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MPL Liability Insurance Sector Report: 2023 Financial Results Analysis and 2024 Financial Outlook

Wednesday, May 22, 2024, 2:00 p.m. ET
Hear analysis and commentary on 2023 industry results and learn what to watch for in the sector in 2024, including an analysis of the key industry financial drivers.

MPL Association’s National Advocacy Initiative in Full Swing

The MPL Association is shifting its focus toward state policy makers with a new program—the National Advocacy Initiative. This comes at an important time for the MPL community as the deteriorating policy environment in the states is resulting in increasing attacks on established reforms.



Leverage Generative AI for MPL Efficiencies

By Mat Winter and Martin Kowal

Think of a time when you’ve gone home after an exhausting day only to remember that you don’t have anything for dinner. You search the pantry and the fridge only to find a random mix of protein, some milk, and a box of macaroni and cheese. Then it hits you. You crack open your laptop, log in to ChatGPT, the generative AI engine, relate the random ingredients you have, and ask it to give you easy dishes to cook in under 20 minutes. In a few seconds, ChatGPT responds with 10 easy meals.

Now imagine using this virtual assistant to compose documents, generate complex Excel formulas effortlessly, and even create corporate interview summaries in the blink of an eye. Generative AI like ChatGPT brings this extraordinary capability to life. In fact, when you read to the end of this article, you’ll find out how much of it was generated by ChatGPT.

As industries worldwide embrace this game-changing technology, the potential applications in insurance and InsurTech emerge, promising to expedite claims processing, optimize risk assessment, and enhance customer experiences. However, with great power comes great responsibility, prompting a closer examination of the impact and implications of ChatGPT in this evolving landscape.

The ABCs of Generative AI

Generative Artificial Intelligence is an advanced language model built upon deep learning techniques, capable of generating human-like text responses. Essentially, generative AI models are trained on millions of examples to recognize and predict sequences of words. Models trained on examples are known as neural networks.

There are dozens of generative AI models. ChatGPT, developed by OpenAI, is one of the most popular and highly utilized. Despite the impressive capabilities of generative AI, these tools do not possess intelligence. Instead, these models are machine-learning-based algorithms trained on more than 40 terabytes of text data, which is equivalent to a quarter of the entire US Library of Congress.



Based on those inputs, ChatGPT and other generative AI tools are able to respond to questions—or prompts—with content that can be indistinguishable from human-generated content. However, there are situations in which generative AI is inaccurate or inappropriate. The AI in generative AI and ChatGPT is similar to the AI in your cars, phones, computers, and “smart” speakers in your home. All of these things use AI, but ChatGPT stands out because unlike cars that can change your speed or phones that can tell you if a call is spam or not, ChatGPT gives you words as its output.

Generative AI Capabilities

Generative AI, exemplified by ChatGPT, offers benefits in terms of efficiency, idea generation, and innovation. Some of the notable benefits include expediting tasks or inspiring new ideas. ChatGPT can expedite various tasks such as writing emails; composing code, including Excel formulas; and summarizing interviews.

Without putting any proprietary information or sensitive data into ChatGPT, in the last month our company saved days of work by using ChatGPT for small snippets of code and complex Excel rules. By automating these processes, we saved valuable time, allowing us to focus on more complex and strategic aspects of our work.

ChatGPT can also serve as a creative collaborator, inspiring new ideas and providing fresh perspectives. It can assist in brainstorming sessions, offer alternative solutions, and expand the creative horizon of human users. More examples of how generative AI can help MPL insurers, healthcare organizations, and healthcare providers include:

  • Efficiency: One of the main advantages of AI systems is their ability to handle tasks efficiently and at scale. By not using AI, you might miss out on the opportunity to automate various tasks, leading to increased manual work and potentially slower response times.
  • Around-the-Clock Availability: Chatbots like ChatGPT-3, which is the free public version, and ChatGPT-4, which is the subscription version, can provide round-the-clock support, whereas human support staff may be unavailable during certain hours—especially nighttime hours. If you choose not to implement an AI chatbot, you may miss out on the opportunity to offer 24/7 customer service. Other benefits related to this capability include:
    • Handling Capacity: Unlike humans, AI chatbots can simultaneously handle many inquiries. No matter how many users are contacting it, every single customer will be attended to.
    • Instant Responses: Chatbots can provide instant responses to users, which is crucial in this digital age where people seek immediate solutions and information.
    • Multilingual Support: Chatbots can be programmed to understand and communicate in multiple languages, thereby improving customer experience for global users.
  • Training and Development: Generative AI can be used in the creation of training materials such as “text-to-video” or interactive training programs, helping to train employees more effectively.
  • Sales and Marketing: Generative AI can be used to personalize marketing campaigns based on customer data or communicate with potential customers in a natural, engaging way. It can also create brochures, articles, press releases, and general advertising ideas.
  • Content Creation: Generative AI can create content for websites, blogs, social media posts, and more, which can be edited and refined, saving time and resources.
  • Data Analysis: AI algorithms can analyze large amounts of data to provide insights, detect patterns, and make predictions, helping businesses make informed decisions.

Generative AI Challenges

While generative AI holds immense potential, it also presents certain challenges and concerns. In fact, OpenAI acknowledges this when you login to use ChatGPT:

    Don’t share sensitive info
    Chat history may be reviewed or used to improve our services. Learn more about your choices in our Help Center.

    Check your facts
    While we have safeguards, ChatGPT may give you inaccurate information. It’s not intended to give advice.


Generative AI presents other challenges. For example, generative AI content cannot be copywritten and may also plagiarize other sources, which is why it is better not to exclusively rely on it for content creation. Generative AI systems may also defame and libel individuals. In general, there is also the potential for data privacy issues, bias, and harmful outputs. Generative AI may make errors—known as hallucinations—and may also be used to create photos, videos, and voice recordings uncannily like an individual, but which are actually fake, known as deepfakes.

The creators of generative AI continue to work on refining these machine learning, large language models. There are also differences between the various models and their capabilities.

MPL Industry Use Cases

In the realm of insurance and InsurTech, generative AI has the potential to streamline various processes, enhance customer experiences, and facilitate decision-making. For instance, ChatGPT can assist in automating claim processing, underwriting, and risk assessment, leading to faster and more efficient operations. It can also aid in personalized customer interactions, offering tailored product recommendations and providing immediate support through virtual agents.  

When it comes to generative AI, it is crucial to strike a balance between harnessing its benefits and mitigating its risks. Extreme views advocating for unrestricted usage or complete bans fail to address the complexity of the technology. Instead, a pragmatic approach is required—one that encourages responsible use, thorough validation, and ongoing monitoring. By leveraging generative AI while embracing human oversight, we can unlock its potential while ensuring ethical and responsible deployment.

So how much of this article was written by a human, and is the information valid? After we fed ChatGPT some quick notes based on our research and personal experience, ChatGPT initially wrote around 80% of this article. We made sure to read over and validate the information it presented and made a few edits; the MPL Association made more. Again, with human oversight, allowing ChatGPT to organize thoughts and research—without using any sensitive data—can save time as well as money.

Mat Winter is a senior business analyst at Sunlight Simplify.

Martin Kowal is a senior business analyst at Sunlight Simplify.
“As industries worldwide embrace this game-changing technology, the potential applications in insurance and InsurTech emerge, promising to expedite claims processing, optimize risk assessment, and enhance customer experiences."