In the first two parts of this series on the future of insurance, we looked at how data, omnichannel access and intelligent tools can be used to collect, validate, supplement and prepare information for insurers. In this post, we will shift our focus on drawing oneself and how collaboration between people and intelligent tools can take it to new heights.
Is tomorrow's insurer a human or a robot?
The ghost of people losing their jobs to robots weaves over all discussions about applying intelligent technology. But the choice that carriers are facing right now is not a binary decision about whether man or machine should guarantee the risk. Rather, it is a question of how best to use machines to help human insurers more consistently make profitable insurance guarantee decisions. , and it will continue to expand to cover homogeneous risks. But human intervention will still be required for more complex cases and coverages. The future of advanced warranties lies in finding out how to best mix people's intuition and expertise with the machine's machining capabilities.
One of the most valuable skills of an expert insurance is their ability to assess the risk against a comparison group of similar risks from the insurer's experience to determine the relative risk of a policyholder. Pairing a human insurer with intelligent technology improves this process by using comparative analysis to measure a single account against comparable companies in the operator's portfolio to create a more comprehensive picture of the risk in the account.
Smart machines mean that an insurer does not & # 39; t need to rely solely on their own experience and judgment when examining the details of a submission. When the insurer examines information about a submission such as salaries, assets, finances, losses, population or driving record, they do not just have to rely on their own expertise. Instead, an analytical dashboard can mark where this specific account is better or worse than its comparison group. This information helps the insurer determine the total risk to the account and how aggressively they should price it. More importantly, it helps prevent them from believing that an account is better or worse than it actually is by providing an empirical view to facilitate the consistency of their assessment. to be more productive and process a large number of assets without missing any important details.
For example, on large real estate accounts and car accounts, it is not uncommon for an insurer to need to evaluate hundreds of properties or vehicles. Analysis and machine learning techniques can be used to identify features or vehicles that require special attention due to defined hazards or remote information. With these tools, an insurer can quickly identify elements from the long list that need human attention. This means that the insurer misses exposures much less. For insurers for employees and benefits, similar techniques can be used to assess large schemes of benefits and classes.
A new era of insurance guarantee
Improvements such as these represent a new chapter in the history of insurance history. The original focus of insurance was to capture the information needed for the policy or quote in our rating and quote systems. Then we had the power to expand workflows to help us manage and move work along the insurance process. Today, we must enable the insurer with superimposed intelligently prepared data and insights into the workflow to enable them to make more consistent, high-quality decisions that lead to better insurance results. .