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Version: 3.4.2

Release notes

Version 3.4.2​

About Release
  • Release date: December 24, 2025
  • Compatible platform versions: Work.AI 10.2.8+

The Tara 3.4.2 release delivers improvements to enrichment accuracy, screening reliability, and connector stability.

  • The SWIFT parser now supports multiple identifiers in the 35B field, extracting all available IDs from the tag content to improve data completeness and reduce manual intervention during message processing.

  • The JNW Generic Connector is upgraded to v2.9.2 for the Orbis connector, fixing vulnerabilities.

  • The name-based search accuracy within the GLEIF API is improved by accounting for common spelling variations such as extra or missing spaces, omitted stop words, and incorrect punctuation, which results in more reliable matches and fewer missed enrichments.

  • Enrichment connectors now cache negative lookup results, storing non-conclusive responses from Orbis and GLEIF with an expiration period to avoid repeated unnecessary lookups and optimize data retrieval processes.

  • The GLEIF connector now refreshes cached lookup data automatically, using a configurable cache retention period to keep data up to date while reducing unnecessary repeated lookups, similar to the existing logic in the Orbis connector.

  • The AI Agent configuration is upgraded with the Decision model v5.2.1, introducing support for caching negative enrichment outcomes to reduce repeated lookups and improve overall processing efficiency.

  • The NER model is upgraded to v2.7.5 with the following improvements:

    • The PersonValidation post-processor reduces confidence for ambiguous PERSON entities, such as multiple names joined by connectors, for example, and/or or und/oder.

    • Person name lookup support is added to the configuration, improving the accuracy of person recognition rules.

    • Overconfident ORGANIZATION entities are further reduced by identifying and handling location-only markers using standard abbreviations and known location names.

    • The asterisk character (*) is added to the preprocessing replacement list to ensure consistent text normalization.

    • Python dependencies are updated and cleaned up, resolving known vulnerabilities.

    • The Python environment layout is updated. The initial deployment requires the bundled environment artifact (bundle-with-env), while subsequent deployments can reuse the existing environment.

    • The OrganizationPrefixSplitter post-processor is introduced to split leading reference prefixes (for example, REF and REF TL) from ORGANIZATION entities, producing cleaner company names.

    • BIC detection logic is revised to reduce false positives where personal names were previously misclassified.

    • The list of known companies is updated, improving organization recognition accuracy.

Bug fixes

  • Resolved the issue with a quality check (QC) report not reflecting security data usage in the screened name. When a security name is used for name matching, the QC report now shows the screened security name. In addition, the data source column now contains the LOOKUP type, making it easier for users to understand how the data was sourced.

  • Fixed the typeMatch logic in the rules JSON object. When the type is set to location, address and country values are displayed instead of the name. If an address is not available, other relevant location details are shown, ensuring more meaningful and reliable matching results.

  • Resolved the issue where GLEIF was not selected by default on the Data Enrichment configuration form when enabling the use of connectors' data. The fix ensures a more intuitive setup and reduces the risk of incorrect configuration.

  • Fixed the issue with SWIFT tag content line splitting where lines starting with SWIFT markers (for example, 1/, 2/) were not correctly rejoined. The fix improves NER model output by accurately reconstructing text and ensuring correct identification and extraction of IDs, organization names, and locations.

  • Resolved the issue where small IDs starting with / (for example, /0B7Z in SWIFT tag 59F) were not recognized as identifiers. The fix enhances ID detection and matching accuracy while avoiding false positives in cases like /Name1/ABC, where Name1 is not an ID.

  • Fixed the issue where ISINs were incorrectly split during NER preprocessing (for example, CNE100004SP7 becoming CNE and 100004SP7), preserving valid ISIN tokens end-to-end and preventing entity misclassification.

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