The insurance industry boasts a storied history dating back to the Code of Hammurabi (around 1750 BC), Roman maritime law and Lloyd’s coffeehouse on the River Thames (1688).
Notably, the longest-tenured property insurance company in the US (The Philadelphia Contributionship, 1752) was organized by Benjamin Franklin. From this founding father, we credit the expression:
“An ounce of prevention is worth a pound of cure.”
The limitations of indemnification
These days, digital technologies enable insurers to create brilliant customer-centric operating models, the likes of which Ben Franklin could only dream of. But the industry’s value proposition – built on the principle of indemnification – now threatens long-term growth opportunities as the short-term cost of doing business skyrockets.
Any insurance company on the planet faces bleak realities. Consider the inflationary pressures working against efforts to reduce costs. Customer expectations for real-time capabilities. And innovative product designs across all business lines designed to deliver a personalized, mind-blowing customer experience at a competitive price.
Insurers must continue to evolve their business models simply to survive. Technologies like artificial intelligence (AI) and generative AI (GenAI) can supercharge this evolution, but the limitations of indemnification will continue to slow momentum.
To speed evolution, insurers need to shift to a new value proposition – predict and prevent.
It’s time for insurance to change
Imagine insurance solutions for property & casualty or life insurance that actively identify risks a person or business can address to prevent a loss from occurring. Even five years ago, such ideas seemed like science fiction. But now, fiction has become reality. For example:
- Ubiquitous connectivity has created an estimated smartphone penetration of 69%.
- The global IoT insurance market is projected to reach $687 billion by 2032.
- A glaring new business imperative has risen from the threats of climate change, with 2023 setting a new record of 142 natural catastrophes.
Something has to change.
Challenge the status quo
As we’ve seen with other technological breakthroughs, success comes from boldly challenging the status quo.
Consider that a human would need to perform one sum per second for 31.7 billion years to perform the equivalent one-second calculations (teraFLOPs) of the world’s most powerful “exascale” computers.
Keeping in mind those profound implications, it seems senseless not to take advantage of this technology. That’s especially the case when we know signals from smartphones and IoT devices can be harnessed to proactively identify situations that cause or aggravate loss.
Now is the time to challenge the status quo of indemnification. Rethink the idea that a loss should happen first before a policy of insurance can respond.
Losses from natural disasters 
In its 2024 Climate and Catastrophe Insight Report, Aon reported that global natural disasters in 2023 resulted in above-average economic losses totaling $380 billion. Around the world, insurers covered $118 billion and set a record number of billion-dollar insured disasters. That year was also the deadliest since 2010 (with 95,000 fatalities) and the hottest year on record.
AI is not perfect – which should not keep us from using it
Consider the insurance industry’s early response to “the internet.” Long ago, many insurers banned its use. But today, it’s common for insurers to search online for information about risks in underwriting and claims processes.
We know that not everything on the internet is true. But it’s still useful in gathering information. The trick is determining what is reliable.
AI is built by people, and it will inherit our flaws. But similar to what happened with online searches, AI will permeate insurance processes over time.
The key to solving issues with misinformation? It’s all about the data.
Remember: The devil’s in the data
Insurers collect and store a veritable cornucopia of data. An individual policy could be rated on hundreds, if not thousands, of variables – details about the named insured or business owner, insured location, loss history, etc.
Small inaccuracies can (and do lead) to major premium differences and some risks being accepted that never should be written. Some are outright declined.
This reality exists because the data is collected by policy applications handled through conversations, sometimes online, sometimes via paper and (yes, still) sometimes even fax. Often, the inaccuracies only surface after a loss and after conducting a more thorough investigation of the risk. If the information was originally collected by an agent, it could trigger errors and omissions coverage.
Yes, manual review can find 95% of errors, but doing so wastes people’s time. So – often the information used to write a policy is never validated.
Increasingly, third parties – bureaus like Dun and Bradstreet – offer data and even data products. Public information that’s accessible online can provide deeper insights into individual risks.
The benefits? According to McKinsey, carriers can achieve double-digit increases in premiums and profitability by adopting “digitized underwriting.”
In these examples, by way of an agent, or third party, the same obstacle remains – the information was collected by an intermediary, rather than directly from the source (the customer). And since most data points are proxies for risk, at best, insurers are locked in a game of horseshoes and hand grenades: “Close enough is good enough to win.”
Overcome chaos and take advantage of new data
So how do we evolve to produce more accurate pricing models, underwriting decisions, and claims experiences for people and businesses?
We need to look to new forms of data (sometimes called alternative data) to develop a better understanding of risks. In doing so, we can begin preventing loss.
Generating more accurate AI models
Researchers have demonstrated that AI can be used to generate more accurate models and an annual economic benefit of $162 billion. Such capabilities were beyond us even just a few years ago.
Thanks to the rapid rate at which computing power increases – along with the widespread adoption of cloud computing – insurers can now obtain massive quantities of data from new sources (like aerial imagery, IoT, public information and more) to analyze via parallel processing, faster than ever before.
Data about actual behavior is superior to proxy data. Recently, some automakers came under scrutiny for allowing data to be shared with insurers. It’s no secret that today’s connected vehicles store data about your driving. That data – harsh braking and acceleration, nighttime driving, speeding, etc. – can be (and is) used to develop rates.
Data can also be used to step into the realm of accident and loss prevention. The scrutiny might be worth it. Consider that:
- 3,308 deaths in US auto accidents in 2022 were attributed to distracted driving.
- Some data points to distracted driving being a factor in 80% of accidents.
The rise of UBI
Carriers have seen increased participation in usage-based insurance (UBI) programs (doubled from 2016 to 2023), and a 26% participation for new customers with a 59-point higher premium (price) satisfaction according to J.D. Power.
Earlier this year I participated in a program focused on reducing distracted driving through Cambridge Mobile Telematics (CMT). I got to see firsthand how notifications reduced distracted driving – a notion I confirmed with the head of telematics at one US insurer running a similar program.
In CMT’s State of US Road Risk in 2024, data showed that the 4.5% decrease in distracted driving in 2023 prevented more than:
- 55,000 crashes.
- 31,000 injuries.
- 250 fatalities.
- $2.2 billion in economic damages.
Refine and use your untapped data – and inspire change
Remember the old saying: “Data is the new oil”? It’s a crude analogy (see what I did there), but still accurate – because you’ve got to extract oil out of the ground, refine it, and ultimately distribute it for consumption and use.
Data is no different. Telematics or UBI data provides an untapped source that insurers can (and should) use to evolve the industry’s value proposition.
State Farm has expanded its fire safety program for two million households through Ting and Whisker Labs. State and local governments are enabling smart cities technologies to ease traffic congestion and combat flood risk. And the Apple Watch has been credited with saving lives through the heart rate app.
These stories inspire change in the industry. All losses will not be prevented, but with the power of AI, we can prevent some losses. The downstream impacts of alleviating loss pressures, saving lives and improving economic value will lead the industry into a new era.
Read about the top five insurance problems – and why AI isn’t one of them
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