Project background

The customer service center of a city commercial bank is faced with problems such as high labor costs, limited service hours, and high repeat rate of customer inquiries. At the same time, it hopes to improve the conversion rate of retail business through precision marketing.

Solution

• Large language model customer service: Based on fine-tuned large models in the financial field, covering account inquiries, loan consultation, financial management recommendations and other scenarios
• Customer profiling engine: Integrate transaction data and behavior Data builds 360-degree customer portraits
• Intelligent marketing recommendations: Personalized product recommendations based on the customer life cycle

Project results

The intelligent customer service coverage rate reached 85%, customer service labor costs were reduced by 60%, and the cross-marketing conversion rate increased by 42%.