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Version: 2.3.5

Input and output data

info

You can use the documentation on version 2.3.5 for working with Tara version 2.3.6.

Input data​

Input to the Payment Sanction Screening Business Process (BP) refers to surveillance system alert details triggered against a given payment message or transaction.

Tara supports the following types of input:

File input​

The file input feature allows uploading an input CSV file. Using a CSV flat file as a template is the easiest input method for uploading input data for Transaction Screening. A BP finishes when all records are processed.

  • The file-in-original format contains a JSON request. This format contains the following columns for execution:
    • request_json_object (required)
    • decision_gold_json_object (optional)
  • The file-in-flat format contains an unfolded request_json_object. A BP treats each row as a separate request.

To upload data, follow the steps:

  1. In your BP, select the Data tab and click Upload Data.

  2. Click Add, find your CSV file, and click Open.

  3. Wait till the file is processed and click Upload.

Streaming API input​

The system obtains input data from the built-in streaming service. You can define and update API endpoints. A Business Process (BP) is available even when all records are processed.

To use the built-in streaming service, do as follows:

  1. In your BP, select the Data tab.

  2. Check the Streaming Records from External Sources box and specify the external source in the field.

    For more details, refer to API | Tara.

Data from connector​

Tara uses the Firco Continuity Connector to communicate with the alerting system. To select the connector as the source of your input data, go to the Data tab in your BP and click No Data.

Request Data Store​

Tara accepts standardized input in JSON-formatted strings and saves requests into the pss_request_processing_v1 Data Store. For more details, see Run screening | Analyze results.

Example of the pss_request_processing_v1 Data Store:

ColumnDescription
system_idDatabase generated ID
request_idUUID generated before adding a record to a Data Store
request_json_objectOriginal customer data for adjudication in the JSON format representing a Message object
decision_json_objectModel Decision in the JSON format representing a MessageDecision object
decision_gold_json_objectGold Model Decision in the JSON format representing a MessageDecision object; used for model training and calculating statistics
message_json_objectProcessed or enhanced copy of customer data for adjudication in the JSON format representing a Message object; data from this column is sent to the model for processing
request_insert_timeInsert data timestamp
last_updated_timeTimestamp of last update
decisionFinal Message decision
statusProcessing status; updated during record processing
client_idUser who sent the request for processing

Output data​

The Tara BP's output data field is model_result.

The model sends a response with structured data in the JSON format:

  • Category: message decision.

  • Model decision: structural data that contains detailed information about decision per hit and message.

  • Reason code: reason for the model decision. See the list of standard reasons that Tara returns:

    TYPE_MISMATCH = "Type mismatch"
    TYPE_MATCH = "Type match"
    ID_MISMATCH = "ID mismatch"
    ID_MATCH = "ID match"
    NAME_MISMATCH = "Name mismatch"
    NAME_MATCH = "Name match"
    PARTIAL_NAME_MISMATCH = "Partial name mismatch"
    LOCATION_MISMATCH = "Location mismatch"
    LOCATION_MISMATCH_INVALIDATED = "Location mismatch invalidated"
    LOCATION_MATCH = "Location match"
    LOCATION_MATCH_INVALIDATED = "Location match invalidated"
    DATE_OF_BIRTH_MISMATCH = "Date of birth mismatch"
    DATE_OF_BIRTH_MATCH = "Date of birth match"
    MSG_NAMES_MISMATCH = "Msg names mismatch"
    SS_NAME_SCREENED_NAME_MISMATCH = "SS name screened name mismatch"
    MULTIPLE_NAMES_FOUND = "Multiple names found"
    LEGAL_TYPE_MISMATCH = "Legal type mismatch"
    EO13662_NOIW = "No inclusion word"
    EO13662_IWNC = "Inclusion word not concerning"
    BLACKLISTED_COMPANY = "Blacklisted company"
    BLACKLISTED_COUNTRY = "Blacklisted country"
    BLACKLISTED_WORD = "Blacklisted word"
    MULTIPLE_HIT_TOKENS_FOUND = "Multiple hit tokens found"
    NOT_ABLE_TO_PREDICT_HIT_TEXT = "Not able to predict hit text"
    SANCTIONED_PROGRAM_ND = "Sanctioned program ND"
    SANCTIONED_PROGRAM_FP = "Sanctioned program FP"
    NOT_IMPLEMENTED = "Not implemented"
    RULE_NOT_CONFIGURED = "Rule not configured"
    ESCALATE = "Escalate"

  • Description: human-readable decision explanation.

  • Score: classification model confidence of the alert to belong to the predicted topic.

A sample model's result is as follows:

{
  "score": 1
  "category": "RESOLVE"
  "estimatedAccuracy": 0
  "attrs": {
    "model-decision": {
      "messageId": "58198"
      "suggestedAction": "RESOLVE"
      "hitDecisions": [
        {
          "hitId": "8194965"
          "suggestedAction": "RESOLVE"
          "comment": "Name mismatch. Screened entity name 'Witting-Armstrong Inc.' does not match Watch List Entity name 'Terry, Konopelski and Auer Solutions Inc'"
          "reasons": [
            {
              "code": "type match"
              "description": "Screened entity type <organization> matches watch ..."
            }
            {
              "code": "name mismatch"
              "description": "Name mismatch. Screened entity name 'Witting-Arms..."
            }
            {
              "code": "location match"
              "description": "Screened entity locations <VUT> either matches or ..."
            }
          ]
        }
      ]
    }
  }
}
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