic view of risk.
  • Perform bulk actions on alerted entities and case queues.

• Peer group anomaly detection

  

  Segment high-risk and de-risk groups based on transactional behavior to compare peers who should behave similarly. Scientists can build peer segments more quickly using proven clustering algorithms by leveraging the in-memory visualization techniques and data.
  Key features:

  • Compare an entity's current, historical, and peers' behaviors.
  • Segment your groups into multiple peer groups.
  • Detects outliers with above and below the expected behavior.

• Search

  

  Empower analysts to retrieve information from case notes, attachments, and other fields available to the system.
  Key features:

  • Swiftly index and analyze data.
  • Easily search for comments and attachments.

• Multitenant architecture

  

  By segregating the data, the single software installation is applied to multiple subsidiaries or jurisdictions, thus reducing the cost of IT support managing numerous subsidiaries, enhancing information sharing, and maintaining data security.
  Key features:

  • The single installation serves multiple customers by segregating data.
  • Integrate separate schemas within a database for partitioning data, and each schema can have its own AGP process, with one web application for all schemas.
  • This architecture is simple to implement.