Our client is a leading financial institution, offering banking, wealth management, and brokerage services to its customers.

The client has deployed a chatbot on Google Cloud™ using native services like DialogFlow, Vertex AI, etc. The intelligent assistant enables their customers to perform trading and banking activities like getting a quote, getting market updates, placing a trade, transferring money, investment FAQs, etc.

The objective was to build a scalable engine within the chatbot to detect customer’s intent and offer appropriate solutions, thereby increasing chatbot accuracy.

• Difficult to tag and identify good quality chat instances that can be used as bot training data
• Each user transcript needs to be manually reviewed to check for classification accuracy
• The off-the-shelf platform does not provide any user adoption metrics or agent performance metrics. The data required to track these metrics are scattered across different sources