What Jim Carroll Saw Coming
Original archive: 2014-07-07 — Trend: The Future of Insurance, and the Actuarial Profession.
Carroll’s work on the future of insurance raised the question of how technology and changing customer behavior might affect the industry. This voyage examines a specific extension: policies informed by connected-device data, with difficult consequences for pricing and privacy. It is a thematic continuation, not a claim that every data-driven product was predicted in the source.
What has changed in the real world
Traditional insurance estimates risk using groups of people with broadly similar characteristics. Connected devices offer a different source of information: direct observations of what a person or asset does. A car might record miles driven and braking behaviour, creating the possibility of pricing that reacts to use. That seems more precise, but precision is not identical to fairness. A signal can be influenced by road conditions, income, neighbourhood and factors outside the customer’s control.
Carroll’s earlier insurance-related thinking examined how connectivity and information would change financial services. Telematics has since become an established term for data collected through vehicles or related devices. The NAIC’s 2026 explanation shows that the data can be remarkably granular. Insurers may offer opt-in discounts, yet different regulators and products impose different constraints. A future policy is therefore as much a governance problem as a sensor problem.
A fact that changes the scale of the question
The U.S. insurance commissioners’ association lists mileage, time of day, GPS location, acceleration, hard braking, cornering and air-bag events among signals that telematics may collect. The original source is National Association of Insurance Commissioners, September 2026. The distinction behind the number or capability matters just as much as the headline: More granular pricing can raise fairness and privacy questions; collection is not evidence of a fairer premium. The point of introducing the fact here is to show the actual scale of the machinery, knowledge, data or infrastructure involved in this particular voyage. It is not evidence that the imagined 2036 destination is inevitable.
2036: one possible destination
An imagined scene, not an account of an event that has happened.
Imagine reviewing a policy renewal in 2036. The customer can see which driving signals were used, what the insurer concluded and how to challenge an incorrect reading. A transparent offer rewards safer choices without silently exposing sensitive location history. Another customer declines monitoring and still has access to ordinary insurance. In this scene, the decisive innovation is control over the use of information, not the mere existence of sensors.
One image captures the possibility: A driver compares discounts against the privacy costs of continual measurement. The conditions behind that image deserve attention. A future built around this capability would also change work, trust, operating costs or personal choices for someone outside the technology industry. That is why this voyage is less about predicting the arrival date of a device and more about understanding the environment that would let it matter. Different communities and businesses could arrive at the same destination at different speeds, or choose a different route altogether.
The detour that could change everything
Data quality, consent and discrimination deserve scrutiny. Driving at night may reflect a job schedule rather than recklessness. A loss of connectivity may corrupt the measurement record. Consumers may not understand how an algorithm weighs inputs, and a company may be reluctant to expose its pricing logic. Regulators must decide which explanations and appeal rights are appropriate.
There is no requirement that technical progress and adoption advance together. People may welcome one application and reject another. The most persuasive future is the one that survives comparison with cheaper, simpler or better-understood alternatives. This is also where the archive has value: an old idea can be supported in direction while the timetable, commercial model or surrounding institutions evolve in ways that could not have been obvious at the start. That history deserves to be told rather than edited into perfect hindsight.
The next chapter is still being written
Look for evidence that monitoring changes accident frequency and reduces premiums or claims fairly across different groups. Check whether customers can correct errors and understand the factors behind a price. If transparency and appeal mechanisms lag, more data may create more distrust.
The practical test is this: Distinguish telemetry-based premiums from continuous individualized surveillance The question for the people making choices today is not whether the imagined 2036 scene is visually convincing. It is whether emerging evidence alters a real decision: where to invest, what to build, which safeguards to demand, and which assumptions should be checked again next year. How far will real-time underwriting go before social and regulatory limits intervene? The answer is not locked in. That is what makes tomorrow a voyage worth taking.