Concern 346 · Working draft · editorial review in progress
Insurance underwriting AI
Insurance underwriting bias occurs when automated decisions about coverage or price unfairly disadvantage some applicants.
Understanding the concern
Underwriting is the process of deciding what risks an insurer will cover and at what price. AI can combine many pieces of information, including data bought from outside suppliers. Some variables may be inaccurate or only loosely related to the insured risk. Others may indirectly reflect characteristics that raise fairness concerns.
Insurance necessarily distinguishes among risks, so every price difference is not proof of discrimination. The question is whether a difference has a sound basis and fits the rules for that insurance product and place. The NAIC's model bulletin encourages oversight of these systems, but a model document is not automatically the law in every state.
An example
An applicant's premium rises because of an outside data record that the applicant has never seen and cannot easily correct.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
NAIC guidance identifies risks and governance expectations. Legal limits vary by jurisdiction and type of insurance.
National Association of Insurance Commissioners · Model guidance
NAIC Model Bulletin on Use of AI by Insurers ↗ (opens in a new tab)2023 model bulletin on governance and unfair discrimination; adoption and legal effect vary by state.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What evidence connects each factor to the insured risk, and how can customers correct bad data?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
Search for more writing
For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.