Banks and other financial institutions hire large teams to monitor real-time transactions and reduce false positive alerts. Tara optimizes the repetitive tasks involved in real-time transaction monitoring and allows analysts to focus on high-value work.
Tara is designed to ingest each alert generated by a financial institution’s sanctions screening system and she determines if it is a false positive. She helps to identify real-time transactions or payments to or from sanctioned individuals, entities, or jurisdictions, not only during onboarding but also throughout the customer relationship lifecycle that may pose a risk to financial institutions.
Tara is also trained to escalate potential true hits to a pre-defined team within the institution. All flagged transactions or payments are then reviewed manually by analysts to clear false positives.
Check the box to use the specified external source
Example of pss_request_processing_v1 datastores:
DB generated ID
Unique UUID generated before adding record to datastore
Original customer data for adjudication in JSON format representing a Message object.
Model Decision in JSON format representing the MessageDecision object.
Gold Model Decision in JSON format representing MessageDecision object. This is used for model training and calculating statistics.
Processed or enhanced copy of customer data for adjudication in JSON format representing a Message object. Data from this column will be sent to the model for processing.
Insert data timestamp.
Timestamp of last update.
Final Message decision
Processing status. This is updated during record processing.
User who sent the request for processing
Model response with structured data
Category: Reason code (Different for each client)
Explanation: Human-readable decision explanation
Score: Classification model confidence of the alert to belong to the predicted topic
Model-decision attribute: – contains full model decision in JSON format