Reimagining How Lawyers Communicate Contract Terms & Insurance Policies to the Public
By Margaret Hagan, first published on Legal Design & Innovation
How can we help people to understand whether a contract is a good fit for them? Like when they are faced with a possible insurance policy, how can we help them make wise, informed choices about whether this policy is a good fit for them?
Our groups at Stanford Legal Design Lab & Codex taught our 2nd class on this topic this Winter 2023. This year’s class was called Becoming the Tech Creator and Regulator: Redefining Insurance Solutions, and taught by myself, Roland Vogl, Megan Ma, and Jay Mandal. Last year’s class focused on people’s decision-making process about how to buy health insurance. That 2022 class produced design research on people’s journeys through insurance shopping & understanding.
A legal innovation class on improving consumer understanding of insurance
This year’s 2023 class was more focused on solutions to the consumer empowerment & disclosure design problems. Our class, Becoming the Tech Creator and Regulator was structured to help students understand the challenge of people trying to understand insurance policies, then to identify promising new solutions that could improve people’s understanding and strategic capability vis-a-vis insurance.
The class partnered with the National Association of Insurance Commissioners (NAIC), which is a US-based national organization that defines standards & offers support to the 50 state-based insurance regulators throughout the country. Like with other policy lab classes, the class client NAIC brought a wicked problem that they are wrestling with: how to improve consumers’ understanding and strategic use of insurance policies.
The guiding challenge of our class:
What hypothetical new interventions could empower consumers while they decide whether & how to purchase insurance?
The policy lab class was a 9-week, two-phase journey through understanding this broad challenge, identifying solutions, and then refining them to be more impactful and innovative.
The 2-Phase Approach to Legal Innovation: The Creator and Regulator POVs
But the class was focused on more than just launching new innovations. The first half of the class used design thinking & agile startup methodology to help student teams identify users’ problems and brainstorm new solutions. But the second half of the class led them through lessons & exercises to think critically about their proposed innovation.
In this part of the quarter, they were to think less like ‘creators’ and more like ‘regulators’. The student teams created risk assessments for their proposals. They learned about the STS (Science and Technology Studies) framework, to think critically about how new technologies create new ways of living, working, and unexpected changes in society.
And in this second phase, they thought about the infrastructure and government’s role in the solutions that they were proposing. Rather than imagining themselves as a group of independent entrepreneurs building a new stand-alone intervention, what if they thought of themselves as a national or statewide group that regulated the industry? What could they do in this role to improve consumers’ strategic decision-making?
Our teaching team intentionally had these 2 phases of the class so that students could appreciate both points of view — that of the eager, innovative creator that is excited to launch their new project, and that of the cautious, but still innovative regulator that wants to see new solutions for the public but also is wary of risks, harms, and negative effects from new initiatives.
This new policy lab class focused on a similar scenario:
- a person, without any special expertise in law, insurance, or other policies
- faced with a complex set of legalese, in the shape of a contract or a policy,
- who then has to choose “what’s the smart thing to do for me?” — about whether they should agree to this contract, pay for this policy,
In this case, we were focused on the consumer’s relationship with possible insurance policies.
When they are buying a trip to travel abroad, or buying a new house, or looking to manage upcoming risks or costs, they might be looking through many different insurance offerings. Which one of them should choose?
We mapped out how this works for most people at this shopping stage: trying to make sense of the policies’ terms & offerings, and then also trying to think down the road: what might happen to me in the future, that I might need to use insurance to protect against?
This is what makes this situation so challenging — and why disclosure innovation is so needed. It’s hard for people to imagine these future scenarios, and then also imagine how these insurance policies might play out to protect them.
The person has to juggle many future predictions, scenarios, and decisions. At the same time as they have to understand what’s inside different insurance policy products, they also have to predict what scenarios are likely to happen to them in the future, and then try to apply these policies to these future scenarios.
As I am buying this trip to travel abroad, what might happen? Might I lose a suitcase? Or miss a flight? Or my kid might break their leg? Which of these possible insurance policies will cover these? And how hard will it to get money if these things happen?
Or, as I am purchasing this house, what might happen? Could my house flood? Will a wildfire harm it? Could there be an earthquake? Or what happens if there’s a big accident? Would this insurance product give me money to help me repair it? What will the journey be like to get money back to help me out?
These are the user journeys we uncovered in our past design research. In this class, we wanted to identify new innovations that could improve a person’s ability to make smart, strategic decisions while trying to make sense of different policies and buy the right one for the scenarios they were at risk of experiencing.
Past Disclosure Designs, with a focus on policy terms
In our many past classes and workshops on disclosures, most of the designs focused on the terms of the policies and contracts. How can we make these complex terms clear, approachable, and engaging?
The underlying theory of these disclosure designs was that clearer policies could lead to higher understanding by consumers. These engaging, user-friendly presentations of legal terms would make it more likely for people actually to read & learn these terms. Then they’d be able to make sense of them and apply them to their future scenarios & risks.
This theory of better disclosure design then leads to proposed interventions like:
- icons, tables of content, and executive summaries of the terms using good visual design
- calculators and wizards to compare policies’ terms
- conversational tools like pre-programmed bots that converse with a user about the terms, gradually educating about them in back-and-forth messages
- navigators, in the form of customer service reps or generative AI products, to walk people through these policies and respond to their specific questions about them.
The focus of these types of interventions is all on the policy or contract. The designs all try to make the terms of the policy or contract as approachable, understandable, and engaging as possible.
Most of our past classes and workshops have pointed to these kinds of solutions as promising.
The Shift to Scenarios, Data, & Risk Communication
The students in this Winter Quarter class, however, ended up with a different focus. In part because of the 2-phase structure of the class, the students went beyond the typical kinds of solutions to think more deeply about how to truly improve consumers’ ability to make empowered, strategic decisions about insurance.
They shifted the disclosure focus from the policy terms to the scenarios.
A better disclosure design (this new theory goes) will focus on what people actually care about — these bad future events they might experience, and if/how the insurance policy will play out if these bad future scenarios happen.
The teams benefitted from greater knowledge & exploration of data’s role in this process. They brainstormed ways (both from the point of view of an independent entrepreneur and a well-connected regulator) that data sets & models could help improve the situation.
This led them to focus on the question: what if consumers had access to data that insurance companies and service providers had? These groups are able to make these complex predictions about what future scenarios people might have.
The teams also uncovered a key insight: most consumers do not care about the insurance policy or any other consumer contract. They care about the money, time, and customer service that they will experience down the road, if they end up in a bad scenario. So why focus the disclosure design on the contract or policy terms?
Even if they are visually appealing, and presented in staged and thoughtful ways, consumers often do not find value in making sense of these abstract terms. It’s hard to over come this resistance to reading contract terms — even the best communication design intervention will struggle to get people to ‘eat the broccoli’ and spend time reading terms and policies.
But consumers do value knowing more practical, less abstract disclosure information. They want to know:
- What are the bad future scenarios that might happen, in this area that I’m possibly buying insurance?
- How likely is it that any of these scenarios will happen to a person like me, especially with the purchase or activities I’m about to engage in?
- Will this insurance policy give me money if these specific bad things happen in the future?
- How burdensome will it be for me to get the money from the insurance company? How long will it take them to issue a check? How much proof and rigamarole will I have to go through?
Disclosure designs that focus on these practical matters will be much more likely to engage and educate the consumers. With more data being gathered about all four of these topics, it should be easier to imagine a future when people can know these things before they decide to buy an insurance policy.
The students, with this new focus in their disclosure design, proposed other kinds of interventions:
- Data-Driven Scneario Predictor. This intervention would use large data pools & models to tell a person what kinds of bad scenarios might happen for the activity they’re about to engage in (like, buying a house in a certain neighborhood, or booking a flight with a particular airline to a certain destination). What can this current consumer learn from past consumers’ outcomes? The intervention would help a person see predictions of possible future scenarios and put clear likelihoods (in the form of percentages, scores, or anecdotes) of these possible scenarios
- Scenario-Spinners, which would be tools or interfaces to help a person understand how different insurance policies would play out if particular scenarios occur. This intervention would explain which possible insurance policies would cover which scenarios, and provide some compensation if these scenarios would happen. It would help make the abstract terms of the policies concrete, by telling people what they want to know — if this bad thing happens in the future, what would this insurance policy do for me?
- Data-based Reviews of Claims-making: This intervention would show a consumer, as they are shopping among insurance products, how burdensome and costly it is to get the different insurance companies to approve & pay out on consumers’ claims. Rather than vague consumer reviews (that are often provided by people who haven’t actually had to use the insurance product, and thus don’t have experience with the claim-making journey), this intervention would provide quantitative data about the burdens or ease of making claims. How long does it take to get an average claim approved? How burdensome are the documentation or meeting requirements to get through the claims process? How low or high is the customer service rated, among those who have had to make claims?
The students drafted versions of these interventions for homeowners insurance (to understand climate risk like flooding, for a home they are considering buying) and for travel insurance (to understand the risks of certain trips, and buy the right kind of insurance product for it). They presented them to partners at NAIC for feedback. This raised some interesting questions:
Question 1: How do we communicate data-driven risk to consumers?
If this new era of disclosure design happens, and data is unleashed & modeled to provide these predictions of future risks and an understanding of past claims-making, we must invest more in risk communication design.
The Winton Centre for Risk & Evidence Communication at Cambridge has done multiple R&D projects on human-centered ways to communicate medical and genetic risks to people. This project is now closing out, but their insights and examples could be a model to legal & insurance professionals who are trying to effectively communicate legal and policy terms to people by focusing on possible future scenarios & ways to mitigate bad outcomes.
There is much to do in this field of legal & insurance risk communication design. Now that we have locked into this insight that we should shift from a focus on designing approachable versions of policy terms, we need to develop new design practices, insights, and models for risk communication.
What are the most effective, clear, and empowering ways to tell people about the likelihood of future scenarios — especially negative ones like flooding, diseases, accidents, etc? How do we build risk literacy?
Question 2: How do we incentivize responsible data-sharing and modeling, that can empower consumers’ decision-making?
One of the big discussion points at our final session was about the feasibility of data-driven solutions for the consumer. If this new generation of innovation depends on data about scenarios & claims:
- Data’s Existence: Where will the data come from? Are there groups who have this information in structured data form?
- Sharing: Will those who hold this data be willing to share it? Are there privacy and protection concerns that should limit this sharing, either legally or ethically? If it is possible to share the data, will there be incentives or requirements by regulators or industry groups to ensure that sharing happens?
- Governance & Ownership: Who will be receiving data from multiple different sources? Should this be a government entity, a nonprofit, an industry group, an advocacy organization, or an academic institution? And how can it be set up so that they can effectively use the data to create models that benefit consumers, with a sustainable support model, but not with distorted priorities that might harm consumers?
These are all open questions, that future work needs to explore.
Question 3: Risk Data Shifting People’s Behavior & Insurance Business Models
If these interventions are successful, and groups are able to build these risk communications about possible future scenarios & how insurance products will play out, will this have unexpected consequences for people & for insurance companies?
These questions were raised at our final feedback session, and need more exploration.
One possible future shift is that people might become more risk-averse, as they know more about bad things that might happen to them. More quantitative data about bad futures might stop them from taking actions, making purchases, or taking chances.
It might particularly inhibit consumer action & spending on things that are shown to have more likelihood of bad future outcomes. In the travel space, that might mean that some geographic regions are shown as having more bad outcomes happening, and thus less people choose to travel there. Or some airlines or cruise lines have more bad outcomes, and fewer people choose to travel on them.
In the home-buying space, this might mean that people are less likely to purchase homes in neighborhoods or whole regions that are shown to have more risky future scenarios, especially with climate change. Quantitative data or risk scores might then consumers away from these areas.
This increased data given to consumers might also disrupt insurance business models. As consumers get more information about exactly what insurance policy they might need to cover scenarios, they might be more selective about when and how to buy insurance. Will this then affect the insurance companies’ solvency?
Our policy lab team uncovered a promising new direction for consumer empowerment & improved disclosure design in the insurance space. We’re excited to see more research, development, and national conversations about whether and how these data-driven, risk-scenario interventions might evolve.
We thank our class clients & visitors for their thoughtful input, and our students for their dedicated & insightful work.