Banking — Customer Intent Classification for Enhanced Response Management

Overview

The purpose of this DAG is to enhance customer service in the banking sector by accurately classifying customer intents derived from CRM interactions and ITSM tickets. The architecture begins with data ingestion from multiple sources, including CRM systems, ITSM ticketing systems, and customer feedback databases. The ingestion pipeline normalizes the data to ensure consistency and prepares it for analysis. The processing steps involve intent classification using machine learning algorithms, whic

The purpose of this DAG is to enhance customer service in the banking sector by accurately classifying customer intents derived from CRM interactions and ITSM tickets. The architecture begins with data ingestion from multiple sources, including CRM systems, ITSM ticketing systems, and customer feedback databases. The ingestion pipeline normalizes the data to ensure consistency and prepares it for analysis. The processing steps involve intent classification using machine learning algorithms, which analyze the normalized data to identify customer intents. Quality controls are implemented at various stages, including validation checks and accuracy assessments, to ensure the reliability of the classifications. The results of this classification process are then exposed via a RESTful API, allowing seamless integration with AI assistants and contact center applications. Key performance indicators (KPIs) such as classification accuracy and response time are monitored to gauge the effectiveness of the system. The business value of this DAG lies in its ability to improve customer satisfaction through timely and accurate responses, ultimately leading to enhanced customer loyalty and operational efficiency.

Part of the AI Assistants & Contact Center solution for the Banking industry.

Use cases

• Improves customer satisfaction through accurate intent recognition
• Enhances operational efficiency in contact centers
• Reduces response times for customer inquiries
• Facilitates better resource allocation based on intent data
• Drives customer loyalty through personalized interactions