Release notes

Version 4.0.1

About Release

• Release date: December 22, 2025
• Compatible platform versions: Work.AI 10.3+

The Tara 4.0.1 release focuses on strengthening security, improving screening accuracy, and refining AI Agent's configuration and enrichment performance.

• Java vulnerabilities identified by Snyk and JFrog scans are addressed. The updates reduce security risk and improve deployment reliability.

• The Tara UI configuration screens now match other AI Agent configuration screens, with updated presentation and wording to deliver a more consistent, clear, and user-friendly configuration experience.

• 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 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.

• Tara now supports updated MLSDK model output formats, ensuring compatibility with newer MLSDK versions by handling both object-based and string-based results for the NER and Decision Models and preventing processing issues after model upgrades.

• The JNW Generic Connector is upgraded to v3.6.0 for the Orbis connector, fixing vulnerabilities.

• Secrets Vault entries can now be created directly from the configuration form, enabling users to add and automatically apply new API credentials without leaving the setup flow, streamlining configuration and reducing setup time.

• Enrichment connectors are enabled to 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.

• Tara now uses the sanctioned name and type as the screened name and type when tag content fully matches a sanctioned entity, improving screening accuracy and consistency while preserving existing screened data.

• Hidden Data Store fields are removed from the AI Agent JSON configuration. Hard-coded, version-bound Data Store values are used instead to reduce runtime lookups, prevent configuration-related failures, and improve overall processing stability.

• The AI Agent configuration is upgraded with the Decision model v6.0.1, introducing the following enhancements:

  • The Java hypermodel worker now requires Java 21, ensuring compatibility with updated runtime dependencies.

  • The model output contract is updated, with attrs.model-results provided as a JSON string instead of an object.

  • Support for caching negative enrichment outcomes is added, reducing repeated lookups and improving overall processing efficiency.

  • Dependencies and component versions are updated to enhance stability and maintainability.

• The NER model is upgraded to v3.0.5 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.

  • Additional financial instrument ID types are supported, including SEDOL and FIGI, with improved prediction and validation for CUSIP, FIGI, ISIN, and SEDOL.

  • The new entity type ID_IMAD is introduced, supporting IMAD and OMAD identifiers used in FedWire payments for tracking and reconciliation.

  • 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.

  • NER processing performance is optimized for duplicate tag content. Identical text is now processed only once and reused across hits to reduce processing time.

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.

• Resolved the data purge issue where archival data purge scripts caused bundle import failures due to a Liquibase migration error. The fix results in smoother upgrades and more reliable data management.

• 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.

• Fixed the issue in hit text calculation where hits on identifiers with a one-character prefix (for example, I/HU and O/US) were incorrectly attributed to a location value. The logic now correctly identifies and presents the intended ID as the hit for adjudication, aligning results with user expectations.

• 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.