Five uses of AI in commercial underwriting
By Charles Dugas, Insurance AI Lead, Element AI —
Underwriting is all about data. Since its inception, people in the insurance profession have been evaluating data on risk, value, and other factors in order to make their decisions. Today’s underwriters have more variables to contend with, more submissions, more competition, and more data of all kinds to deal with than ever before. That’s why more and more insurance firms are deploying AI in commercial underwriting.
Machine learning (ML) and AI are incredibly well suited for helping to deal with the masses of data underwriters now face. These technologies are changing underwriters’ working lives for the better and delivering huge benefits to businesses and the insurance industry as a whole.
In this article, we’ll explore five key ways you can implement AI and ML in the underwriting process and the results they can achieve. Without further ado, let’s get started.
(1.) Processing underwriting submissions
Although efforts have been made to streamline submission processing, many lines of business in the insurance industry still have to deal with large volumes of documents that need to be processed manually. Until now, that’s just been part of the job — and a time-consuming, laborious one.
New applications of AI in commercial underwriting can give great assistance in extracting information from PDFs, printed documents, emails and even handwritten documents, reducing the amount of work underwriters need to do by hand. Optical character recognition and natural language processing are now sophisticated enough to identify the required data in a document, extract it, and even perform a degree of evaluation. These advances in text extraction and analysis are opening up new efficiencies in underwriting processes, expediting workloads that had previously been a burden to insurance professionals. Time saved on submissions processing is time gained for more rewarding work that makes better use of underwriters skills and helps to develop the business.
(2.) Making risk appetite decisions
As you know, reviewing submissions for viability is another task that can take up a lot of an underwriter’s time. Analyzing the submission and all the related risk data, making the decision whether to underwrite it or not – it all takes time and effort. And it’s another area where you can deploy AI in commercial underwriting to achieve great results.
Machine learning can now offer underwriters valuable assistance in the decision-making process. Using data on previous applications that have been approved or rejected, these systems build an understanding of which are likely to be viable and which aren’t. They can automatically decline certain activities described in the application as free-form text, if deemed too risky or otherwise unviable. Using text classification, these activity descriptions can be automatically mapped onto their corresponding industry codes, based on a given standard. If an application is found to be viable according to the system’s judgement, it can also recommend the most appropriate product according to your historical data. Once again, this valuable assistance can be a real asset for time-pressed underwriters.
(3.) Submission assignment and triaging
Some underwriting submissions, in certain lines of business, require extra attention during processing. They need to be prioritized, but unlike other submissions, this can’t be done using simple, blanket rules such as their policy effective date. Underwriters need to look in greater depth in order to decide their priority.
Using AI in commercial underwriting can help here, too. Optimization and forecasting technologies can assist in assigning these submissions to the most appropriate underwriter. Predictive modelling can also rank submissions according to their estimated closing ratio or some other KPI. So, for instance, it could decide to rank one application highly because you’ve recently been successful at closing business with that broker. These innovations have a tangible impact on how well your business operates and your bottom line: submissions are allocated more effectively, and your overall closing ratio improves.
(4.) Evaluating risk profiles
In order to evaluate the risk involved in a submission, underwriters must often invest considerable time in research. They must research and weigh up all kinds of information in order to properly evaluate these risk profiles. Sifting through the wealth of information available, in myriad formats, can be like searching for a needle in a haystack — until now.
Today’s intelligent tools can search through many types of structured (processed and labelled) data as well as raw, unstructured data and aggregate relevant information for underwriters to use. For instance, an underwriter may use this system to search through a database of property inspections, to compare similar cases of structural damage and their results. These systems also make it far easier to retrieve similar past applications to see patterns and learn from earlier experience. Now your business never has to make the same mistake twice.
As we said earlier, AI is the master of dealing with large volumes of complex data, so when it comes to locating and surfacing valuable items of information like this, it’s in its element. The benefits for underwriters and businesses are huge here: they can be better informed and more confident in their risk evaluations.
(5.) Coverage recommendations
Toward the end of the underwriting submissions review process, it’s time to make a judgement: what coverages will be recommended? AI-powered systems are capable of assisting end-to-end, so they have much to offer at this point, too.
Recommender systems can help with coverage judgements. By analyzing previous applications, they can get a sense of what the appropriate coverages, with limits and deductibles, might be and offer suggestions the underwriters can use to make their final decision. On a business-wide scale, this means your product and coverage recommendations will be better aligned with clients’ needs and their risk profiles.
Ready to deploy AI in commercial underwriting?
All the use cases we’ve outlined here are available to businesses right now, so if you want to start deploying AI in the underwriting process, you can start attaining the benefits without delay. If you’d like more details, and to learn how leading insurers are making the most of AI, just take a look at our Element AI for Insurance section.
As the industry evolves in the coming years, we’re certain that AI will become an even more useful assistant to underwriters all over the world. And as new applications of AI in commercial underwriting are developed, we look forward to telling you all about them.
Interested in deploying AI in commercial underwriting? We’d be glad to discuss the possibilities and what your first steps might be. Talk to the team at Element AI today.
About the Author
Charles Dugas joined Element AI in September 2017 as Director of Industry Solutions where he leads the insurance vertical, helping carriers and other insurance market participants develop and implement a strategic vision and roadmap for the rollout of AI capabilities across their organizations. Charles holds a B.Sc. in Actuarial Science, a M.Sc. in Electrical Engineering, and a Ph.D. in Machine Learning with Yoshua Bengio. He has 20 years of experience developing and implementing analytical solutions in financial services, either as an entrepreneur, a professor, or manager of large corporations’ analytical teams.
About Element AI
Element AI is a global developer of AI software that helps people work smarter. Founded in 2016 by serial entrepreneurs including JF Gagné and A.M.Turing Award recipient, Yoshua Bengio, PhD, Element AI turns cutting-edge research and industry expertise into software solutions that exponentially learn and improve. Its end-to-end offering, including advisory services, AI enablement tools and products, aims at helping large organizations operationalize AI and create real business impact. Element AI maintains a strong connection to academia through research collaborations and takes a leadership position in policy-making around the impact of technology on society.
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