Tag Archives: underwriting

AI in Commercial Underwriting

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 that 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 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 – 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. The systems 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 judgment, 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 triage

Some underwriting submissions, in certain lines of business, require extra attention during processing. They need to be prioritized, but, unlike with other submissions, this can’t be done using simple, blanket rules such as their policy effective date. Underwriters need to look in greater depth 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 modeling can also rank submissions according to their estimated closing ratio or some other key performance indicator (KPI). For instance, AI 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.

See also: ‘3D Underwriting’ in Life Insurance

4. Evaluating risk profiles

To evaluate the risk involved in a submission, underwriters must often invest considerable time in research. They must research and weigh all kinds of information 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 labeled) 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, AI is 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 judgments. 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 obtaining the benefits without delay.

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.

This article was originally published here.

3 Big Opportunities From AI and ML

In 2020, we find ourselves living in a world that demands a real-time shopping experience. Brands like Amazon make this experience as easy as possible by providing the option to compare one product against another product(s). The comparisons include price, features and the length of time it will take for the product to arrive. Furthermore, we can see recommended products based on buying behavior patterns, as well as related products that can be purchased to maximize the overall value. Each of these factors weigh into how, when and from whom we purchase.

Behind the scenes of Amazon’s user experience are two key technologies driving innovation: artificial intelligence (AI) and machine learning (ML). These terms are not often tossed around when referring to the current group insurance shopping experience, although there is certainly much room for carriers to integrate these innovations to their benefit. The McKinsey Global Institute reports that up to 60% of insurance sales and distribution tasks could be automated, as well as up to 35% of underwriting tasks.

Herein lie three major machine learning opportunities to unlock a better user experience for all stakeholders in the purchasing process, from sales representatives and underwriters to brokers, employers and employees. 

1. Automating Broker Emails and Required Quoting Documents

Imagine if Amazon required you to email a request every time you wanted to purchase a product, without knowing when the product would arrive, how much it would cost or whether it would even be shipped at all until three to five days after sending the original email. 

In many cases, this is the experience today for brokers who email a request for proposal (RFP) to a group insurance carrier. And so we arrive at our first opportunity for machine learning; speeding up the quote turnaround time (TAT) by automating the setup of broker emails and documents required to quote. As we peel back the onion to see how most life and disability and worksite group carriers receive and process quote requests today, it is clear how manual the current process is. This process often entails inputting data twice; once in a CRM such as Salesforce, and a second time in a quoting and underwriting engine, or spreadsheet on macro steroids.

Much of this process can be automated by leveraging machine learning to train a model that runs through thousands of previous broker email RFPs to understand broker requests, the differences between brokers and what information is required to quote the desired products. Oftentimes, brokers do not provide all the information necessary for quoting, which today is handled by placing the group “on hold.” The RFP intake specialist then has to manually email the broker back and ask for the missing information to proceed with the quote request. Machine learning can help to quickly identify what is missing, and automatically reply to the broker requesting this information and drive to completion.

See also: COVID: How Carriers Can Recover

2. Automating Plan Design(s) to Quote

Many times the RFP includes a current coverage contract or booklet that could be anywhere from 30 to 50 pages. This document contains all the clues as to which plan design should be quoted to compete with the carrier currently in force. The foundational plan design to quote starts with matching up the exact benefits for each product line and, you guessed it, going line-by-line through that 50-page contract booklet to manually hand-stitch a plan design to quote. As you can imagine, this is not the most efficient experience for the RFP intake specialist, nor the broker who ends up receiving a quote riddled with manual errors and plans that do not match up with the customer’s current coverage.

In this case, a machine learning model can be trained to extract all the plan design elements from any incoming file that contains current coverage details. This ML model would be able to decipher the current carrier’s format structures and benefit naming conventions, and subsequently translate them into the quoting carrier’s structure. Of course, there are instances in which a customer’s current plan design is not able to be quoted, sold and administered. In this case, a machine learning model would be able to flag any benefits that aren’t able to be translated and accounted for. To get the maximum value, this use case assumes an API integration with a quoting engine to automate plans to quote.

3. Analyzing Closed-Won and Closed-Lost Proposals 

At the moment, once a case has been either sold or lost, most carriers are not harnessing the true power of the resulting data (i.e. the insights and components required to make a winning proposal.) Carriers tend to look more closely at closed-won proposals because they have to use this data to implement policies and sold rates. But even here, the data currently being captured and tracked leaves much room for improvement.

Machine learning and AI models can be used here to better analyze which RFPs are the most likely to win based on a variety of factors. For example, an ML model could track the current carriers and rates on incoming RFPs and gather won/lost data once the sale has closed. This data can be used to inform which future RFPs are most likely to win based on the customer’s current carrier.

On the flip side, closed-lost proposal data (that now typically ends up in an abyss far from any BI visualization tools) could be used to show key factors as to why the case was lost. A national life and disability carrier focused on the small group sector may have around 100,000 RFPs a year. If the close ratio is 9%, that means 91,000 proposals were lost. These thousands of proposals could be fed into a machine learning model to analyze their ingredients, in the hopes of adjusting the sales recipe to increase future close ratios.

A More Profitable Future  

Opportunities for ML and AI implementation within the group industry are evident, and these use cases will ultimately enhance the user experience as well as service policies, manage billing, process claims and handle renewals. 46% of AI vendors in insurance offer solutions for claims, and 43% have solutions for underwriting; the solutions have been far more widely used within the home and auto industry than in the group insurance sector. One important part of this approach is to identify where the “lowest hanging fruit” use cases exist, which can be implemented in a proof-of-concept fashion.

See also: How Machine Learning Halts Data Breaches

The implementations can either be achieved with internal teams or by working with insurtech partner solutions. The first and second ML opportunities outlined both exist within the RFP intake process, which can provide direct operating savings ROI, whereas the third may take longer to actualize as close ratios gradually increase. To move toward a more profitable future, it is essential that group carriers notice and take full advantage of the advancements being made in machine learning technology today.

3 Key Enablers for Better Underwriting

Within the commercial property and casualty sector, there has been a lot of attention on the underwriting process, which is inherently manual and time-consuming. Carriers and insurtechs are primarily trying to accomplish two things: make it easier for the end user to buy insurance and improve the accuracy and efficiency of underwriting.

These two goals complement each other. By tapping into third-party data sources, carriers can get more accurate information. Carriers also increase ease of doing business, for both underwriters and customers.

To date, carriers have leveraged in-house tools and partnered with technology providers to gain information for underwriting. Property sensors, public records, telematics and drones are just a few of the sources underwriters are using to access risk. But to stay competitive against other carriers and to continue to cut down on the number of questions that will need to be answered by the applicant, carriers need to continue to innovate.

See also: Winning in Small Commercial Lines  

An Accenture report centered on the rise of insurtech found that 86% of insurers believe innovation must happen faster. So, what can carriers do to boost their underwriting game?

  1. Start with areas that are aligned with strategic objectives: Insurance carriers need to think more strategically about innovation. Areas of investment in innovation need to tie directly to where you want to grow or improve from a business perspective. It is easier to gain internal buy-in and traction on topics that everybody already agrees need the most attention.
  2. Understand operational readiness for prioritized uses: Before any investment takes place, business leaders must understand what it would take to put an idea into production. In many cases, a clear path to test a new capability is identified–e.g.. limited proof of concept (POC)–but the actual requirements, timeline and costs (rough order of magnitude) in a production scenario are not analyzed or understood. Early focus on the desired end state can set the proper vision and avoid stalls and misdirection later on. 
  3. Make it easy to collaborate. The thing that insurance carriers that have strong innovation programs have in common is that they are not afraid to collaborate with insurtech partners. Today’s insurtechs are building niche businesses that can be tapped to enhance specific parts of the insurance value chain, often far faster than a carrier’s internal capabilities could allow on their own. Leading-edge carriers are collaborative and oriented toward feedback. The approach by insurtechs creates a healthy ecosystem and promotes effective product development.  

Once a carrier implements new technologies in the underwriting process, the organization should ensure that it is measuring the improvements in accuracy and efficiency. One way is to look at the organization’s overall profitability. Another is to look at team productivity–if measures have been put in place to boost efficiency and accuracy, the same underwriting team should be able to quote more policies than before, and more quotes should be “bindable.”

Agents will find it easier to do business with carriers that operate more efficiently and effectively.

While the insurance industry has fallen into the late adopter category, carriers and insurtechs have the opportunity to take advantage of a market that is ripe for change. 

See also: The New IoT Wave: Small Commercial  

DataCubes focuses on powering commercial underwriting using decision science. The organization was built on the idea that there is a more productive way to underwrite.

How Machine Learning Transforms Insurance

We like our insurance carriers to be risk-averse. So it should come as no surprise they are often last to innovate. Insurers need to feel very comfortable with their risk predictions before making a change. Well, machine learning is writing a new chapter in the old insurance book. There are three key reasons why this is happening now:

  1. New insurtech players are grabbing market share and setting new standards. Traditional carriers have no choice but to follow suit.
  2. Customers are expecting Netflix/Spotify-like personalization, and have no problem changing providers — this trend is expected to grow as we see more millennials maturing out of their parents’ policies.
  3. Getting started with machine learning is becoming VERY easy because of open source frameworks, accelerated hardware, pre-trained models available via APIs, validated algorithms and an explosion of online training.

As with any innovation, it only takes two things for widespread adoption:

  1. Potential to improve business goals.
  2. Ease of establishing pilots.

With time, we see that successful pilots become products. Teams are hired/trained, resources are allocated, business goals gain more “appetite” and models are tweaked.

For P&C carriers. we see the opportunity for improving business goals and easily pilot machine learning in the following areas:

Risk Modeling

Given the complex and behavioral nature of risk factors, machine learning is ideal for predicting risk. The challenge lies in regulatory oversight and the fact that most historic data is still unstructured. This is why we often see machine learning applied to new products such as those using data from IoT sensors in cars (telematics) and home (connected home). But innovative carriers are not limited. They use pre-trained machine learning models to structure their piles of unstructured data: APIs to transcribe coupled with natural language understanding (NLU) extract features from recorded call center calls, handwriting and NLP/NLU tools for written records, leading toward identifying new risk factors using unsupervised learning models.

See also: 4 Ways Machine Learning Can Help  


Carriers can get an actuarial lift even without designing and filing new actuarial models. Using machine learning to better predict risk factors in existing (filed) models. For example, a carrier may have already filed a mileage-based rate-plan for auto insurance but rely on user-reported or less accurate estimates to determine mileage. Machine learning can help predict mileage driven, in a less biased and more accurate way. Similarly, APIs to pre-trained chatbots using lifelike speech and translators can turn website underwriting forms into more engaging and personalized chats that have a good chance to reduce soft fraud.

Claims Handling

Claims handling is a time-intensive task often involving manual labor by claims adjusters onsite. Innovative carriers already have policy holders take pictures and videos of their damaged assets (home, car…) and compare with baseline or similar assets. Carriers could easily leverage existing APIs for image processing, coupled with bot APIs to build a high-precision model, even at the expense of low recall. Compared with having 100% of the book handled manually, a triage bot that automates even a mere 20% of the claims (with high precision) can enable carriers to start with a low-risk service that’s on par with new insurtech players and improve ratios over time. Such a tool can even be leveraged by adjusters, reducing their time and cost.


While personalized pricing may be regulator-challenged, personalizing the insurance product offering is expected in this Netflix/Spotify age. As basic coverage is commoditizing, carriers differentiate their products based on riders and value-added services, not to mention full product offerings based on life events. Carriers can (with consent, of course) leverage social media data to tailor and personalize the offering. Similarly, marketing departments can use readily available recommendation algorithms to match and promote content about the benefits of certain riders/value-adds to relevant customers at the relevant time.


The world of insurance distribution is growing in complexity. Carriers are struggling with the growing power of intermediaries, and agents are having hard time optimizing their efforts due to lack of predictability of loss commissions. Point-of-sale and affiliation programs are growing, and with them the need for new distribution incentive models. Both traditional and new distribution channels could benefit from machine learning. Brokers, point-of-sale partners and carriers can leverage readily available machine learning models and algorithms designed for retail, to forecast channel premiums. Carriers can grow direct channels without growing headcount, using pre-trained chatbots, NLU and lifelike speech APIs.

See also: Machine Learning: A New Force  

Machine learning is part of our everyday lives. Innovative insurers are now jumping on the ML wagon with an ever-growing ease; which carriers will be left behind?