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Underwriting is the central task of insurers, underpinning the principles on which the insurance business model rests. Underwriters assess applications for insurance and decide who will get cover and what the terms of that cover will be.
Historically, underwriting has been one of the most complex and nuanced aspects of insurance, and it's important to get it right. Deciding how to price policies to offer good value to customers without compromising on profitability will make or break an insurance business.
It's no surprise then, that underwriting is traditionally a time-consuming and expensive job - but not for much longer.
With the help of automated underwriting software, insurers can spend less time wading through paperwork and more time adding value to their business.
How can insurance underwriting be automated with software?
Underwriting can now be automated with the help of sophisticated insurance underwriting software which analyses big data to come to a decision about an applicant's suitability for a policy as well as how that policy should be priced.
Because a lot of the data used in underwriting is quantitative, it's possible for algorithms to do at least some of the work. Automated insurance underwriting software can be used to gather data from customers and telematics, as well as using this data to analyse risk and triage applications by risk level.
This means that some applications can be accepted or rejected automatically; those which are very high risk are rejected, while those applications which are very low risk are accepted. Those in the middle can be triaged and forwarded to skilled underwriters for a final decision.
Automated underwriting keeps insurers in control - allowing underwriters to set their own risk limits and price bands - but speeds up and streamlines the process so that clients receive decisions more quickly and underwriters have more time to spend on the most difficult tasks.
Automated insurance underwriting can also make application triaging more accurate,more consistent and completely free of human error. However, the real benefits of automated insurance underwriting really begin to take shape when AI and machine learning come into play.
The role of AI and machine learning in automated insurance underwriting
Automated underwriting is only possible today because of artificially intelligent computer software like artificialOS, which can be used to digitise the quote, bind and issue process for insurers. But software like this is capable of so much more.
While AI is used to describe any kind of intelligent machine or technology, machine learning is the term used for when artificially intelligent systems can learn and improve over time. Automated underwriting software can do this, too.
By analysing patterns in policy and claim data, machine learning software will tweak its algorithms over time to reflect both inaccuracies in the original process as well as changes in customer behaviour.
Perhaps in 2005, certain behaviours were indicative of higher risk levels, but in a culture which is changing all the time that historical trend may no longer be relevant.
Machine learning can also be used to tackle cybersecurity issues and prevent fraud. Because machine learning software is designed to analyse patterns and track trends, it can spot red flags and anomalies that human underwriters aren't even looking for, as well as identifying new signs of fraud as they arise - a common scenario in today's changing insurance landscape.
Why automate underwriting?
Partially automating the underwriting process can make the claims process simpler for both the customer and the insurer. Automation can help insurers to digitise claims, reducing turnaround time for customers as well as cutting down on paper waste.
Using automated insurance underwriting software also makes it easier to keep track of individual claims with in-built claim tracking and reporting. This makes it simpler to ensure claims are on-track, as well as simplifying communication between the insurer and their customer.
It also means insurers can make use of a much larger data set than they can when underwriting manually. Traditional underwriting methods may take into consideration a small number of data points acquired from the client, but big data has never been an option until now.
Today, automated insurance underwriting means big data - meaning potentially hundreds of data points per application - can be scoured in seconds for any details which might affect that applicant's risk level.
By using artificially intelligent insurance software like artificialOS, you can speed up the applications process and free up your underwriters for more important tasks, such as pricing and managing only those tricky 'mid-risk' applications, or spending more time tailoring policies for individuals.