Insurance Chatbots Improve Customer Experience and Cut Costs

How insurance chatbots use decision trees and natural language processing to meet insureds’ needs

The Automated Insurer | Customer Experience

By Mike de Waal | 1 October 2021

Key Points: Insurance Chatbot Optimization

Insurance chatbots are rapidly being deployed across all lines of business. Designed in line with best practices, with rich data to support them, insurance chatbots can improve the customer experience and increase customer service capacity.

In this article, we address the two major types of chatbots, as well as their respective strengths and challenges:

  • Rule-based Chatbots (Decision Trees)
  • AI-based Chatbots (Natural Language Processing)

The insurance industry’s sales and customer success teams are under pressure to deliver positive customer experiences faster than their competition. Customers expect an honest and positive experience in all end-to-end transactions like quoting, policymaking, and policy activation. Today, rising costs and long wait times have led carriers and brokers to deploy insurance chatbots to meet increased demand.

So, what makes a positive customer experience?

A recent report by PwC says nearly 80% of all American consumers point to speed, convenience, knowledgeable help, and friendly service as the most critical elements of a positive customer experience.

Traditionally, an insurance company’s customer service team would answer every customer’s phone call and email and talk about how to do business with the insurer.

In a lot of cases, traditional methods work great. The issue is scale.

Long wait times, language barriers, and a high volume of client calls and emails have increased personnel costs and led to poor prioritization of cases and a weakened customer experience for many insurers.

In response to these trends, rule-based and AI chatbots are changing the insurance industry’s customer service strategy:

  • Close to 30% of life and property insurers have used chatbots in their operations in the last five years.
  • Chatbots have become the leading application of AI in insurance within routine operations like customer service and lead management.
  • By 2026, chatbots will occupy 40% of all overall deployment within the insurance industry’s customer service roles.

Whether rule-based or AI-enabled, chatbots lift resource constraints and drive customer service strategies across the insurance sector. As a result, it is clear that chatbot deployment will remain a priority for insurers in the foreseeable future.

Let’s examine the two main types of chatbots that insurers widely deploy: rule-based and AI-based.

Insurance chatbots illustration showing a user interacting with a humanoid robot on a mobile screen

Rule-Based Insurance Chatbot Advantages

Rule-based chatbots follow a predesigned sequence of questions and commands that a given user would find helpful. To configure a rule-based chatbot, insurers must analyze data to anticipate what tasks users are most often trying to accomplish. Users will choose between various options (e.g., “Help me file a claim,” “Help me complete enrollment,” “How do I adjust my plan?”), and depending on their selection, the chatbot will direct them to the right resource.

A recent survey by Drift discovered the most common frustrations for customers are websites being hard to navigate, simple questions not being answered, and primary contact information for a business being too hard to find. Rule-based bots can improve the customer experience by quickly directing a user to the correct information immediately after being asked.

When a customer asks an unprogrammed question to a rule-based bot, it can immediately transfer the conversation to a human. This ensures the chatbot can resolve simple cases while freeing capacity to deliver better customer service for more complex issues.

Based on the responses given by the user and how the rules-based chatbot is programmed, the bot can either give a written reply back or trigger a task such as sending out an email, relocating the user to a different page, scheduling a meeting, or issuing an invoice.

Best Practices for Rule-based Chatbots in Insurance

High-quality rules-based chatbots rely on rich data sets to respond to various user actions. For example, an insurer can plan different chatbot sequences depending on whether the user has visited the website before, what insurance product they’re looking at, or if the user has spent lots of time clicking through the website with little reading (“Are you having trouble finding something?”).

Rule-based chatbots are highly valuable for maintaining existing business as well. For example, let’s imagine a client’s policy renewal is coming up. Instead of going through a manual renewal process and answering tedious questions with the client, you could program the bot to recognize the logged-in user and proactively ask the user if they want to purchase additional coverage or review their policy upon entering the website.

By effectively analyzing their user data, insurers can program a rules-based bot to appear with the right message, at the right time, to the right user, on the right device!

Insurance chatbots graphics

AI Chatbots: The Future of Customer Service in Insurance?

AI-powered chatbots pack more power than typical rule-based chatbots. As opposed to manually pre-programmed “if this, then that” rule-based chatbots, AI bots use algorithms and Natural Language Processing to develop human-sounding responses and collect data to learn from each interaction, improving over time. 

Natural Language Processing (NLP) refers to how artificial intelligence can give computers the ability to comprehend text and spoken words the same way humans can. NLP combines rule-based modeling of human language with complex machine learning models. These combined technologies allow AI to understand human writing and voice from various languages, making 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, at scale. AI chatbots use Neural Machine Translation engines to learn new 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.

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, AI chatbots can understand AI to mean “additional insured” instead of other words like artificial intelligence based on contextual data.

These machine learning capabilities of AI chatbots allow them to become more precise over time, as the bots learn from each interaction with users.

Best Practices for AI Chatbots in Insurance

Insurers can drastically reduce costs and turn-around time by adopting multilingual AI chatbots. According to a study by Juniper, 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, an established 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 2 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 in history. 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 under three seconds. 

AI chatbots clearly show great promise, however, there are important things for insurers to keep in mind before investing in AI. One key consideration when deploying AI chatbots is feedback mechanisms.

A feedback mechanism can be a simple question the bot asks the user. Some great questions include:

  • On a scale of 1-10, 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.

Infographic showing the differences between Rule-based and AI-based chatbots. It boils down to pre-programmed decision trees versus natural language processing.

What type of chatbot should insurers use?

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.

This doesn’t mean they’re a poor choice.

Every customer service team has three or four questions they get asked more than any other. Following the popular 80/20 rule, many insurers will be surprised to find how much time even a simple rules-based bot can save.

Regardless of how complex 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. Customer experience is a broad discipline encompassing an ever-growing number of 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.

Mike de Waal

Mike de Waal is president and founder of Global IQX, an Ottawa-based software provider of AI-driven sales and service solutions to employee benefits insurers.  He has deep experience in both software development and business management skills. Early in his career, he worked as a computer programmer and then went on to become a financial planner and a benefits consultant with giant Manulife Financial before becoming a tech entrepreneur.  He can be reached at [email protected].

Neon sign of a chat bubble with the word "Hello!"
Individual using a laptop under the night sky
Jim Harris Speaking on Digital Disruption in Employee Benefits

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