There is an interesting overlap in insurance and IoT: The big insurance companies like MunichRE are big in the business of insuring other companies against downtimes of their (industrial) machinery. If you equip machines with sensors, there is the chance of predicting downtimes (and unplanned maintenance) and preventing them. So the insurance can offer better pricing.
Overall, a lot of stuff in the insurance business depends on having the right data available. If you manage to collect the right(!) data using sensors, you can get an competitive edge.
Of course, a lot depends if you can crack the data analytics problems around predicting and preventing downtimes.
A lot of the companies in the space are collecting the wrong, or useless kinds of data, and overcharging massively for it.
Oftentimes IoT data collection can be a "solution looking for a problem" situation. But that's what you get when you start running out of good ideas, with billions sloshing around looking for ROI.
Just as many cars now have dongles sticking out of their OBD ports that feed info to auto insurance companies, one day you'll have a dongle on the inside of your mouth telling the health insurance companies when you last smoked, what you've eaten, which drugs you've taken when, etc. if you're not sufficiently wealthy enough. This is the end result of the IoT if steps aren't taken to limit corporate/government surveillance and guarantee personal freedom. Thankfully we are far from that now in the West, but the way things are developing in China is worrying...
At some point, what are you insuring against, if you know which machines will fail? This reminds me of the pre-existing conditions debate in healthcare - if you only insure healthy machines, then what is the point of buying insurance? Sure, catastrophy insurance is good, but I wonder if better data may reduce the size of the insurance market. Not necessarily bad for those being insured, since they could do preventative maintenance, but I wonder if the insurers are at all concerned.
The insurance company wants to pay out less money, so they offer a reduction in premium cost for behaviors that they think are worth that differential.
Suppose you have a thousand doohickey machines that cost 10,000 each to replace in an emergency, of which 50% is the doohickey cost and 80% of the rest is the emergency labor cost ; a ten year lifespan, and an observed failure rate of 1% per year.
In a normal year, you need to replace 100 doohickeys at a cost of a million. Over ten years, you replace 1000 doohickeys at a cost of ten million. Your insurance company charges you 1.02 million a year whether they have to replace 900 or 1100 in that particular year. It costs you a little more on average, but it keeps you from experiencing a catastrophe.
Now the insurance company gathers data from your doohickeys that predicts with 90% reliability that a doohickey will fail within a month. If they can pay the 1000 non-emergency cost of the labor (plus the 5,000 part cost), then they go from a 10,000 outlay to a 6000 outlay. 4000 savings x 90% x 100 doohickeys needing replacement is 36000.
So the insurance company offers you a reduction from 1.02 million per year to 985,000 per year if you install the realtime doohickey monitoring system. That's a great savings for you, a good savings for the insurance company, and everybody is happy...
unless it turns out that the realtime doohickey monitors have lousy security and leak valuable personal information to anybody who guesses the password (which is password321).
See also: why hasn't any car manufacturer purchased an auto insurer and use on-car sensor suites to generate fine-grained behavioral risk profiles?
To some this may sound like a dystopian invasion of privacy, and I probably would not opt-in to such a system. But the alternative for young men is massively subsidizing other young men's stupid choices... which IMO is even more dystopian.
From everything I've heard from people close to the company they've been struggling really hard to close big contracts (and spending a lot of time/money/effort on building prototypes for that), and they've been hemorrhaging employees the last year.
I can confirm this - I got similar information from several employees. The market is extremely hard and doesn't scale well. They did a great job solely by surviving and I'm really happy, that their hard work is rewarded now.
Pretty cool to see company I shared an accelerator and an office with being sold for this generous sum. I just don’t see how a Berlin IOT startup and its culture is going to thrive under a Munich insurance company. Very curious how this pans out.
Are the cultures very different between Munich and Berlin? Your comment implies this and would be interested in hearing about this for those of us that have only infrequently visited other cities in Germany such as Frankfurt or Düsseldorf.
Munich Re is in the (re)-reinsurance business, but they apparently own HSB which appears to be telecom-related so it could make sense to buy an IoT middleware startup. Can anyone explain the relationship between these Munich Re and HSB? They seem to be two different, non-complementary lines of business.
From my understanding, HSB is the industry arm of Munich Re. For insuring industrial facilities, expertise in auditing those is needed, and they also sell that expertise. IoT fits in there as a way of collecting more data, improving the quality of their risk assessment, selling monitoring equipment also means being able to avoid insurance cases, monitoring of SLAs that are insured, ...
I work at Neos, where we’re using IoT in the context of home insurance. Almost all our investors are insurance companies (including Munich Re who underwrite our policies).
Insurance is all over IoT, and indeed anything else with the chance of reducing risk - it’s what they do.
Wow, good to know. I know.Progressive insurance used to have a.dongle to monitor driver behavior for a discount. It predated.the term Not and used.the 2g network.
Overall, a lot of stuff in the insurance business depends on having the right data available. If you manage to collect the right(!) data using sensors, you can get an competitive edge.
Of course, a lot depends if you can crack the data analytics problems around predicting and preventing downtimes.
Disclaimer: Worked for Relayr