• Sub-category: At-fault accident
• Sub-category: No-fault accident
• Sub-category: Glass breakage
• Sub-category: Theft
• Sub-reason: Change of vehicle
• Sub-reason: Addition of a driver
• Sub-reason: Change of address
The Zaion NLU engine draws on a database comprising millions of real-world transcripts collected from BFSI contact centers. This data is clustered, annotated, and structured to create an intent classification scheme tailored to each industry sector.
A collection of millions of real conversations from contact centers in the banking, insurance, and financial services sectors in French.
Automatic grouping of similar verbatim entries and manual annotation by subject-matter experts to ensure the quality of the classification scheme.
Creation of an intent taxonomy organized by sector (Banking, Auto Insurance, Home Insurance, etc.) with sub-intents and business entities.
Thanks to a deep understanding of business needs, customer inquiries are routed to the right specialized AI agent or advisor the first time, reducing unnecessary transfers and improving satisfaction.
The NLU engine makes it possible to track changes in contact sub-patterns over time, identify seasonal peaks (insurance claims in the winter, policy purchases before vacations), and adjust resources accordingly.
Analyzing volume and complexity by intent helps identify use cases with a high ROI for automation (high volume, low complexity) and prioritize the content and user journeys to be created.
Explore each AI building block and its performance.
