Generative AI powered Customer Service in Insurance

This News Covers

• Which insurance company launched AI voice bot?
• How AI can improve customer service?
• What is generative AI for customer segmentation?
• What are the risks of AI in insurance?

Which insurance company launched AI voice bot?

Edelweiss General Insurance (EGI) has made a significant stride in the insurance industry by launching an AI voice bot to facilitate motor claim registrations. This innovative move aims to provide customers with a seamless and convenient way to intimate insurance motor claims.

The AI voice bot, powered by Yellow.ai, is a conversational tool that can interact with customers in Hindi, English, and Hinglish. It enables real-time registration of claims 24/7, thereby enhancing the customer experience by providing round-the-clock support. The bot has been initially introduced for garage owners, with plans to extend the service to all customers in the coming months.

EGI's AI Voice Bot:

1. Initially introduced for garage owners, the service is planned to be extended to all customers in the near future.
2. Beyond claim registration, the bot can answer questions raised by garage representatives, providing necessary information and guidance.
3. The introduction of the AI voice bot enhances customer experience by providing round-the-clock support and streamlines the claim handling process.
4. The AI voice bot leads to faster claim resolution, improved customer satisfaction, and increased operational efficiency for the insurance company.

The launch of the AI voice bot by EGI is a significant step towards leveraging AI in the insurance industry to enhance customer experience, streamline operations, and mitigate risks.

How AI can improve customer service?

Artificial Intelligence (AI) can significantly enhance customer service in various ways:

1. Chatbot-Based Customer Support: AI can automate interactions, making them ideal for customer support.
2. Robotic Interpretation of Customer Voicemails: AI can transcribe and analyze customer voicemails, saving time and improving accuracy before the call reaches a human service representative.
3. Round-the-Clock, Round-the-Year Support: AI can provide automated customer service across all channels, ensuring availability and responsiveness at all times.
4. Improved Human Interactions with Customers: AI can augment human interactions with customers through AI-augmented messaging and AI email tagging.
5. Personalized User Experiences in a Site’s FAQs: AI can analyze large data sets and suggest relevant self-help content to customers based on their past behavior and location.
6. Giving Greater Meaning to Customer Data Touchpoints: AI can analyze behavioral patterns of customers and the sentiment of their emails and chat requests to improve responses over time.
7. Predictive Insights: AI can create personalized experiences for customers, making it easier to avoid problems before they occur.
8. Smart Task Management: AI can help customer service agents manage follow-up tasks in a comprehensive and timely manner.
9. Real-Time Writing Assistance: AI can assist customer service agents in producing grammatically correct and well-researched responses.
10. Product Innovation: AI can analyze customer data to find opportunities for product extensions or new product innovation.

These are just a few ways AI can enhance customer service. The key is to leverage AI's capabilities to improve efficiency, accuracy, and personalization in customer interactions.

What is generative AI for customer segmentation?

Generative AI can significantly enhance customer segmentation in analytics. It can identify customer patterns and similarities by analyzing large amounts of customer data, including demographic, psychographic, and behavioral data. This allows for the creation of more accurate and effective customer segments.

For instance, a retail company can generate customer segments based on their shopping behavior, such as frequency of purchase, type of product purchased, and average purchase value. Generative AI can be used for customer segmentation in analytics in the following ways:

1. Automated Feature Engineering: It can automatically generate and select the most relevant features for segmentation.
2. Customer Clustering: It can group customers into distinct segments based on their similarities.
3. Predictive Modeling: It can predict future customer behavior based on their past behavior and other characteristics.
4. Personalized Recommendations: It can provide personalized product or service recommendations based on the specific characteristics and preferences of each customer segment.
5. Sentiment Analysis: It can analyze customer reviews, social media posts, and other forms of customer feedback to understand the sentiments of different customer segments.

By providing valuable insights into customer segmentation, generative AI enables businesses to better understand and engage with their customers, develop targeted marketing campaigns, and improve customer retention. It helps companies gain a competitive advantage in their markets and improve their bottom line.

What are the risks of AI in insurance?

The adoption of AI in the insurance industry, while promising, comes with certain risks and limitations. Here are some of them:

1. Data Confidentiality: AI development has enabled the collection, storage, and processing of information on an unprecedented scale. This raises concerns about data confidentiality and privacy. For instance, when a generative AI system is fed with corporate data to produce a summary of confidential corporate research, a data footprint is left on the external cloud server of the AI, potentially accessible to competitors.


2. Security: AI algorithms optimize the training data that gives the AI its ability to provide insights. If the parameters of an algorithm are leaked, a third party may be able to copy the model, causing economic and intellectual property loss to the owner. Additionally, if the parameters of the AI algorithm model are modified illegally by a cyber attacker, it can cause the performance deterioration of the AI model and lead to undesirable consequences.


3. Transparency: The 'black box' characteristic of AI systems, especially generative AI, makes the decision process of AI algorithms hard to understand. This is particularly important in the insurance sector, a financially regulated industry where the transparency, explanability, and auditability of algorithms is of key importance to the regulator.


4. Inaccuracy: The performance of an AI system heavily depends on the data from which it learns. If an AI system is trained on inaccurate, biased, or plagiarized data, it will provide undesirable results, even if it is technically well-designed.


5. Abuse: Even if an AI system is operating correctly, it still has the risk of abuse. The operator use, purpose, method, range, and so on could be perverted or deviated and meant to cause adverse effects. One example of this is facial recognition being used for the illegal tracking of people's movement.


6. Overreliance: Overreliance on AI occurs when users start accepting incorrect AI recommendations, making errors of commission. Users have difficulty determining appropriate levels of trust because they lack awareness of what the AI can do, how well it can perform, or how it works. A corollary to this risk is the weakened skill development of the AI user. For instance, a claims adjuster's ability to handle new situations or consider multiple perspectives may deteriorate or be restricted to only cases to which the AI also has access.