Tag Archives: underwriting

Role of Underwriter in Age of Insurtech

The age of insurtech has brought a wave of new digital experiences and automation in insurance. From websites that instantaneously compare auto insurance quotes to mobile apps that allow us to submit claims directly by snapping a picture of a damaged window, we continue to benefit from significant improvements to the insured experience.

These improvements in distribution and claims are part of an industry-wide appetite for increased accuracy and efficiency, including in underwriting. Personal lines carriers have already made good strides, and carriers see a similar opportunity to improve loss and expense ratios in commercial lines.

For small business policies that involve a high volume of submissions and lower premiums, the challenge is to enable an efficient, high-throughput underwriting process that complies with exacting standards for quality. In the mid-market, the stakes are even higher for underwriters. They must be diligent about selecting high-quality risk against a backdrop of declining capacity and a tsunami of submissions from brokers who remarket risks in search of better rates.

While the goal of shorter time-to-quote is laudable, and addresses a critical frustration for insureds and brokers, the implementation often overlooks the crucial role that underwriters play. By failing to listen to underwriters’ needs and play to their strengths as expert assessors of risk, technology providers and insurers alike continue to achieve sub-optimal underwriting outcomes.

Commercial underwriters are at the forefront of some of the most challenging and important work in the industry. They serve a multi-faceted role: developing and fostering relationships with brokers, exhaustively reviewing submissions, validating an insured’s business and property information, analyzing exposures and, eventually, rating, quoting and binding policies. Underwriters must bridge the gap between carriers that set aggressive goals for profitable premium growth and brokers who want a quote “yesterday” — and often pair incomplete submissions with demands for a rapid turnaround.

When underwriters conduct a thorough investigation of the risk – executing online searches, ordering inspections and asking tough questions, they’re invariably perceived as being too slow, inflexible and uncooperative. If they compromise on thoroughness to increase throughput, or if too many submissions are superficially passed through, their book may grow quickly, but the quality and profitability will suffer. All the while, underwriters want to deliver a comprehensive policy that best addresses the insured’s needs and grows the relationship. Reconciling these often-conflicting priorities is difficult but sets the most effective and experienced underwriters apart.

Data analytics, artificial intelligence and machine learning can make a big difference but, for most insurers, have failed to deliver great value within underwriting.

Improving outcomes requires an approach that combines the best of underwriter judgment with machine intelligence.

See also: The Future of Underwriting

Specialized, AI-powered software can now do much of the heavy lifting for underwriters, while eliminating frustrating activities. Underwriters who experiment with, and embrace, new technologies are already setting themselves apart from their peers. They stand to improve their individual performance and also help to chart the future course of underwriting within their organizations.

For insurtechs to truly deliver on their collective promise, they need to empower those who are actually performing the work of insurance. Automation and machine learning need to be force multipliers for underwriting excellence – not poor substitutes for it. Getting this right will lead to a better experience for the insured and superior outcomes for the industry.

The Next Wave of Insurtech

Long before the COVID-19 pandemic, insurers were investing in digital transformation, spurred by the rise of startups. Those investments took on new urgency as the pandemic forced businesses across industries to move to digital operations to stay afloat. 

Over the long term, no technology will prove as vital to insurers’ agility and success as artificial intelligence, whose far-reaching impact will define the next wave of insurtech innovation.

Legacy players and nascent startups alike will leverage AI and machine learning to enhance customer service, speed claims processing and improve the accuracy of underwriting – enabling insurers to match customers to the right products, operate with greater efficiency and achieve better results.

Though insurance is often cast as slow to embrace technology and innovation, in a certain respect AI is very much within the industry’s wheelhouse. Since the first actuaries began their work in the 17th century, insurance has relied heavily on data – and as AI empowers insurers to do even more with vast swaths of data, the benefits will redound to providers and policyholders alike.

Bringing Customer Service to the Next Level

In today’s digital economy, personalization is all the rage. Customers crave tailored, relevant experiences, offers and promotions that reflect their unique backgrounds, needs and interests – and they increasingly expect businesses to deliver these experiences as a basic standard of service.

While personalization is often discussed in the context of sectors like e-commerce, the insurance industry is no exception to this trend. According to an Accenture survey, 80% of customers expect their insurance providers to customize offers, pricing and recommendations. 

Of course, delivering bespoke experiences requires an abundance of customer data – and customers are more than willing to provide it in exchange for personalized service; 77% told Accenture that they’d share their data to receive lower premiums, quicker claims settlement or better coverage recommendations. 

Because personalization can only deliver on its promise if it’s holistic and omnichannel, the most successful insurers will be those that don’t view personalized engagements as one-offs – a tailored email here, a promotion there – but that consistently provide personalization at every stage of the customer journey. 

What will that look like in practice? AI chatbots will become a lot more “chat” and a lot less “bot,” not only providing 24/7 customer service but also using cutting-edge methods like natural language processing (NLP) to better understand what customers actually need and to conduct more natural, intuitive conversations. Underwriting will become much more precise as machines crunch massive sets of data – reams of usage and behavioral data generated by customers and their IoT devices, as well as relevant geographic, historic and other information – to create customized policies that reflect a policyholder’s true level of risk. 

See also: Insurtechs’ Role in Transformation

From Cumbersome to Swift

Harnessing the power of AI, insurers can also streamline claims processing as part of a comprehensive digital strategy. Forward-thinking providers will increasingly integrate automated customer service apps into their offerings. These apps will handle most policyholder interactions through voice and text, directly following self-learning scripts that will be designed to interface with the claims, fraud, medical service and policy systems. 

As a McKinsey analysis noted, with automated claims processing, the turnaround time for settlement and claims resolution will start to be measured in minutes rather than days or weeks. Meanwhile, human claims management associates will be free to shift their focus to more complicated claims, where their insights, experience and expertise are truly needed. 

These transformative applications of AI will unlock revenue opportunities, improve risk management and help insurers deliver a new level of personalized customer service. But if AI will act as the great enabler, what will enable AI itself?

The answer lies in a robust digital core, which is vital to facilitating efficient business processes, maintaining resilience in an unpredictable world and supporting the rollout of new products and business offerings. Whether insurers manage to achieve that kind of digital agility will determine their ability to survive and thrive in a landscape that’s shifting faster than ever.

Long-Overdue Change in Commercial Lines

When Deb Smallwood and I wrote A Recipe for Commercial Lines Underwriting Transformation, we articulated the need to break free of the traditional paradigm of siloed and incremental evolution being viewed on a quarter-by-quarter and year-by-year basis. We challenged commercial insurers to leap forward with a big vision for the future and then reverse-engineer a holistic strategy to deliver the vision that leads with business, people and culture and is enabled by technology. This is the path to meaningful transformation that will ensure sustainable economic success AND create attractive environments that support good talent and generate further innovation. Our industry survey confirmed that significant change is on the horizon and that we’re not taking all the right steps today to prepare for, let alone capitalize on, that change. Meaningful differences emerged between the way small commercial units and medium-sized to large commercial units are positioned today, and what their paths forward should look like. Download the eBooks here for additional detail.

Perhaps not unexpectedly, given the prioritization and investment in personal lines and small commercial, we find that small commercial executives expect significant change to affect their organizations more quickly than do leaders in the medium-sized to large market space. Small commercial expects more dramatic change over five years, and our data shows that they are further along in envisioning and executing toward a future state, though there are still notable gaps to which attention must be paid.

Underwriting in the middle and large market spaces has historically been more complex and handled in a bespoke and high-touch way. It is here that we see the infinite layering of tactical tools as a stopgap, and prioritization for technology investments are behind their small-market counterparts. It makes sense that they see a 10-year runway for significant change borne out in the data. What gives pause for concern, however, is the lack of clarity on when and where to begin. We see this and other elements as potential roadblocks and warn that a good blueprint is necessary to manage through the construction. This space is ripe for development, and we are focusing on helping insurers create and activate those blueprints.    

There are elements to be learned from the small-commercial journey. Data shows that they are leading with an outside-in, customer-centric view that drives meaningful and urgent change internally. While there are gaps to be attended, that action orientation with the end customer in mind is to be favored over the internal focus on improving speed, expense and risk-taking that is slowing change in the middle market and complex space.

A bright future is unfolding for commercial insurance underwriting, and the possibilities are both exciting and long overdue. Given the risks identified in our research and documented in these eBooks, it is critical to think big and enable real transformation – the market, your teams and leaders and your customers all need and expect it. It is a differentiated starting place between small commercial and the middle market, but, in both cases, we believe the best way to get there is to flip the lens and let the business and people lead the effort, updating processes, with all of it enabled by technology. Creating a culture of change readiness and innovation today pays dividends immediately and is the best way to accelerate this journey toward the future.

See also: The Future of Underwriting

In addition to the small commercial eBook and mid-to-large-market eBook, SMA and Boundless Consulting have written a research paper on this topic and designed a framework to guide organizations on this journey. Click on the links for more information.

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.