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Version: 3.0.3

Release notes

Version 3.0.3​

About Release
  • Release date: April 17, 2024
  • Compatible platform versions: IA Cloud 10.2.7, Work.AI 10.2.8 and 10.2.9

Tara v3.0.3 focuses on an in-depth understanding of payment messages and alert details, improves disposition logic, and offers a higher automation rate.

The release provides the following additional capabilities:

  • Recognition of context in composite fields and establishing linkage between elements. An example is a beneficiary field that includes an account number, a name, and an address.

  • Recognition of multiple identifiers in a payment message, such as UUID, ISIN, IBAN, BIC, and other codes.

  • Recognition of common language expressions in a payment message, including account types, references, and more.

  • Customization of disposition logic and narratives using rules.

  • Lookup, validation, and enrichment for the following external sources:

    • Google Geocoding for address validation

    • Custom sources for company, individual, or address validation

New features

  • A customizable rule engine enables creating custom rules and narratives without code changes, providing final decisions and dispositions. The default rules are optimized to produce the minimum error rate.

  • The Continuous Learning Data Store (CLDS) allows storing analysts' historical decisions and applying them to real-time alerts.

  • The stand-alone Name Entity Recognition (NER) model enables the sending of predicted entities for enrichment and custom modifications and provides fine-tuned recognition of the ID, Individual, Organization, Location, and Vessel types.

  • The unified name matcher is a reusable component you can embed into any model and use as a service.

  • Recognizing a dependency between entities (for example, ID > Name > Address) increases the number of data points for accurate decision-making.

Extended entity recognition

Tara's named entity recognition now includes understanding of entity types, common language expressions, identifiers, locations, and additional information. Each recognized entity is assigned a confidence score to gradually fine-tune results and improve decision accuracy.

External source enrichment

Tara can perform external lookups and enrichment and validate information, such as location, company name, individual name, and identifiers. The enrichment functionality is optional and can be enabled as needed. Custom enrichment sources can be integrated alongside out-of-the-box options.

Rule-based disposition

The disposition logic is now rule-based. You can extend the default rule set with custom logic or modify rules to match the existing procedures. Rules also allow for narrative customization and prioritization.

Extended payment message context

Tara's context understanding capabilities are extended to account for additional payment details, such as hit types (name, embargo, ID), sender and receiver information, and the existing knowledge about entity types. When available, the data can be input into Tara to enhance disposition decisions and increase the automation rate.

For more details on new features, see the information below.

Business Processes

  • Migrated Tara to Java Native Worker.

  • Added the Drools rule engine to make a final decision. Created a new business object that contains all the data points, including those returned from the Decision model. A rules file is located in S3 MinIO and can be easily updated.

  • Optimized the rule engine initialization.

  • Added the Decision Reapplication step known as CLDS to the configuration UI.

  • Enabled reading CLDS historical data from the common directory regardless of Tara's version.

  • Updated CLDS historical upload statistics per run.

  • Deleted the Sanctioned program and Open Corporate Connector steps from the configuraton UI.

  • Enabled the application of rules on requests with errors or exceptions by allowing hits with empty tag content to go through the rule engine.

  • Enabled calling to the Geocoding API only when an address is available but a country is not. The validation is displayed as true or false if Google Geocoder was used; otherwise, null is shown.

  • Added hit text calculation and entity type using similarity scores and fuzzy matching. The functionality is moved from the Decision model.

  • Updated joining of duplicate tag values from the content.

  • Introduced the NER model v1.6.8.

  • Added NER Model as a standalone bot step in the Tara Business Process. Added the nerData object for processing. NER processing is no longer part of the Decision model.

  • Introduced the Decision model v4.1.13.

  • Added Superset analytics into the bundle.

  • Added support for multiple ID types, such as BIC, ISIN, IBAN, UUID, IFSC.

  • Updated the 50F country parser and the Swift dictionary.

  • Revised the stopword list for the name matcher.

  • Improved Swift data parsing for Name and Address fields 50F, 50K, 50A, 59F, 59, 59A. Added multiline parsing support.

  • Enabled parsing a country from IDs located in sender and receiver fields and identified entities.

  • Improved country parsing and converting all countries to the ISO 2 format.

  • Combined duplicate FUF tags data into a single one when parsed.

  • Updated the HTML report to display a business object.

  • Enabled display of the name matcher score as the reason score inside a decision object.

  • Enabled an API response and analytics and a report to run in parallel, speeding up the response return to a client.

  • Improved the ID matching logic.

  • Added a few scenarios for parsing Additional Info per customer request.

  • Added support for the ID_OTHER and ACCTYPE NER types.

  • Added support for the XS country code, the ISIN special code.

  • Added the isLatinChars and isHieroglyphsChars rule functions.

  • Added the run_uuid and tara_version columns to the pss_request_processing_v1 Data Store.

  • Added the Maximum Hits Per Message processing limit.

  • Improved the logic that updates Rules Object with a parsed country and a country source.

  • Added a CSV file with countries to unify country information for Rules Object.

  • Implemented many changes in the rules file and utilities to decrease the error rate to less than 1%.

  • Added payment information (sender, receiver, and so on) and hit type to the request JSON and processing support.

  • Enabled identifying relationship between entities based on NER Name and Address, NER Name and ID, and so on.

  • Added the UI configuration for the NER confidence threshold per type that allows to ignore NER predictions of low confidence.

  • Updated screened data based on the Name and Address tag type and tag content. Now, the Decision model uses screened data over NER data when making a decision.

  • Enabled recording of all hits with empty content to analytics.

  • Added the Decision model and NER model versions to the analytics tables.

  • Updated processing and reports of filtered hits.

  • Added support for the VESSEL and TEXT_TAG NER types.

  • Added rules for the VESSEL screened type.

  • Added the logic to parse multiple IDs inside a code-map entry separated by ;.

  • Added the logic to remove extraneous newline characters at 36th positions in tag content due to Swift tag length limitations.

  • Set the rules service as a singleton.

  • Removed the version from the DR upload directory.

  • Added DR tests.

Models

  • Enabled a standalone location mismatch.

  • Enabled usage of the provided NER model and enrichment data for processing.

  • Removed the NER dependency. Now, NER is a separate model.

  • Added support for the ID type.

  • Updated the logic to use data according to the provided type.

  • Added the logic to compare known ID types, such as BIC, ISIN, IBAN, UUID, and so on.

  • Removed the Multiple hit tokens found escalation type.

  • Optimized the name natcher performance.

  • Improved the name matcher logic for the Organization type.

  • Added support for the NER Account and ID_OTHER types.

  • Improved ID matching for the ID mismatch to return the same types only.

  • Enabled the export of a processed screened name and a watchlist entity name.

  • Added the pre-processing function to remove a hyphen within the ID-like values.

  • Integrated unified name matcher v0.1.3.

  • Enabled the output of processed names from the name matcher.

  • Made the name matcher a part of the pipeline.

  • Enabled the usage of the configurable name matcher threshold from the pipeline.

  • Updated Python dependencies and bundled the Python zip environment to python3.11.5-pss-v2.

Bug fixes

Business Processes

  • Fixed the issue with missing analytics and an HTML report for failed messages with empty tag content.

  • Fixed the issue with updating the flat result Data Store.

  • Fixed the issue with writing results to the flat output table.

  • Fixed the issue with failing to find a tag in content when it is split into two or more parts by combining duplicate FUF tags data with count into a single one when parsed.

  • Fixed performance issues when loading big historical data files.

  • Fixed the issue with updating the tag content with an empty value.

  • Fixed the issue with reporting duplicates to analytics tables.

  • Fixed the issue with fetching a Business Process run ID in IA Cloud v10.2.7.

Models

  • Fixed the issue with producing ID match and mismatch without a sanctioned ID.

  • Fixed the name matcher decision when the threshold equals 1.0.

  • Fixed the issue with a country incorrectly identified as a city.

  • Fixed the issue with the concatenate space logic on big names for the name matcher.

  • Fixed a possible Organization type mismatch when the sanctioned type is missing (equals null).

  • Added various fixes for preprocessing, matchers, and decision-making.

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