Have you heard the buzz around ChatGPT?
Created by OpenAI, ChatGPT is an AI-powered chatbot that responds to questions and engages in conversation using opensource AI. RGA decided to put ChatGPT to the test by posing three questions related to underwriting and the future of life insurance. The answers from ChatGPT that follow are entirely AI-generated. Following those responses is a brief FAQ that further explores this emerging topic.
Three Questions for ChatGPT
Note: Responses below were generated entirely by ChatGPT.
What is the future for digital distribution in life insurance?
Digital distribution in life insurance refers to the use of digital channels and technologies, such as websites, mobile apps, and social media, to sell life insurance policies to customers. The future of digital distribution in life insurance is likely to see continued growth and expansion as more and more consumers become comfortable with purchasing insurance online.
There are a number of factors driving the growth of digital distribution in the life insurance industry. One key factor is the increasing use of technology and the internet by consumers. More and more people are using the internet to research and purchase products and services, including life insurance, and this trend is likely to continue in the future.
Another factor driving the growth of digital distribution in life insurance is the increasing use of data and analytics. Insurers are using data and analytics to better understand the needs and preferences of their customers, and to develop more personalized and targeted products and services. This is likely to lead to the development of more innovative and customized life insurance products that can be sold through digital channels.
Overall, it is likely that the future of digital distribution in life insurance will be characterized by continued growth and innovation, as insurers seek to meet the changing needs and preferences of their customers.
How will COVID-19 affect U.S. long-term mortality?
It is difficult to predict the long-term effects of COVID-19 on mortality in the United States. The pandemic has already had a significant impact on mortality, with over 1.1 million COVID-attributed deaths reported in the country. However, it is possible that the mortality rate from COVID-19 could decline over time as more people become immune to the virus and as effective vaccines are developed and distributed.
There are also other factors that could affect the long-term mortality rate from COVID-19. For example, the effectiveness of public health measures, such as mask-wearing and social distancing, in controlling the spread of the virus could have a significant impact on the number of deaths. In addition, the availability of effective treatments for COVID-19 could also play a role in reducing the mortality rate from the virus.
It is important to continue monitoring the situation and taking steps to control the spread of the virus to minimize the impact on mortality.
Jerry's adoptive parents both died in their mid-40s due to hemophilia. How could this affect Jerry's long-term health prospects?
Hemophilia is an inherited disorder that affects the body's ability to control blood clotting. People with hemophilia often bleed easily and have prolonged bleeding after an injury or surgery. The severity of hemophilia can vary widely, with some people experiencing only mild symptoms and others experiencing severe and life-threatening bleeding episodes.
If Jerry's adoptive parents both had hemophilia, it is likely that Jerry inherited the condition from them. This means that Jerry may also be at risk for bleeding episodes and other complications of hemophilia. It is important for Jerry to be aware of his hemophilia and take steps to manage the condition to reduce the risk of complications. This may include taking medication to help control bleeding, avoiding activities that may cause injury, and seeking medical attention for bleeding episodes as needed.
It is also possible that Jerry may be a carrier of hemophilia, meaning that he has inherited the genetic mutation that causes the condition but does not have the condition himself. In this case, Jerry may not experience any symptoms of hemophilia and may not need to take any special precautions to manage the condition. However, it is still important for Jerry to be aware of his family medical history and the potential risk of hemophilia, as it could affect any children he has in the future.
Frequently Asked Questions
Note: All responses below were generated the old-fashioned way – by humans.
What is ChatGPT?
ChatGPT is a computer program that can understand and generate text in a manner similar to humans. ChatGPT works like an assistant that users can ask to perform tasks or answer questions. For example: “Write a paragraph describing the differences between automatic and facultative underwriting.” It is based on machine learning technology, which allows it to understand and respond to natural language inputs. ChatGPT can be used for various tasks, such as answering frequently asked questions, generating text, and even creating conversations.
Why is ChatGPT getting so much buzz?
A significant amount of media buzz surrounds ChatGPT and other language models due to their potential to revolutionize the way we interact with computers and to automate a wide range of tasks. These models employ cutting-edge machine learning techniques, making them capable of understanding and generating natural language akin to human conversation. ChatGPT can be fine-tuned to specific tasks or industries, making it a versatile tool for many applications. Some use cases include customer service, content generation, and language translation, where it can save time and resources by quickly processing large amounts of text data.
What does this ChatGPT demo show us?
In our demonstration above, we chose three questions to illustrate both the strengths and weaknesses of the tool. The first two questions ask it to write about life insurance underwriting topics, and ChatGPT performs admirably. The third question is more problematic and demonstrates a notable weakness: ChatGPT fails to recognize that a child cannot inherit a genetic condition from an adoptive parent. Unless its source corpus contains enough examples about the inability of adoptive parents to pass on genetic traits to their adopted children, ChatGPT will fail to make this connection.
What are the main limitations of ChatGPT?
ChatGPT and other language models have several limitations. These models:
- Are trained on large amounts of text from the internet, which may contain biases and can result in the model reproducing biased language and stereotypes
- Lack common sense knowledge, which can lead to nonsensical or unrealistic responses
- May struggle with tasks that require knowledge of specific subjects or industries
- Are not designed to perform tasks that require reasoning and logical thinking, such as deduction and induction
- Lack empathy and cannot understand and respond emotionally to the context
ChatGPT only accesses written text at the time it was trained. It is not capable of utilizing tabular data, diagrams, or data sources created after its last training.
What is the potential relevance of ChatGPT to the insurance sector?
ChatGPT and other language models could be used to accomplish several insurance-related tasks, including:
- Providing automated customer service through chatbots, answering frequently asked questions, and delivering information about policies and claims
- Helping underwriters analyze large amounts of unstructured data, such as customer reviews, social media posts, and news articles, to identify potential risks and inform underwriting decisions
- Processing large amounts of text data, such as claims and policy applications, to identify patterns and anomalies that may indicate fraudulent activity
- Generating content such as policy summaries, coverage explanations, and customer communications
- Enabling multi-lingual customer service by translating customers' queries and responding in the customers’ preferred language
One such example is DigitalOwl's newly released V4.0 Digital Underwriting Abstract, which introduces the first medical generative language model summary, specifically tailored to the insurance space. Learn more about the exciting collaboration between RGA and DigitalOwl.