Tag Archives: machine learning

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.

How Insurers Are Applying AI

AI is everywhere. Insurers are piloting various AI projects, insurance technology vendors are building it into their solutions, some insurtech startups are all AI-powered and horizontal tech vendors are creating AI platforms that sit underneath it all. Insurers that haven’t experimented with AI yet are benefiting from the technology through third-party relationships, even if they don’t realize it. 

Unfortunately, the broad scope covered by the umbrella term “AI” can cause confusion for insurers — especially because some technology providers use this label to better position their offerings in the marketplace.  

Usage of AI in the insurance world can typically be broken down into four categories:  

  • Machine Learning. The goal of machine learning, a process where an autonomous system learns from a data set to identify novel patterns, is often to refine underwriting or claims algorithms. Applications include advanced predictive modelling and analytics with unstructured data. 
  • Image Recognition. Until recently, images were a type of unstructured data better resolved by humans. Image recognition leverages AI to extract insights from digital image analyses. Applications include photo analysis and handwriting processing. 
  • Audio Recognition. AI-enhanced audio recognition captures any sound (from human speech to a car horn) and turns it into a rich, usable data source. Applications include speech recognition and non-voice audio recognition. 
  • Text Analysis. AI-powered text analysis is pulling out meaningful insights from a body of text (structured or unstructured). Applications include form reading and semantic querying. 

Justifying the Use of AI in Insurance

Novarica’s Three Levers of Value framework can help conceptualize the business value of each AI use case for insurers. Each of these levers — Sell More, Manage Risk Better and Cost Less to Operate — is applicable to a specific AI technology use case. 

Helping insurers identify upsell/cross-sell opportunities, for example, falls under sell more, while accelerating underwriting risk assessment could be categorized as managing risk better and enabling more efficient help desk support helps insurers cost less to operate. 

These are just a few examples of the value AI can bring insurers; AI use cases span categories such as product/actuarial, marketing, underwriting, customer service, billing, claims and compliance. Key use cases include: 

  • Deploying better pricing models. This machine learning use case chiefly falls in the domain of product owners and actuaries, as it applies to the area of predictive analytics. In this case, AI can help actuaries make better decisions when pricing products, thus managing risk better. 
  • Improving marketing effectiveness. This machine learning marketing use case involves using third-party or internal tools to analyze vast amounts of raw data and identify the media channels and marketing campaigns with the greatest reach and engagement levels. Here, big data analytics can help insurers sell more. 
  • Performing better property risk analysis. Using AI-powered photo analysis, underwriters can generate faster and more accurate roof damage estimates. Ultimately, this helps insurers manage risk better. 
  • Leveraging smart home assistants to deflect calls from call centers. Through a voice prompt to their smart home assistants, customers can get quotes, request policy changes and even start a home insurance claim thanks to AI-powered audio recognition. By offering another avenue to help answer customers’ FAQs, insurers free their call center employees to address more complex customer inquiries, decreasing operating costs. 
  • Increasing invoice processing speeds. Through use of text analysis and image recognition technology, AI can help billing staff eliminate error-prone human invoice handling. Using AI-powered form reading leads to greater process efficiencies, which lowers operating costs. 
  • Identifying and mitigating claims fraud. Here, machine learning can help identify potentially fraudulent claims faster. This processing speedup gives claims staff more time to focus on higher-value transactions and leads to better risk management. 
  • Enabling automatic handling of compliance requirements. Machine learning can help team members improve compliance and reporting by automatically handling complex compliance requirements. This results in lower operating costs as compliance staff can direct their attention to tasks requiring human review. 

See also: 4 Post-COVID-19 Trends for Insurers

The AI ecosystem is evolving quickly, with new technology applications emerging every day. We may soon even see further AI and ML processing speedups with the advent of quantum artificial intelligence and machine learning.  

Insurers should not invest in technology-driven projects; instead, governance should search for use-case-driven projects that most benefit the company. However, in the case of important emerging technologies — like AI and ML — it’s valuable to look for ways to deploy that technology and build up skill sets (and culture) within the organization. Additionally, many insurers have an innovation group whose sole purpose is to future-proof the organization by seeking out opportunities to deploy emerging technologies. In these cases, it’s important to refer to actual business use cases and elucidate the concrete value they provide to specific business units.

To learn more on this topic, check out Novarica’s brief, Artificial Intelligence Use Cases in Insurance.

AI in a Post-Pandemic Future

The COVID-19 pandemic put businesses under extreme pressure and has led to a massively accelerated digitalization of the workplace. The silver lining is the opportunity to develop more efficient, digital operating models by reinventing work and leveraging the power of artificial intelligence and automation.

Artificial intelligence and why it matters

Hype has for some time surrounded AI, but promises first made more than 60 years ago are now finally being delivered. What has been the game changer responsible for putting AI back on the map and on the verge of changing, well, just about everything? The answer is deep learning, an old idea that found an opportunity to mature in the late 1990s and early 2000s. 

Based on learning tasks using artificial neural networks inspired by the biological nervous system, deep learning technology is highly advanced and requires vast volumes of data and computing power only recently made possible. By 2030, AI is estimated to contribute as much as $15 trillion to the world economy, making it the biggest commercial opportunity in today’s fast-changing economy. Indeed, the new realities of the post-COVID-19 world require the accelerated adoption of AI to deliver the efficiencies and augmentations of a highly digitized workplace.

Figure 1: AI’s projected impact on global GDP

For more than 250 years, the fundamental drivers of economic growth have been technological innovations, the most important being general-purpose technologies such as electricity and the steam engine. Now it is AI that stands out as the transformational technology of our digital age, which, as with previous GPTs (general purpose technologies), is expected to trigger waves of complementary innovations and opportunities.

What tangible opportunities does AI offer businesses right now? We are currently witnessing the first wave, usually as a result of companies automating tasks and processes, reducing costs and creating more efficiencies. The work dividends from this first wave are mostly positive. Low-level, tedious, hazardous and boring tasks are taken over by machines, freeing time for the humans to do the higher-level, more productive tasks. 

Significant shifts in computing power and availability of large-scale data advance the development of AI applications that continue to rapidly grow in complexity and autonomy. AI’s autonomous nature and the way it is trained on data – essentially learning from the mistakes made in the past – make the technology both an opportunity and a risk.

See also: 4 Post-COVID-19 Trends for Insurers

AI at work

As organizations deploy technologies that automate work or introduce machine intelligence in the organization, the limiting factor in translating these innovations into real business benefits will be talent. Beyond the designers, developers and data scientists that everyone is battling for today, companies will need to explore what new roles are likely to emerge in digital disruptors.

As with many professions, underwriters have been doing a job one way for decades and now are expected to do things differently. The role is primed for transformation as AI is poised to reconfigure and augment insurance underwriting. Fueled by an explosion of data, low-cost data storage and open source technology, AI has the potential to help underwriters analyze an incredible amount of information, find red flags and help make more accurate decisions. 

While there is no expectation for human underwriters to be replaced, as their judgment will still be needed for complex cases, future underwriters will be expected to work alongside AI systems to ensure all risks are accurately measured and priced. As underwriters increasingly interact with automated AI systems, there will be a need for new skill sets to develop, with some old skills potentially becoming obsolete.

Meanwhile, demand for these new skills far outstrips supply at present, which indicates that the main roadblock to insurers capturing the full value of this new technology is not the science, but the human change management factor. It is a tall order, but starting by having the right people with the right skills in the right roles will far outweigh picking the right technology, algorithm or latest start-up to work with.

More digital, more human

One of the major transformations of the digital age is to see more companies adopting a flat working structure, where career paths are less clear and the turnaround of young talent greater. In this new environment, a next-generation operating model that supports the opportunity to learn skills, to have thought leaders provide mentoring and to involve new staff in meaningful projects will be critical to attract and retain the best digital talent. 

By moving beyond a one-size-fits-all approach to human resources and talent management, digital workforce platforms can help create the conditions in which employees feel energized by their work, valued by their organization and happy in their environment.

Google and Apple are examples of early adopters of digital workforce platforms that built ecosystems allowing them to innovate, take advantage of new technologies to cut costs, improve quality, build value and respond quickly to the fast-changing and rising digital expectations of consumers. How can this model be replicated across other industries?

The answer may depend on the ability of corporate leaders to restabilize the workforce — and to reconceive organizational structures — by using the very same digital technologies that have destabilized it in the first place. The incoming AI revolution should reinforce, not weaken, the uniquely human characteristics that define how we work, particularly in the way that we collaborate, communicate and develop relationships. To fully exploit emerging digital capabilities, most organizations will continue to depend on people, with human skills actually becoming more critical in the digital world, not less. 

See also: Stop Being Scared of Artificial Intelligence

As tasks are automated, they tend to become commoditized; a “cutting edge” technology such as smartphone submission of insurance claims quickly becomes almost ubiquitous. In many contexts, therefore, competitive advantage is likely to depend even more on human capacity, on providing thoughtful advice to an investor saving for retirement or calm guidance to an insurance customer after an accident.

AI is likely to be one of the biggest game changers in insurance history, offering a wide range of opportunities from faster and more efficient claims management to a greater variety of on-demand insurance services. As organizations transform to thrive in a digital environment, their success will be affected by how well they integrate their workforce into the transformation journey and manage the tension between the constant drive to innovate and improve and the new governance, compliance and regulatory risks created by new AI technologies. Digital transformation requires the overhaul of culture beyond technology updates or process redesign to reap the anticipated benefits.

Optimizing Care with AI in Workers Comp Claims

The expert panel explains how AI can:

  • let you identify the right provider for a case and steer the injured worker to that provider
  • help busy adjusters easily spot potentially troublesome cases and manage them better, from start to finish
  • continuously optimize your network of providers, so you can be sure to have the right provider working with the right worker at the right time.

This panel consists of: Gary Hagmueller, CEO of CLARA analytics; veteran adjuster Nicole Corey; and CLARA analytics Chief Medical Officer Paul Kim. The panel is moderated by Paul Carroll, Editor-in-Chief of Insurance Thought Leadership.

Don’t miss this free on demand panel discussion. Space is limited, so register today!


Presenters:

Gary Hagmueller

CEO, CLARA analytics

Dr. Paul Kim

Chief Medical Officer, CLARA analytics

Nicole Corey

Owner, California Work Comp Advocacy

Paul Carroll

Editor-in-Chief, Insurance Thought Leadership