The Future of Health Insurance Underwriting: Chatting with AI

Ever wondered how a mere conversation with a computer can help an underwriter make a decision? To put it simply, imagine a UW simply typing his/her query about the details of a policy proposer and waiting for the system to respond about insurance eligibility. This super easy-sounding process has a lot going on behind the screens - and that is Artificial Intelligence.

All this while, an Underwriter used to scout for various details, do eye-ball checking, and browse through papers to make a decision on an insurance application. Then came the era of systems where various integrations helped the UW do 'less eye-ball' checking, as the system did it for him/her.

Now, an UW can simply ask the computer!

If a health insurance underwriter wants to use generative AI to supplement their underwriting process, there are several tools and platforms they can use like Amazon Sagemaker, TensorFlow, etc. where a pre-trained AI model helps find more details pertaining to a customer, based on a large set of data that aligns with the customer.

Platforms like ChatGPT can be used by underwriters to generate output by integrating with tools like Amazon SageMaker, PyTorch, and other machine learning frameworks. First, let's see how we can configure Sagemakers of the world to build AI models:

1. Use case identification: The underwriter first identifies a use case where generative AI can be used to supplement their underwriting process. For example, they may want to generate synthetic health data to predict an individual's risk of developing a particular disease.
2. Data preparation: The underwriter then prepares the input data for the machine learning model. This may involve aggregating and anonymizing real-world health data, as well as identifying the specific features and characteristics that will be used as input to the model.
3. Model selection and integration: The underwriter selects an appropriate generative AI model based on their use case and the available data. For example, they may choose to use a pre-trained GAN (Generative Adversarial Networks - framework that created synthetic data mimicking the real world and a distinguisher that finds out the difference) model to generate synthetic health data. They then integrate the model into their workflow, using tools like Amazon SageMaker, PyTorch, or other frameworks.
4. Output generation: Once the model is integrated, the underwriter can use it to generate output. They can input data into the model and generate synthetic data that mimics real-world health data. They can then use this synthetic data to supplement the information provided by the applicant and gain additional insights into an individual's health risks.
5. Evaluation and refinement: The underwriter evaluates the output generated by the model and refines the model as necessary. This may involve adjusting the model parameters or adding additional data to improve the accuracy of the output.

Now how can technologies like ChatGPT simplify this?

So, here's a step-by-step process that a health insurance underwriter can use to take decisions using ChatGPT and SageMaker:

1. The underwriter logs in to the ChatGPT platform and initiates a conversation by typing a message.
2. ChatGPT receives the message and processes it using natural language processing (NLP) techniques to identify the intent and extract relevant information.
3. ChatGPT prompts the underwriter to provide additional information required for the underwriting decision. This information may include the policyholder's age, gender, medical history, and other relevant factors.
4. Once the underwriter provides all the necessary information, ChatGPT prepares the input data in a suitable format and sends it to SageMaker.
5. In SageMaker, a pre-trained machine learning model is used to process the input data and generate an output.
6. The output generated by the model is sent back to ChatGPT in a suitable format.
7. ChatGPT processes the output and presents it to the underwriter in a human-readable form. This output may include a recommendation on whether the policy should be approved or declined, as well as additional information that the underwriter may need to consider.
8. The underwriter evaluates the output generated by ChatGPT and uses it to make an informed decision on whether to approve the policy or request additional information.

Overall, this process involves the use of advanced technologies like natural language processing, machine learning, and cloud computing to streamline the underwriting process and make more informed decisions. By leveraging these technologies, underwriters can reduce the time and resources required for underwriting and provide better services to policyholders.

Just like any other business, insurance will also be greatly impacted by technologies like ChatGPT, and all the more because these were difficult to comprehend. GPTs have made it easier to understand and process. Insurers, service providers, and vendors should buckle up!

Note: Most of the content of this article has been generated through ChatGPT by asking various questions. Prompt engineering can help make the content crisp and smooth!