Eric Herbelin analyzes data analytics and emerging technology trends in providing better risk coverage
Eric Herbelin analyzes data analytics and emerging technology trends in providing better risk coverage
Insurance is essential, and despite its intricacies, it holds practical utility in a world full of risks. This especially rings true as your assets and financial obligations continue to grow, and with it, risks that require careful consideration to mitigate. Let’s take a look at data and analytics advances as well as technological changes within the insurance industry and how you can use it to your advantage.
With a more comprehensive and holistic understanding of risk and data as a whole, you’ll make better decisions. You will get better coverage that serves your exact needs, which can later be further customized and tailored to your goals.
Taking a close look at the relationship between longevity assessment and life insurance
Insurance companies use and leverage data. Data is essential for providing meaningful coverage that protects beneficiaries. It’s also important for surviving in a marketplace riddled with expectations, caveats, risks, and regulations. Insurance companies go through great lengths to mitigate risk at every level. They need to ensure the premiums collected and the investment income they derive from these outweigh the payouts made, because they are essentially looking to breakeven and unless they are mutual companies or fraternal societies acting for the greater good of their members, they will be aiming to generate a profit for their shareholders.
Insurance companies work with data sets to apply risk mitigation strategies across the board. Historical information is essential — life expectancy, health risks, the comorbidities that come along with them and more. This is why health checkups are essential to provide life insurance products. Each health factor is yet another risk calculation taken on board, and some health factors can even eliminate eligibility for some products.
Having certain genetic or biological factors also play into the equation. Being prone to certain diseases or of a certain age will also put you at risk. All these elements determine the risk level, and it affects premiums accordingly.
The due diligence of insurance companies is primarily based on these risk factors and assessing how much they might need to pay. Of course, financial planning can still be complex even with all of this data, statistics and probability. Average life expectancy and actual mortality rate among people still vary greatly.
This is where artificial intelligence (AI) and machine learning come to the rescue. They can be of great service to mitigate that risk and provide even higher levels of preciseness in their overall assessments, bringing us up to 99% accuracy. Machine learning is being enhanced by other models, such as neural networks used in deep learning, although these techniques are more difficult to apply in practice in highly regulated sectors such as insurance due to their lack of “black box” effect and their difficulty to explain how they arrive at certain outcomes.
Companies like Prudential are already looking to dive in applying more traditional yet effective machine learning methods. Recently, they started using such systems to make life insurance underwriting more efficient. While other insurance companies still feel a bit hesitant because it’s hard to discern the logic behind these machines and discover if there’s any bias, they are pursuing it with caution.
Insurers are now including this when customers renew their insurance, and the system is able to auto-approve or flag for further investigation. The real issue may lie, however, in how accurate these models are. Since they are able to predict the life expectancy of people within a 1%-2% margin of error, will life insurance be needed at all? Or will it lead to different policies to account for the existing margin of error? It raises key questions that need to be answered.
Life insurance works on the notion of uncertainty, yet insurance companies aren’t even the primary data collectors as data continues to be more widely available. Technology companies are vastly superior in their data collection efforts and can eventually become the new insurance companies, having deeper pockets and much more advanced technical capabilities.
Embedded insurance adds more complexity to the overall situation
Embedded insurance is when insurance becomes a subset or ancillary product of another core product or service provided by a company in a sector other than insurance. E.g. embedding insurance with the sale of a Tesla car or the travel insurance plan sold by an airline.
Here is an example scenario: You’re ready to book your next holiday on the airline of your choice, and you notice a tick box asking if you want to purchase travel insurance. This is a worthy add-on cost that can provide you with the necessary coverage should something go wrong during your holiday.
Let’s take a look at the auto insurance industry as well. While typically auto insurance is only 10–15% of the total monthly cost of the car, it is a lot, considering the average monthly expenses are $1000. Yet, it’s starting to become embedded into car purchases as the next automatic go-to. You can immediately pick insurance after purchasing a car online with some manufacturers. This isn’t a new concept, but it is simply becoming popular again with embedded insurance.
Embedded insurance is also something that tech companies and airline companies are beginning to offer. This is because they have their own massive data sets that they are sharing with insurance companies anyway, so essentially, why not cut out the proverbial middleman? Apple Care is already managed by Apple technically while the risk is underwritten by AIG. Even IKEA launched home insurance with iptIQ, a subsidiary of Swiss Re, as its partner.
What about retiring? Do I stick with the annuity strategy or look at the markets?
When it comes to insuring yourself for retirement, it’s always important to understand how each of these would work. The burning question to answer is whether to consider the immediate payout of an annuity and use that as your income stream or take the capital straight to the financial markets and manage the investment yourself.
Annuities seemed to have lost the steam that made them once an excellent, guaranteed source of income as annuity rates are tied to the prime interest rate, which, up until recently, was near zero. But this has changed and now that they’ve gotten their breath of fresh air, they are starting to look like the better and much less riskier option.
If you’re married, it’s even more straightforward by having one spouse added as an insured person; the annuity amount goes to them if the primary spouse passes away. There are also annuity options out there that adjust for cost of living and inflation adjustments, which can definitely make all the difference, as we’ve recently seen.
Experience is key
I noticed one key element when it came to insurance, especially during my travels. While we typically end up buying insurance because of a timely need, we don’t really look at the overall experience and long-term implications.
One of the best experiences I’ve ever had was when an insurance company called me to tell me they were actually lowering my premiums, which may sound silly but when considering the value of the relationship over my lifetime as a customer, this may be highly beneficial as I am definitely more likely to be a more loyal customer.
Looking towards the future, I’m looking forward to discussing some of the data and analytical components that factor into insurance, but also within organizational structures.
On my official website, I primarily explore leadership, transformation and change, and assessing how data, analytics and disruptive technologies transform industries and how this is affecting you is a crucial part of those overarching themes.
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