Natural language processing refers to how artificial intelligence can give computers the ability to comprehend text and spoken words like humans can. NLP combines rule-based modeling of human language with complex machine-learning models. These combined technologies allow AI to understand writing and voices from various languages. AI makes it easy to translate languages, respond quickly to verbal requests and summarize large amounts of text in a quick, digestible manner.
One advantage of AI chatbots is they enable insurers to serve customers in their preferred language—whether it’s English, French, Spanish or another language— at scale. AI chatbots use neural machine translation engines to learn languages. Machine translation refers to the set of tools that allow users to input text in one language that generates an instant translation to a different language. Google Translate is the best-known example.
An AI chatbot can even learn slang and understand different acronyms. It’s vital for bots to understand insurance acronyms and not confuse them with the incorrect phrase. For example, based on contextual data, AI chatbots can understand “AI” to mean “additional insured” instead of “artificial intelligence.”
These machine-learning capabilities allow AI chatbots to become more precise over time as the bots learn from each interaction with users. Insurtech vendors are forming partnerships with AI vendors to advance the state of the art.
Best Practices for AI Chatbots in Insurance
Insurers can drastically reduce costs and turnaround time by adopting multilingual AI chatbots. According to a study by Juniper Research, using conversational AI chatbots for insurance will lead to cost savings of about $1.3 billion by 2023 across life, property and health insurance.
When Generali, the big Swiss insurer, installed an AI-enabled email chatbot, the bot was able to:
- Triage incoming emails
- Obtain open invoices
- Forward the emails to the correct department automatically and understand the urgency and tone of the email
As a result, Generali increased Level 1 support capacity by 40%, and it now takes under two seconds for the AI chatbot to send an email to the correct department 24/7.
In 2016, Lemonade’s AI chatbot set a world record for the fastest processed insurance claim. The chatbot received a claim for a $979 coat, checked the claim against the policy, ran 18 different anti-fraud algorithms and made the payment – all in less than three seconds.
Customer-Feedback Mechanisms
AI chatbots clearly show great promise. However, there are important things for insurers to keep in mind before investing in AI. One key consideration is the customer-feedback mechanism.
A feedback mechanism can be a simple question the bot asks the user. Some great questions include:
- On a scale of one to 10, how would you rate our conversation?
- What did you enjoy the most about our discussion?
- What can I improve on?
- What else are you looking for?
- Is there anything else you’d like to mention about our interaction?
AI is only as good as the data it is based on. By validating the AI program early and often with real user feedback, insurers can invest in AI sustainably and avoid costly AI mistakes down the road.
Type of Chatbot
The ideal chatbot solution for an insurance company depends on how much data your marketing and client-support teams can collect and analyze effectively. AI chatbots have a clear advantage in their ability to learn through each interaction and provide helpful responses to a broader set of inquiries. However, their reliance on big data and various machine learning and NLP techniques make AI chatbots a heavy lift for some carriers facing constraints in other areas of their business.
Rules-based chatbots are quick for insurance companies to implement but less flexible than their AI-enabled counterparts.
Regardless of how great your insurance chatbot is, customers still value human connection. According to a survey by HubSpot, 81% of consumers would rather interact with a live human agent than an electronic system. Insurers need strategies to persuade that group to give chatbots a chance to help them.
Customer experience is a broad discipline encompassing multiple channels, both digital and non-digital. Striking a balance between approaches is key, and it will require some trial and error to determine when a chatbot should let a human take over to best meet your customer’s needs.
The future is coming fast, and insurers should consider both rule-based and AI-powered chatbots.
