Traditional vs Gen AI Chatbots: A Guide for Insurance and Lending Companies

Discover the key differences between traditional and Gen AI chatbots and their usefulness for insurance and lending companies.

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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.

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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.

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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.

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Applications of Rule-Based Chatbots in Insurance and Lending

Customer-Facing Applications:

Rule-based chatbots help customers directly by providing quick answers and support for common inquiries. These bots work well for:

• 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.

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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.

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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:

Generative AI chatbots enhance customer experiences by providing personalised, real-time assistance for complex needs. Here are some common uses:

• Personalized Policy or Loan Recommendations: Generative AI chatbots can analyse a customer’s history and preferences to suggest the best insurance policies or loan options. This level of personalization helps customers feel valued and makes the process faster and easier. Suppose a customer tells the chatbot they are interested in a low-risk investment. The chatbot could then recommend an insurance policy that fits this preference, like a policy with lower premiums and fewer risks.
• Guiding Customers Through Complex Processes: Generative AI chatbots can walk customers through detailed processes, such as applying for a loan or making a claim, by explaining each step in a personalised way. For example, when a customer applies for a loan, the chatbot can guide them through each stage, like verifying income and uploading documents, making the experience smoother and less intimidating.
• Detecting and Preventing Fraud: By recognizing unusual patterns, generative AI chatbots can help identify suspicious activity, potentially preventing fraud before it escalates. For example, if a customer makes multiple high-value claims in a short time, the chatbot can flag this for a deeper review, helping to protect both the customer and the company from fraud.
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Internal-User-Facing Applications

Generative AI chatbots also benefit employees within insurance and lending companies by automating support and improving efficiency:

• Assisting Employees with Knowledge Gaps: Internal employees often have questions about products or policies. Generative AI chatbots can act as a knowledgeable assistant, providing quick answers to policy details, loan structures, or compliance requirements. For example, if an employee is uncertain about underwriting criteria, they could ask the chatbot, “What’s the standard loan-to-value ratio for home loans?” The chatbot can quickly provide the correct information, helping the employee respond confidently to customers.
• Providing Real-Time Support for Operations: Generative AI chatbots can assist employees by analysing customer data and guiding them through underwriting or claims evaluation processes, for example,  which can speed up approvals and reduce the chance of human error. For instance, an underwriter processing a complex claim could ask the chatbot to summarise similar cases, allowing them to make a more informed decision.
• Analysing and Predicting Customer Needs: Generative AI chatbots can analyse interactions and data to predict what customers may need next, allowing employees to proactively reach out with relevant solutions or upsell offers. If the chatbot recognizes that a customer has shown interest in health insurance add-ons, it could prompt the employee to offer add-on recommendations during their next interaction.

Generative AI chatbots have transformed how insurance and lending companies interact with both customers and employees. 

By providing personalised, intelligent assistance and having access to vast datasets, these chatbots not only improve customer satisfaction but also enhance internal efficiency, making them a powerful tool for the modern insurance and lending landscape.

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Key Differences Between Traditional and Generative AI Chatbots

Let’s break down some of the biggest differences between these two chatbot types:

Scalability and Flexibility:

Traditional chatbots are less flexible because they rely on fixed scripts and cannot handle conversations that stray from predefined scenarios. Generative AI chatbots, however, are much more scalable and can handle a variety of complex customer inquiries, making them suitable for evolving demands.

Customer Experience:

Rule-based chatbots provide a straightforward but sometimes rigid experience that may not fully satisfy customer needs. Generative AI chatbots, on the other hand, offer a smoother, more natural interaction, which often results in higher customer satisfaction.

Implementation Complexity and Costs:

Setting up and maintaining a rule-based chatbot is usually simpler and less costly initially, as it only requires scripting for common inquiries. While generative AI chatbots have higher setup costs, they often save time and resources over the long term due to their adaptability and self-learning capabilities.

Integration with Other Systems:

Both types of chatbots can be integrated with company systems, but generative AI chatbots can handle complex integrations more smoothly, particularly in areas like customer relationship management (CRM), underwriting, and claims processing.

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Benefits of Generative AI-Powered Chatbots for Insurance and Lending

Generative AI chatbots offer several key advantages in insurance and lending, including:

• Enhanced Customer Engagement: Gen AI chatbots can interpret subtle language and tone cues, which allows for a deeper connection. Imagine a user asking about a policy update. Rather than a generic response, a Gen AI bot can provide a clear, tailored explanation on how the change impacts the user’s coverage, creating a more personalised experience.
• Operational Efficiency: They automate processes like onboarding, claims processing, and loan application assistance, being able to take in data and documents and also conduct KYC and authentication, which reduces human intervention and speeds up services.
• Data-Driven Insights: Gen AI chatbots can analyse user interactions to identify frequent questions, recurring concerns, and emerging needs. Companies can use this data to refine services, enhance customer support, and even develop new products.
• 24/7 Multilingual Support: Gen AI chatbots never clock out. They offer continuous support across different languages, making them ideal for companies with a diverse, global customer base. This is especially valuable in financial services, where clients often need assistance beyond standard business hours.
• Scalability and Flexibility: Unlike traditional bots, Gen AI chatbots are easy to scale and customise. For instance, an insurance chatbot could seamlessly shift from discussing health insurance to auto insurance depending on the user’s query.

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Himanshu Gupta, COO of Iorta Technology Solutions, notes, "Gen AI chatbots have completely changed the game for customer interactions. They’re smarter, more flexible, and actually understand people, unlike traditional bots that often feel robotic. With the ability to hold real conversations and tap into data insights, these chatbots make it easier for businesses—especially in insurance and lending—to genuinely connect with and support their customers."

Challenges and Limitations

Despite their advantages, generative AI chatbots come with some challenges:

• Data Privacy and Compliance: The use of AI in financial services brings up data privacy concerns, as handling sensitive customer data requires strict adherence to regulatory standards.
• Model Accuracy and Hallucinations: Generative AI models sometimes “hallucinate” or generate incorrect responses. For insurance and lending companies, accuracy is critical, as misinformation could damage customer trust.
• Complexity in Deployment and Maintenance: Generative AI requires more technical expertise for setup, training, and ongoing maintenance, which may be resource-intensive for some organisations.

How to Choose the Right Chatbot Solution

Choosing between a rule-based and a generative AI-powered chatbot depends on your organisation’s needs and goals.

• Evaluate Business Needs: Consider your business context and customer behaviour to evaluate whether a straightforward solution would suffice or if more nuanced capabilities are required.
• Assess Information Security: While most LLMs can run locally on private cloud or on-prem servers, it's important to assess the way data traversing takes place during the journey of parsing an input and inferring for the output. This is where well-designed info-sec guidelines can really safeguard a company's interests with modern AI packages.
• ROI Considerations: Balance the upfront costs with the long-term benefits. Generative AI is often more cost-effective in the long run due to reduced need for human intervention.

In summary, both traditional and generative AI-powered chatbots have their places in the insurance and lending industries. 

Rule-based chatbots provide a simple, low-cost solution for common questions, while generative AI-powered chatbots offer an adaptable and natural conversational experience that can meet complex needs. 

Newer AI models are becoming even better at understanding human language and may include multimodal capabilities, integrating voice and visual inputs along with text. 

However, as AI chatbots improve, companies will still need human oversight to ensure high-quality service and prevent any potential issues with accuracy.

For companies looking to enhance customer service and streamline operations, especially in industries as customer-centric as insurance and lending, generative AI-powered chatbots can provide a strong competitive advantage.

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How We Can Help

At iorta, we can help insurers and lenders integrate Gen AI-powered chatbots into their internal and customer-facing operations using our platform, Dot. 

Get in touch with us at hello@iorta.in to learn how we can help improve your processes and customer experience through AI.

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