Generative AI refers to the branch of artificial intelligence that focuses on creating content, such as images, texts, music, claims data, and more. It uses complex algorithms and neural networks to generate realistic and creative outputs based on patterns and data it has been trained on.

In the insurance industry, generative AI can be employed to streamline processes, enhance decision-making, and improve customer experiences.

Our research report surveyed more than 100 UK and US insurers about their thoughts on using generative AI within their businesses. We discovered that 59% of US and UK insurers are already using generative AI.

Download the free report: Generative AI in the Insurance Industry

However, there are a number of misconceptions surrounding the application of generative AI in insurance that we’ll debunk in this blog.

1. AI and automation will completely take away jobs

A common misconception is that AI and automation will completely replace claims handlers and underwriters, rendering their expertise obsolete. However, rather than replacing jobs, it’s more accurate to say that we can delegate tasks to AI. By automating routine and repetitive processes, employees can focus on more complex and value-added activities, such as analysing risks, developing tailored solutions, and providing personalised, empathetic customer service. Generative AI is a powerful tool that complements people’s skills, empowering them to provide a human touch and make more informed decisions efficiently.

Read more: 8 insurance functions that can benefit from generative AI

2. Generative AI will replace human expertise

On a similar note, generative AI is not designed to replace human expertise. Instead, it serves as a tool to augment human capabilities. Insurance professionals bring invaluable domain knowledge, experience, and judgement to the table. Generative AI can assist in processing vast amounts of data, identifying patterns, and generating insights, human interpretation and decision-making are still required to contextualise and apply those findings effectively.

3. Generative AI will eliminate the need for human interaction in customer service

Generative AI can be used in the automation of certain customer service tasks, such as basic inquiries or routine processes. However, it does not eliminate the need for human interaction. Insurance is a complex industry that often involves emotionally charged situations, nuanced discussions, and personalised outcomes. Human empathy, understanding, and expertise are essential to providing a positive customer experience. Generative AI can help claims handlers by providing relevant information and recommendations, as well as speeding up tasks. As a result, they can serve customers more efficiently and empathetically.

4. Generative AI will make the insurance industry less human-centric

Read more: How can insurers create people-centric business with generative AI?

You might think that the growing use of generative AI in insurance will make the industry impersonal. However, generative AI can actually enhance the human-centric aspect of insurance. By automating basic inquiries and providing valuable insights, generative AI frees up time to focus on complex claims, speak with customers, and deliver personalised solutions. The same is true for other departments, such as underwriting or marketing. Combing human expertise and generative AI capabilities can create a more customer-centric and efficient insurance business.

5. It’s difficult to understand how generative AI works

AI, including generative AI, is sometimes viewed as a black box. It’s difficult to interpret and understand the decision-making process  – right? Actually, no. Advancements in explainable AI techniques have made major strides in addressing this concern. Explainable AI models provide insights into how the AI arrived at a particular decision or recommendation, enabling a clear understanding of how each output has been reached, and increasing transparency. With explainable generative AI, insurers can confidently use its capabilities while maintaining accountability and compliance.

6. Generative AI can lead to biased decision-making

Generative AI models have a reputation for perpetuating biases present in the data they are trained on, leading to biased decision-making. This issue can be avoided through careful data selection, preprocessing, and ongoing monitoring of the AI models – which we do carefully at By ensuring diverse and representative datasets and implementing bias detection and mitigation techniques, insurers can minimise the risk of biased outcomes. In fact, outcomes maybe even less biased than those produced entirely by humans. 

7. Generative AI isn’t accurate

Likewise, some say that automation, including generative AI, may not be as accurate as humans. It really depends on how and where it’s being used. Studies have consistently shown that automation can outperform humans in specific tasks, especially when it comes to processing large amounts of data and identifying complex patterns. By using generative AI models, insurers can analyse vast datasets, spot correlations, and generate valuable insights that may otherwise go unnoticed. This enables more precise risk assessment, personalised pricing, and efficient claims management, ultimately leading to improved accuracy and better outcomes for insurers and customers alike.

8. Bigger AI models are always better

When it comes to generative AI models, bigger is not always better. Data trains generative AI, but it’s not just about quantity. Quality and relevance are equally, if not more important. Rather than relying on vast amounts of data, the key is to ensure that the data used to train generative AI models is representative and accurately reflects the diversity of claims and customers. A smaller, well-curated dataset can produce more reliable and precise outcomes than a larger one.

9. Generative AI is expensive

A prevailing misconception is that implementing AI, including generative AI, in insurance businesses is expensive if it’s any good. While there may be an initial investment required, the long-term benefits and cost savings tend to outweigh initial expenses. Generative AI can be used to reduce manual efforts, increase operational speed, and reduce errors. When used in underwriting, for example, its enhanced risk assessment and decision-making capabilities can result in more accurate pricing and personalised products, ultimately reducing claim costs and improving profitability.

Generative AI at

Generative AI has the potential to transform the insurance industry for the better. It can be used to enhance operations, improve decision-making, and deliver more tailored and efficient services to their customers. 

Here at, we use generative AI in a number of ways that help us automate claims processing fast. From the creation of synthetic data to policy checking, generative AI has helped us create a product that insurers can integrate into their current systems at speed.

To learn more about how we help insurers process claims faster and better serve their customers, download the report, or book a call with one of the team.

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