ELEMENT OF COMPLIANCE

Compliance is a moving target. As the commercial landscape evolves so does threats, and regulations. Element of Compliance was designed by senior compliance officers to automate regulated processes, by harnessing Element’s learning capabilities. The application reduces obscurity, allowing to make better informed decisioning, thereby minimizing both: False Positives – Customers that would pass normal KYC processes, that only time consuming deep analysis will find hidden constraints,and potentially disputable, and therefore exposing the organization to unwanted risks False negatives – Sound customers that would be automatically rejected according to rigid processes, causing the organization to lose business, and impact its reputation

Adaptable KYC Adaptable KYC workflow consisting a Dynamic onboarding questionnaire based on field proven Ontology, unifying the cumulative knowledge of hundreds of banks from around the globe, in order to construct entity networks, and quantify their risks.

Entity Screening Real-time Entity Screening, continuously comparing both monitored entities (e.g. bank clientele), and related entities (e.g. clientele stakeholders, customers, and suppliers), against dynamically maintained indexes.

Reputation Monitoring Continuous Reputation Monitoring, allowing to scan and interpret countless data sources (e.g. international news outlets), resolve mentioned entities, comparing them to the persisted knowledge networks, in order to assess the impact on the organization.

Transaction Screening Real-time Transaction Screening, allowing to flag or prevent risky transactions, based on configurable risks (e.g. sanctioned geographies, volumes, patterns).

Transaction Monitoring Network based Transaction Monitoring, allowing to assess both entities involved with each transaction, across internal, external and open sources, thereby achieving complete visibility and allowing to determining individual transaction risk.

Transaction Clustering Transaction Clustering, utilizes graph-theorem based pattern recognition to correlate seemingly unrelated transactions, and assess their accumulative risk.