A chatbot is a computer program designed to simulate conversation with human users, typically through text or voice. It can answer questions, provide information, and assist with tasks, making it a helpful tool for customer support and other services. Chatbots have become an essential tool for businesses in the insurance and lending sectors, where they help streamline customer support, improve the speed of service, and provide answers to frequently asked questions. However, not all chatbots are created equal.

With advancements in artificial intelligence, chatbots have evolved from traditional, rule-based systems to more advanced, generative AI-powered systems. In this article, we'll explore the differences between these two types of chatbots, their unique features, and how each can be applied in the insurance and lending industries to enhance customer experience and optimise operations.

What Are Traditional (Rule-Based) Chatbots?

Traditional or rule-based chatbots are early types of chatbots that respond to users based on pre-written scripts and rules. One of the very first examples of a rule-based chatbot was ELIZA, developed by MIT professor Joseph Weizenbaum in 1966. ELIZA was designed to mimic human conversation by recognizing keywords in user input and pairing them with matching responses. For example, if a user mentioned “feeling sad,” ELIZA might respond with a scripted question like, “Why do you feel that way?” without truly understanding the context. Similarly, other early chatbots like PARRY (created in 1972) and Jabberwacky (in 1988) also relied on matching user inputs with scripted responses.

How Rule-Based Chatbots Work

Rule-based chatbots work by recognizing specific keywords, phrases, or questions and then providing a programmed response. If the user’s input matches a known keyword or question, the chatbot replies with a pre-written answer. However, if the input doesn’t match anything the bot has been taught to recognize, it cannot provide a helpful answer. Imagine a customer asks, “What’s my loan status?” A rule-based chatbot programmed with the keyword “loan status” might respond with, “You can check your loan status by logging into your account.” But if the user asks the question in a different way, such as “Can you tell me if my loan has been approved?” the chatbot may not understand, as it was not programmed to recognize that exact phrasing.

Key Characteristics of Rule-Based Chatbots

• Limited Responses: Rule-based chatbots can only respond to questions they’re explicitly programmed to understand. If a user goes beyond these topics, the chatbot is unable to help. For example, if a chatbot is set up to answer questions about loan terms, it might respond well to “What is the interest rate?” but may not understand “How do I qualify for a lower interest rate?” unless that exact phrase was anticipated.
• Scripted Flow: They follow a structured, step-by-step process, or “decision tree.” If users go off this path, the chatbot may struggle to provide the correct response. For instance, a rule-based chatbot helping with account login issues may ask users to select from options like “Forgot password” or “Locked account.” If the user’s issue doesn’t fit these options, they may have to start over or reach out to a human agent.
• Predictable and Reliable for Basic Questions: These chatbots are dependable for straightforward questions but may not handle more complex requests or provide conversational responses. For example, a rule-based chatbot might be very reliable for answering, “What’s the premium for my car insurance?” but may not be able to explain the differences between multiple policy options if the question is complex.

Applications of Rule-Based Chatbots in Insurance and Lending Customer-Facing Applications:

• Answering Frequently Asked Questions (FAQs): Customers often have standard questions like “When can I renew my policy?” or “What’s the status of my application?” Rule-based chatbots can easily answer these by recognizing keywords, providing quick, accurate responses without needing a human agent. For example, if a customer asks, “What’s my premium amount?” the chatbot responds with a standard answer based on their input.
• Basic Support for Simple Processes: For basic tasks, like resetting passwords, logging in, or providing information on standard products, rule-based chatbots are very efficient. They can guide customers through these steps, allowing them to get quick assistance. For example, a customer who says, “I forgot my password” would receive step-by-step instructions from the chatbot on how to reset it.

Internal-User-Facing Applications

Rule-based chatbots can also assist employees within insurance and lending companies by streamlining internal processes. These chatbots are helpful for:

• Assisting with Internal FAQs: Employees often need to look up information quickly, such as company policy guidelines or HR-related questions. Rule-based chatbots can instantly respond to questions like, “What’s the policy on remote work?” or “How do I submit an expense report?” For example, an employee asks the bot, “How do I apply for leave?” and the chatbot provides the leave application process, saving the employee time.
• Guiding Employees Through Standard Procedures: When employees need support for routine tasks like account setup, IT troubleshooting, or checking on internal protocols, rule-based chatbots offer fast, reliable guidance. For instance, an employee facing login issues could ask the chatbot, “How do I unlock my account?” and receive immediate steps to resolve the issue without waiting for IT support.

Rule-based chatbots work well for straightforward customer needs but struggle with complex or nuanced questions. Today, many companies are moving towards more advanced, AI-powered chatbots that can provide greater flexibility and personalization.

What Are Generative AI-Powered Chatbots?

Generative AI-powered chatbots are advanced chatbots that use machine learning and natural language processing (NLP) to understand and respond to questions in a flexible, conversational way. Unlike traditional rule-based chatbots, which are limited to set scripts, generative AI chatbots are trained on large datasets, allowing them to generate responses that feel more natural and are not restricted to specific keywords or phrases. To respond accurately to customer queries, generative AI chatbots need access to data from various sources. The chatbot has to have access to the insurance company’s internal systems, such as databases for policy information, customer records, and claims history. This allows it to retrieve relevant details, like the specific features of a policy or a customer’s claim status, and give accurate, personalised responses. If a customer asks, “What’s my claim status?” the chatbot can check the internal database and respond with the exact status without needing a human to look it up.

Key Characteristics of Generative AI Chatbots

• Adaptive Responses: Generative AI chatbots can respond to a wide range of questions, even if they weren’t specifically trained to handle them. They use machine learning to understand new topics, allowing them to answer unexpected questions more accurately. For example, if a customer asks a generative AI chatbot, “What’s the best coverage for my family’s health needs?” the chatbot can understand the request and provide suggestions, even if it hasn’t seen that exact question before.
• Self-Learning: These chatbots learn from past conversations, improving over time based on customer interactions. This means that the more they are used, the better they get at answering complex questions. For instance, if multiple customers ask for information about flexible payment options, the chatbot learns to provide a helpful answer more effectively and may even start offering this option proactively.
• Natural Conversations: Generative AI chatbots are designed to hold conversations that feel human-like. They can understand the context of a conversation, recognize the tone, and respond in a friendly, helpful way. If a customer starts with, “I’m feeling confused about my coverage options,” the chatbot can pick up on this sentiment and respond empathetically, saying, “I understand! I can help explain your options step-by-step.”

Applications of Generative AI Chatbots in Insurance and Lending Customer-Facing Applications:

• Personalized Customer Support: Generative AI chatbots can provide tailored advice based on the customer’s specific circumstances, like recommending insurance plans suited to their needs or helping customers understand loan terms. For example, a customer might say, “I want insurance that covers dental and vision but is affordable.” The chatbot can analyze options and suggest the best plans.
• Complex Query Handling: These chatbots can manage multi-step interactions, helping customers navigate complicated processes like filing claims, checking loan approvals, or changing policy details. For instance, a customer could say, “I want to file a claim for my recent accident and also know if I’m eligible for a rental car.” The chatbot can walk through the claim process and provide information on additional benefits.
• Proactive Engagement: Generative AI chatbots can initiate conversations based on triggers, like reminding customers about upcoming payments, policy renewals, or new offers. A chatbot might say, “Your car insurance policy is up for renewal next month. Would you like me to help you review your coverage?”

Internal-User-Facing Applications

• Intelligent Assistance for Employees: AI chatbots can assist internal users by quickly fetching information from complex databases and helping with tasks like underwriting, risk assessment, or compliance checks. For example, an employee could ask, “What are the recent claims for high-risk policies?” and the chatbot retrieves and summarizes this data.
• Training and Onboarding: Generative AI chatbots can act as interactive trainers for new employees, answering questions about company policies or procedures dynamically. New hires might ask, “How do I use the claims processing system?” and the chatbot can guide them step-by-step, making training faster and more efficient.
• Process Automation: Generative AI chatbots can help automate repetitive tasks, such as data entry or generating reports, freeing employees to focus on higher-value work. For instance, the chatbot might automatically generate a daily summary of loan approvals and send it to the relevant team.

Comparing Rule-Based and Generative AI Chatbots

• Flexibility: Rule-based chatbots are limited to predefined scripts, while generative AI chatbots can adapt to new questions and scenarios dynamically.
• Understanding Natural Language: Generative AI chatbots have advanced NLP capabilities, allowing them to understand varied phrasing and context, whereas rule-based chatbots rely on keyword matching.
• Complexity of Interactions: Generative AI can handle multi-turn conversations and complex queries; rule-based chatbots work best for simple, linear interactions.
• Training and Maintenance: Rule-based chatbots require manual updates for new questions or processes. Generative AI chatbots improve automatically through machine learning but require initial training on large datasets.
• Use Cases: Rule-based bots excel at answering FAQs and guiding users through standard procedures, while generative AI bots are better for personalized advice, complex support, and proactive engagement.

Which Chatbot Is Right for Your Insurance or Lending Business? The choice between a rule-based chatbot and a generative AI chatbot depends on your business needs, budget, and the complexity of customer interactions you want to support. If your primary goal is to handle common questions quickly and reduce call center load, a rule-based chatbot may be a cost-effective solution. If you want to provide a more personalized, conversational experience that can handle complex queries and improve over time, investing in a generative AI-powered chatbot is worthwhile. In many cases, companies combine both approaches, using rule-based chatbots for simple tasks and escalating to AI-powered bots or human agents when the conversation requires more sophistication.

Conclusion

Chatbots are transforming the insurance and lending industries by improving customer service, reducing operational costs, and enabling faster access to information. Traditional rule-based chatbots offer reliability and ease of implementation for standard questions, while generative AI chatbots provide advanced conversational abilities that adapt to customer needs. Understanding the differences between these technologies helps businesses choose the right solution to enhance customer experience and operational efficiency in a rapidly evolving digital landscape.

"AI is not just a tool; it's a game-changer for insurance companies looking to stay ahead. By helping us understand customer needs faster and refine our products more efficiently, AI allows us to deliver policies that truly resonate with people. It's about combining data-driven insights with a human touch to create insurance solutions that matter."

Using AI to Update Existing Insurance Products

1. Review Policy Performance

Tasks:
Analyze customer feedback, claim trends, and complaints to identify issues.
Check if competitors have introduced better features or pricing.
Evaluate changes in regulations or market conditions. How AI Adds Value: AI analyzes vast amounts of feedback and claims data to identify trends, such as frequent complaints about specific exclusions. It also tracks competitor pricing and features to highlight areas where the policy lags. For example, AI might detect a surge in customer dissatisfaction due to high claim rejection rates tied to outdated exclusions in a health insurance policy. Limitations: AI may not be able to fully interpret qualitative nuances from customer complaints, such as emotional factors influencing dissatisfaction.

2. Identify Areas for Improvement

Tasks:
Add new features (e.g., wellness benefits in a health plan).
Modify exclusions or adjust coverage limits to meet current needs.
Revise premiums to reflect updated risk models. How AI Adds Value: AI can simulate the impact of adding new features or revising premiums by analyzing historical data and predicting customer behavior. Limitations: AI cannot finalize which features align best with the company's strategy or customer relationship goals. These decisions require human oversight.

3. Consult Stakeholders

Tasks:
Engage with brokers, agents, and customers to validate proposed updates.
Discuss with legal and compliance teams to ensure changes meet regulatory requirements. How AI Adds Value: AI can summarize stakeholder feedback from surveys or focus groups, making it easier to identify consensus on proposed updates. It also flags potential compliance risks by cross-referencing regulatory databases. Limitations: Building trust and gathering qualitative input require human interaction. AI cannot replace the nuanced discussions with stakeholders or interpret their emotions and intentions.

4. Test Changes

Tasks:
Roll out updates to a small customer segment to gauge acceptance.
Monitor claim ratios and overall customer satisfaction for the updated plan. How AI Adds Value: AI accelerates analysis during pilot testing by quickly processing feedback and claim ratios. Limitations: AI relies on data volume and may struggle with limited pilot test data, making it less effective for smaller test groups.

5. Implement and Communicate Updates

Tasks:
Fully integrate changes into the policy offerings.
Inform existing policyholders of the updates and how they impact them.
Update marketing materials and train distribution channels. How AI Adds Value: AI personalizes communication by tailoring messages based on customer profiles. For example, it can generate email campaigns explaining updates in a way that resonates with different demographic groups. AI also optimizes marketing strategies by identifying the most effective channels for outreach. Limitations: AI cannot always convey the emotional reassurance often needed in communication about policy changes. Human input remains essential for sensitive messaging.

6. Monitor the Revised Policy

Tasks:
Continuously track the policy’s performance post-update.
Assess whether the changes improved customer satisfaction, claim ratios, or profitability. How AI Adds Value: AI provides real-time tracking of metrics like adoption rates and claim ratios. Limitations: AI cannot interpret broader implications of performance metrics, such as reputational impact or long-term customer trust, which require human analysis.

Final Thoughts

AI is transforming how insurance companies develop and update their products. It streamlines market research, analyzes customer behavior, and provides actionable insights that allow insurers to respond quickly to changing demands. While humans remain essential for conducting primary research and bringing creativity to product design, AI complements these efforts by offering efficiency and precision. By embracing AI and combining it with human expertise, insurers can stay ahead in a competitive industry while better meeting their customers’ needs.