Generative AI has multiple use cases in the insurance industry, the most common one being customer experience. A McKinsey survey performed among 8500 insurance customers based in North India clearly established that customer experience is a strong predictor of insurance carrier selection.
What better way to improve customer experience than leveraging GenAI? GenAI-driven chatbots, knowledge bases, and personal assistants improve the efficiency of customer support teams, leading to higher user satisfaction among insurance customers.
This article will discuss the strategic importance of GenAI in the insurance industry and specific use cases.
The same McKinsey survey mentioned above showed that 40% of insurance customers canceled their plans because the plans were not valuable to them. This explains a huge gap in communication between insurance carriers and customers. Many such gaps can be filled with GenAI tools.
Insurance carriers are adopting GenAI primarily for three different reasons:
There are multiple cases of GenAI being used in insurance. Let’s discuss these in the next section.
Here are some of the most popular GenAI use cases for the insurance sector in 2024:
While there are multiple ways to deploy GenAI into insurance customer support, here are a few unique ways that stood out to us:
Powerful AI support agents like Alltius convert historical customer conversations into real-time knowledge bases. To launch your AI assistant, you add your knowledge sources to the tool, select your desired skills from a list of 50 canned skills, and launch it. Your AI assistant will automatically improve its responses over time.
An AI-powered support agent helps human agents:
A leading Asian bank contacted Alltius to improve its product discovery. They wanted to create an AI assistant that provides clear, concise responses to customer queries like "Which credit card is most suitable for someone with a $10,000 monthly income who spends on dining and travel?”
Alltius created a product discovery assistant by processing over 3000 product collateral pages within minutes. The bank then launched a chatbot through its web widget channel, which reduced the time spent on product information from 15 minutes to 1 minute.
Alltius’ in-product widgets provide users with self-learning, humane in-product AI assistants, and widgets that help end-users become familiar with the product. This helps increase the adoption rate of insurance products.
With in-product widgets, insurance customers can:
AngelOne, a FinTech company based in India, was struggling to handle the increased volume of customer support tickets. The typical waiting time to find answers to queries would be as high as 30 minutes. The quality of responses was also not uniform due to disparity in the knowledge levels of the agents.
AngelOne used Alltius to create test assistants and support agents and trained them with over 20,000 multilingual web pages. Within three months, the Alltius assistants went live and reduced the contact ratio by 15%, with 100000 interactions per week.
Read the entire case study here
Another great use case of an AI chatbot is creating sales assistants. A GenAI-powered sales assistant helps sales reps to prepare better for sales calls, assists sales reps with in-call insights, and reduces ramp-up time of sales reps by 75%.
Some of the major uses of a sales assistant include:
Insurance provider Assurance IQ had over 6000 insurance plans, and it was difficult for agents to stay updated on each plan's specific terms and conditions. The problem was more evident for new agents as the ramp-up time increased to six months.
Alltius distilled the top 10 relevant plans for up to five specific needs and created an AI assistant that instantly answers queries like, "My client is a 60-year-old woman in Atlanta who utilizes physical therapy, has glaucoma, and prioritizes dental benefits. Which plan would best suit her?", freeing up ramp-up time for agents.
Read the entire case study here
While deploying GenAI in the insurance industry, insurance carriers may face the following challenges:
Challenge: Insurance carriers rely heavily on customer data. If AI models don’t follow the relevant data protection compliances and are trained on sensitive data, this could lead to data infringement and penalties.
Solution: Whether insurance carriers are developing AI models internally or relying on third-party AI vendors, they must stay compliant with all relevant data protection guidelines, such as GDPR, PIPL, etc.
Challenge: A major challenge with AI algorithms is generating manipulated, incorrect outputs. If not instructed correctly, GenAI tools often end up providing flawed, assumption-based outputs to users. For insurance carriers, this would mean misleading the end-users and affecting their experience.
Solution: If you rely on third-party AI vendors, understand how their technology works. Instead of investing in an AI tool immediately, opt for a free trial to assess the accuracy of the tool’s output before making it live.
Challenge: Unless an AI tool is integrated with all relevant data connectors and sources, there is no point in deploying it. However, many new AI service providers neither support integration with workplace solutions nor have APIs to connect through workflow automation software. This leads to significant frictions in organizational systems.
Solution: Assess carefully the types of integrations an AI vendor offers. Also, avoid using multiple tools for a single AI tool to automate various operations end-to-end. Relying on multiple AI tools to automate a single workflow increases friction.
If you are planning to introduce GenAI-powered assistants to improve insurance customer experience, Alltius is a great tool to explore.
Alltius’ support assistant, in-product widgets, and sales assistants have helped insurance and other businesses close sales and support queries 100X faster, increase user engagement by 140%, and increase self-serve operations by 80%.
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