Automated Complaint Categorization for Enforcement Bureau
Description
The AI model is designed to automate the categorization of consumer complaints received by the Enforcement Bureau. Currently, this process is performed manually by staff, which is time-consuming and prone to inconsistency. The model uses natural language processing (NLP) to classify complaint text based on predefined categories, streamlining the workflow and improving accuracy.
Detailed example
The AI system produces categorized complaint data in Excel format. These files are stored in a shared drive and are later visualized using Microstrategy dashboards. Users do not interact directly with the model; instead, they access the model’s outputs through reports and visualizations. The model is updated approximately every six months to ensure continued accuracy and relevance.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The AI system improves operational efficiency by reducing the manual workload for staff, allowing them to focus on higher-priority enforcement activities. It enhances consistency in complaint categorization, which supports more accurate reporting and analysis. Faster processing of complaints enables quicker responses to public concerns, and the structured data output supports better decision-making and policy development aligned with the agency’s mission.
Controls / human review
ATO: Not reported; PIA: Not published