CIR: GenAI-based Communication Insight Recommendation System Qorus-NTT DATA Innovation in Insurance Awards 2026

(1) Concept and Objectives

This project delivers the insurance industry’s first GenAI-driven end-to-end solution for Intelligent Note Summarization, Deep Customer Insight, AI-Powered Service Recommendation, and Long-Term Service Enablement, covering three core service scenarios: face-to-face meetings, voice calls, and video consultations. By addressing four key dimensions—automated communication capture, intelligent demand analysis, precise service recommendation, and structured long-term service management—the solution overcomes industry pain points and enables a fully intelligent and efficient insurance service workflow. It establishes a closed business loop of “communication becomes insight, insight becomes knowledge, knowledge becomes service,” empowering insurance agents to improve service efficiency and professionalism, while providing clients with personalized and refined insurance services, and driving the industry’s transformation from a human-driven to an intelligence-driven model.

The core innovation lies in real-time capture of multi-scenario communications and intelligent note generation. Through proprietary dialect-adaptive models, real-time semantic parsing engines, and customer lifecycle intelligence algorithms, interactions across face-to-face, voice, and video channels are transformed into structured electronic notes, professional service summaries, and accurate recommendations, while dynamically tracking changes in customer needs to provide data-driven support for long-term services. This technological approach reshapes insurance service workflows and achieves smarter demand insights, faster operations, and more effective client service.

(2) Underlying Rationale

The insurance industry has entered a deep phase of digital transformation. As the key connection between insurers and customers, agents’ service efficiency and expertise directly determine business conversion and customer satisfaction. In traditional insurance service models, agents face four main challenges: time-consuming manual note-taking, demand analysis relying on personal experience, weak long-term service management, and poor tool adaptability. These issues not only limit agent productivity but also result in inconsistent customer experiences. The industry urgently needs a GenAI solution that deeply adapts to agent workflows and integrates the full service chain, which led to the development of this project.

(3) Competitive Landscape

Current insurtech tools generally focus on improving efficiency in single service stages, providing basic functions such as transcription and note-taking. They suffer from limited scenario coverage, low intelligence, and inability to achieve data integration or long-term service enablement. Some GenAI insurance tools lack dedicated industry knowledge, generating outputs that deviate from practical business use, and show weaknesses in dialect recognition and compliance management. This project differentiates itself by using GenAI as the core technology to integrate the entire service chain from communication capture to demand analysis, recommendation output, and long-term service. It is deeply customized for the insurance industry, achieving a close integration of technology and business, forming an irreplaceable market competitive advantage.

(4) Inspiration

The project was inspired by the practical needs of frontline insurance agents. Through research with over 1,000 agents nationwide, we identified core pain points in communication recording, demand assessment, and long-term service. At the same time, rapid advancements in generative AI and big data analytics provided mature technical support to address these challenges. The team combined insurance business characteristics with technological trends and defined a research direction to empower full-scenario agent services with GenAI, creating an intelligent tool that truly meets frontline operational needs.

(5) Involved Departments

The project is led by the company’s Technology R&D Department, collaborating with the Insurance Business, Product Design, Data Compliance, and Operations & Service Departments. Each division plays a distinct role with close coordination: Technology R&D develops algorithms and systems; Insurance Business provides frontline requirements and domain knowledge; Product Design is responsible for feature and interaction design; Data Compliance ensures regulatory and privacy compliance throughout data collection, analysis, and storage; Operations & Service manages post-deployment iteration and frontline support.