Our proven technology utilizes proprietary algorithms, or AI agents, to identify instances of financial crime. AI agents employ a variety of techniques such as fuzzy matching, graph traversal, criminality sentiment and more to generate observables indicating the risk potential of a transacting party in three areas:
- Entity Reputation. Detects instances of potential financial crime from structured and unstructured data to calculate an entity’s reputation risk.
- Transaction Monitoring. Detects instances of potential financial crime from transactional data such as SWIFT messages or other data sources that show an exchange of value.
- Intent. Detects instances of potential financial crime from behavioral data derived from transaction history, company records, and other sources.
Risk segmentation and scoring is a tremendous cognitive challenge for compliance teams. Our trained deep neural network produces incredibly detailed and accurate criminal sentiment around entities based on potential financial crime risk. Through our decisioning engine, entities’ transactions are marked as anomalous or non-anomalous and are given a mathematically-generated risk score and confidence level specific to clients’ risk tolerance.