One of the areas that is going to see some major rearrangements in the life insurance process is underwriting. Algorithms can do just about everything these days, and carefully-set-up electronic systems can not only fast-track the sales process, but can collect data, measure risk, and predict the future.
One of the reasons people are uninterested in insurance is the lengthy, invasive application procedure. People like easy things, which is why - despite the noted extra expense and possibly inferior product - they like buying their insurance off the internet. The idea that we need a real human - after reading our fifty-five-thousand-page application from start to finish - to determine our risk manually seems outdated and laborious. For now, the cost of underwriting and personalised insurance is high.
But why can't this process be automated?
Well, it can. Financial advisers can provide expert and tailored advice, but realistically, this process too can be automated based on choice questions loaded into a machine. It doesn't mean it will necessarily be better, but that too is up for debate and very likely to change, especially if you take bad or lower-end advice into the equation.
In reality, technology and humans will work in harmony, with underwriter's jobs being faster and more accurate when determining risk.
Electronic health records and social media could all provide alternate data sources. Medical record collection takes time, with most of the information provided in print, needing to be sifted through. Instant access is not yet available, but think how much time that would save. Permission given, records accessed. Life insurers now also have health records from their health and fitness programs which can also be used, even being switched between insurers.
While it remains controversial, genetic testing can further identify risks (though the question remains: should we be allowed to deny cover to someone based on a genetic marker, not on actual disease manifestation?). Soon, we'll have a better basis for genetic risk analysis, though that requires more people to not only undertake genetic testing, but submit it to someone for data gathering and analysis. This is an immense area for fascinating human discoveries that has only just begun, but also a broad area of risk determination for underwriters.
Predictions and analysis
Modelling and use of statistics and data analysis can find relationships between things to predict the future that a person just can't do, which can be an alternative method of evaluating risk. Data mining and prediction is a rich area for future underwriter toolkits.
Tweaking policies for high-risk consumers
Not everyone is approved for insurance cover, but this doesn't have to be the case into the future, with monitoring techniques (like wearables) making monitoring of a health condition - and its insurable states - much easier. If a client has a well-managed health condition, with provable data that goes along with it, there should be no reason why their insurance can't be continued in certain circumstances. Low-risk customers, conversely, can ask for discounts.
Data collection and integration for sales
Getting a policy up and running as quickly and painlessly as possible is one goal of both insurers and advisers. Having digital set-ups that allow for this are already in place, but are evolving to further improve ease of use and speed.
Underwriting of the future
Soon enough, underwriting tools will include well-developed data mining and analysis tools. This is good news, since the more data we have at our fingertips, the better tools we develop to understand and use this data, the better risk can actually be measured and paid for by clients in a fair and reasonable way.