Tag Archives: artificial intelligence

AI Investment in Commercial Lines

Artificial intelligence (AI) has been in almost every technology-based headline over the past 24 months. If an incumbent technology provider or a newly emerging insurtech organization wants to grab attention – well, just insert AI in the first few lines of the description. Better yet, insert AI in the product or organization name.

In fact, AI holds exceptional business promise, and there are numerous proven use cases. But AI is a complicated topic.

There are many sub-categories of AI, and one of the first steps in choosing the appropriate technology is to break down AI into consumable bites. SMA finds that there are six primary AI technologies in play within commercial lines organizations: machine learning (ML), computer vision, natural language processing (NLP), user interaction technology, voice technology and robotic process automation (RPA). The big question is – which AI technologies drive the most value for commercial lines?

Not surprisingly, there is a tug-of-war between AI for transformational purposes and AI for tactical purposes. According to commercial lines executives and managers, ML, RPA, computer vision and NLP (in that order) will transform commercial lines the most. Given the general need for transformation across the insurance industry, one could conclude that the previously stated order of technologies would be where the industry is heading in terms of investment. But that is not the case.

The actual investment order is new user interaction tech, machine learning, RPA and NLP, with the remaining technologies following. Does this mean commercial lines insurers have gotten it wrong? The answer to that question is “no,” with possible shadings of “could be.” Much of the framing for this answer lies in the product mix.

For the small business and workers’ comp segments, new user interaction technologies such as chatbots and text messaging have been invaluable in contact centers. This affects underwriting and claims by clearing tasks from work queues, thus freeing up technical expertise for more complex interactions. Billing benefits, as well. Collaboration platforms and real-time videos proved highly valuable during the pandemic’s height and continue to be highly worthwhile.

Machine learning has universal value across product lines. Whether it be ML to improve straight-through processing for less complex lines, such as small business and workers’ comp, or to provide decision support for complicated product lines, ML can contribute in all areas. The great thing about this is that investment in adopting ML skills pays off across the enterprise.

RPA is a technology that not only improves operational efficiency and expense management – important internal goals – but also enhances customer and distributor satisfaction through rapid request fulfillment. Policy service, underwriting and claims all gain value through RPA adoption. Because almost all commercial lines segments have repetitive processes, RPA skills are used universally.

See also: COVID-19 Sparks Revolution in Claims

The “could be” warning comes in terms of computer vision and NLP. Both technologies have significant transformational value in commercial lines, ranging from turning aerial images into information to digitizing paper-based information sources. Prioritizing these technologies sooner rather than later is critical across all product segments.

More than almost any other technology, AI technologies work best in combination — for example, NLP with RPA to increase process penetration. The industry is in its early days when it comes to AI usage, and skill sets are still advancing. The “getting it right” discussion is frequently dependent on product segments. But, over time, value will be universal regardless of product complexity, albeit for different reasons.

For additional information on all six AI technologies and survey results, see SMA’s new research report, AI in P&C Commercial Lines: Insurer Progress, Plans, and Predictions.

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.

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

How to Thrive Using Emerging Tech

Early adopters of artificial intelligence (AI) and machine learning (ML) are able to sift through massive amounts of data and use it to enable various capabilities. These range from making decisions about how to triage a claim using algorithms to improving a customer’s overall claims experience using more data and sources automatically pulled in from AI and ML methodologies. 

But where does the rest of the industry stand with these new capabilities? We released a study around how the top 100 U.S. carriers are benefiting from AI and ML and the challenges and opportunities for an AI-driven future. We found that 75% believe proper implementation of AI can provide carriers with a competitive advantage through better decision-making. 

While only 62% say the carrier they work for is already applying, piloting or planning AI and ML initiatives, these early adopters are already seeing significant AI and ML benefits. In terms of improving the experience for existing customers, insurers are experiencing advantages with faster claims settlements (88%), improved fraud detection (87%) and better risk scoring (85%). On the prospecting side, AI and ML are enabling early adopting insurers more customized and targeted opportunities for cross- and upselling (88%). 

Of the survey respondents representing insurers that are early adopters, most come from the 20 largest U.S. carriers, but adoption across the remaining top 100 U.S. carriers is also rapidly increasing.

While carriers are generally positive about their use of AI and ML, implementation does come with its own set of challenges surrounding staffing, data and compliance. 

The challenges around AI and ML adoption 

Insurance carriers are largely positive about the value of their AI and ML initiatives, but the study identified the challenges they will need to overcome. Staffing challenges are a major concern. According to the study, nearly half of the respondents (49%) said that AI and ML implementation has already affected their staffing plans today. Insurers need people who can understand the inputs and outputs of the applications, and who can explain them to the business. They need knowledge managers who can speak in both technical and non-technical languages and link the dialogue between parties.

See also: Stop Being Scared of Artificial Intelligence

Another major concern is the ability to access high-quality, trustworthy data. The three main issues with data that survey respondents mentioned include their ability to manage the volume and security of the data; linking and normalizing data across different data sources; and ensuring access to the data. Adopters clearly see the value of third-party data, as a majority of the adopters (82%) say their organizations have or will buy external data for their AI and ML initiatives. 

The third concern we found is around compliance and regulatory challenges with insurers’ use of AI and ML. Adopters worry that regulators and legal bodies may not understand AI and ML applications and could possibly block or limit them. Nearly three-quarters (74%) of adopters also have concerns about data privacy, security and ownership issues, anticipating increased regulatory scrutiny as more data sources are accessed and modeled.

Although the COVID-19 pandemic has slowed things, 95% of personal lines insurers are moving forward with their overall technology plans and investments, with only 5% retrenching, according to Strategy Meets Action (SMA). Meanwhile, 75% of commercial lines insurers are moving forward with their overall technology plans and investments, with only 25% retrenching or pausing. 

See also: Step 1 to Your After-COVID Future

Despite these challenges, the early adopters of AI and ML are already seeing benefits. Faster claims settlement, more targeted cross-selling and upselling, improvement in fraud detection and better risk scoring are just a few advantages that insurers are leveraging. As insurance carriers look to implement emerging technology, they should find a technology partner that has a deep understanding of the data, analytics and insurance industry to help them maximize their AI and ML initiatives. In particular, they should look to find a partner with a demonstrated expertise in building models that leverage advanced analytics and that have extensive experience in managing, normalizing and analyzing increasing volumes of data. By this time next year, only those insurance carriers that are fully embracing and implementing AI and ML capabilities now will have that competitive advantage.

For additional insights and data from our study, you can turn to our white paper, The State of Artificial Intelligence and Machine Learning in the Insurance Industry.

How to Recruit Claims Adjusters

If you look at a list of preferred jobs of college graduates, “claims adjuster” might not make the list. Insurance claims operations likely seem foreign to a 21- or 22-year old who probably has never had to file a claim. There is limited understanding about what is involved or why. One doesn’t hear much about claims adjusters on the news or on YouTube; the profession is all sort of a mystery. Because of this, claims operations tend to be jobs people “happen upon.”

In the past, the insurance industry has not needed to recruit aggressively, but this is about to change. The average claims adjuster stays in the industry for only four years. Another swath of adjusters soon will retire. Employee turnover coupled with an aging out of other workers will create problems that can cripple organizations. Attracting new talent to the field while retaining a larger percentage of experienced workers is critical.

Gaps

There are two gaps that have to be addressed to preserve the health of claims adjusters: the mindset gap and the knowledge gap. The mindset gap pertains to negative perceptions of the field and its related responsibilities as well as to how workers approach their jobs. The primary generations of workers entering claims operations, millennials and Gen Zers, typically don’t plan to stay with the same company in the same position for long. Career aspirations are different than for previous generations ruling claims operations. According to Gallup, only half of millennials plan to be in their same job a year from now, and just 29% feel invested in their work. High millennial turnover costs the U.S. approximately $30.5 billion annually. The numbers don’t look much better for Gen Z — and, in a profession like claims, this paints an even bleaker picture.

The claims field already has strikes against it that other jobs don’t have. If droves of millennials are leaving positions because they are not engaged or emotionally invested, a general perception that claims is a data entry job becomes highly problematic. Piling on, claims representatives traditionally have been viewed as adversaries to claimants, with claims adjusters not trusting the people they are charged with helping. This hardly evokes the warm, fuzzy feeling workers want to experience with their respective jobs, likely hastening a quick exit from the profession.

Compounding the problem, it takes claims adjusters years to accumulate the knowledge needed to be effective. Experienced claims adjusters understand countless factors and nuances that come into play in a claim, creating a sizeable knowledge gap between new and veteran claims adjusters. If employees walk out the door before accumulating this knowledge, that is a huge loss on the investment spent on training without ever reaping the benefits. Similarly, there are significant issues as adjusters retire, taking a career’s worth of knowledge and experience with them before well-trained workers are in place.

See also: Future Is Already Here in Claims

AI: The Secret Weapon in the Battle to Recruit and Retain Talent

One of the most promising solutions to recruiting and retaining workers lies with artificial intelligence — and not in the way that you might think. I am not talking about the solutions that identify potential workers and send them an email. I mean applying AI to attack the actual gaps in mindset and in knowledge.

First, mindset. AI-driven solutions are being implemented across insurance organizations that can provide far richer information about claims while eliminating rote tasks. AI enables claims team members to dive deeper into a claim and instantly discover new information that is meaningful to their decision-making process. With the latest in data science (an attractive term to would-be applicants seeking cutting-edge jobs), AI-based solutions prompt adjusters to use data and apply their own brain to solve increasingly complex challenges.

Additionally, AI helps root out fraud, removing from claims representatives’ minds the fear that someone is trying to run a scam and helping to shift the adjuster’s role from adversary to advocate. Within this new paradigm, it becomes possible to scale compassion so that representatives can truly serve people in a highly efficient manner. This concept, one of scalable compassion, also creates a shift in how claims themselves are viewed. Instead of seeing a hassle or a mountain of red tape to fight through, claimants view their claims as actual benefits and treat representatives with greater respect. These factors combine to make a much more inviting environment for new recruits while assisting in the retention of the current workforce.

In terms of the knowledge gap, because AI adds intelligence to the claims process, the learning curve for newer representatives can be reduced dramatically. AI gets all workers to a competent level almost immediately. It’s an equalizer of sorts. While experience will always be an asset, the knowledge gap essentially disappears when a smart system that tells representatives what they need to know when they need to know it is in place.

See also: The Case for Paying COVID BII Claims

Fortifying the Industry

Because the nature of the claims representative position changes — from adversary to advocate, from heavy on data entry to full of opportunities for insightful decision-making, from a high barrier of entry to hitting the ground running — there is greater appeal and room for growth.

Additionally, in the current environment, the majority of the workforce is being asked to work from home. This has been a difficult transition for businesses across a range of industries. Insurance has been surprisingly adept, however, with many claims adjusters already working remotely. This practice will continue after COVID-19 passes, which can be a perk for many millennials and Gen Zers who don’t want to commute to an office every morning and who value flexibility.

With a little innovation, a more adaptable approach and proper application of AI-based technology, the claims industry can transform what it means to be an adjuster; attract new, talented workers to organizations; and provide them with long, fulfilling careers — all while averting a hiring crisis.

Without such change, I predict the claims process as we know it will unravel within a decade.

The next step is clear. Will you take it?