April 30, 2024

Weekly Business Insights 04/30/2024

Generative AI for Customer Support in Insurance. Sentiment Analysis, Identifying Common Customer Inquiries, Generating Content to Explain Regulations, Summarizing Customer calls, and more.

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Generative AI for Customer Support in Insurance

Sentiment Analysis: Generative AI can analyze customer conversations to gauge their sentiment. This can be used to identify frustrated or upset customers and route them to a live agent for more personalized support.

Identifying Common Customer Inquiries: Generative AI can analyze past customer support interactions to identify the most frequently asked questions. This information can be used to develop FAQs, chatbots, or other self-service options that can help customers find answers to their questions without needing to speak to an agent.

Generating Content to Explain Regulations:  When regulations change, insurance companies need to update their customer support materials. Generative AI can be used to quickly create clear and concise explanations of new regulations, helping agents answer customer inquiries accurately.

Summarizing Customer Calls:  AI can analyze recordings of customer support calls and automatically generate summaries of the key points discussed. This can save agents time and help them identify trends in customer inquiries.

Detecting Non-Compliance: Generative AI can be used to monitor customer support conversations for potential compliance violations. For example, AI can flag instances where agents may be making unauthorized promises or providing inaccurate information.

Creating an Interactive Knowledge Base:  Generative AI can help organize and summarize existing information about insurance products, policies, and procedures. This can be used to create a user-friendly knowledge base that allows agents to quickly find the information they need to answer customer questions.

Personalized Policy Recommendations: Generative AI can analyze a customer's needs and claims history to recommend additional insurance products or policy upgrades. This could be particularly helpful for upselling or cross-selling relevant coverage to existing customers.

Chatbots with Context: While traditional chatbots can answer basic questions, generative AI chatbots can leverage claims data and customer history to provide more context-aware responses. Imagine a customer inquiring about the status of a claim. A generative AI chatbot could access the claims data, identify the specific claim, and provide a real-time update on its status.

Using Claims Data for Personalized Support:

Predictive Issue Resolution: By analyzing past claims data and customer interactions, generative AI can predict potential issues and proactively reach out to customers with solutions. For example, If a patient with a history of high blood pressure has a smartwatch that tracks his heartrate and activity levels, based on the patient's medical history, Generative AI can prompt the patient to check blood pressure at home using a home blood pressure monitor.

Personalized Risk Mitigation Tips: Generative AI can analyze a customer's policy and claims history to identify potential risks. It can then generate personalized tips and recommendations to help the customer mitigate those risks. For example, if a customer has a history of filing recurring hospitalizations for asthma, generative AI could suggest, Enrolling in an asthma management program.

By automating these tasks, Generative AI can free up customer support agents to focus on more complex issues thatrequire human judgment. This can lead to improved customer satisfaction and amore efficient customer support operation.

Additionally, generative AI can also be used to:

·       Generate content for emails and letters to customers.

·       Assist agents during live chats by suggesting responses or providing relevant information.

·       Personalize customer interactions by tailoring responses to individual customer needs.

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