Why do Insurers Choose to Go the Gen AI Route? 

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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. 

Why are Insurers Adopting GenAI? 

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: 

• To improve customer experience by deploying real-time virtual assistants and AI agents. These agents automate repetitive queries, provide employees with quick access to real-time knowledge bases, and reduce long waiting times 

• GenAI automates large volumes of data, including past call recordings, policy documents, notes, and legal paperwork. This means empowering customers by enabling self-service and automating parts of the insurance lifecycle. GenAI also enhances agent’s productivity and helps them invest more time in solving complex queries 

• Compliance is regulated, and compliance plays a key role in the insurance industry. GenAI simplifies agents’ jobs with automated compliance monitoring, content creation, and fraud detection tools

There are multiple cases of GenAI being used in insurance. Let’s discuss these in the next section. 

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Use Cases of GenAI in the Insurance Sector 

Here are some of the most popular GenAI use cases for the insurance sector in 2024: 

Enhanced customer support with AI agents and in-product widgets 

While there are multiple ways to deploy GenAI into insurance customer support, here are a few unique ways that stood out to us: 

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GenAI-powered support agents 

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: 

• To know customers better and understand their queries extensively 
• Summarize complex issues to find answers faster 
• Identifying solutions to complex queries easily with in-depth product knowledge 

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Case Study of a Leading Asian Bank

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. 

Read entire case study here

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AI-based in-product widgets 

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: 

• Get answers to their queries based on urgency and how much in a hurry they are. For example, if a customer simply has an account issue’, the assistant responds with quick and actionable answers based on popular themes 

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• Alltius’ in-product assistant doesn’t answer your queries based on assumptions. It doesn’t mislead the customers and route them to appropriate workflows to resolve their queries faster 

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• Alltius’ retrieval system adjusts knowledge management segments to rank them based on the popularity and acceptance of the end-users 

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Case study of AngelOne: Leading FinTech Brand

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

Improve sales team’s efficiency with sales excellence assistants 

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: 

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• Understanding the prospects’ profiles by speaking directly with your CRM software 
• Learning specific features and details about the product without seeking the product team’s assistance every time 
• Generating competitor battle cards instantly to increase the conversion potential of sales calls 

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Case Study of Assurance IQ: An Insurance Company 

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

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Other popular use cases of GenAI in insurance 

• Automated underwriting: Involves using GenAI technology to assess risks related to insurance plans without manual interventions  
• Insurance claim processing: Process insurance claims automatically with GenAI technology to extract and process data from documents like forms, medical records, legal documents, and receipts 
• Fraud detection: Detecting fraud activities and unauthentic access attempts, patterns with machine learning algorithms to take fast actions
   

Challenges of GenAI Adoption in the Insurance Industry 

While deploying GenAI in the insurance industry, insurance carriers may face the following challenges: 

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Privacy and security concerns 

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. 

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Ensuring output accuracy 

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. 

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Integrating with existing applications 

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. 

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Conclusion 

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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Want to explore how? 

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