Tag Archives: commercial insurance

How ‘Explainable AI’ Changes the Game

Artificial intelligence (AI) drives a growing share of decisions that touch every aspect of our lives, from where to take a vacation to healthcare recommendations that could affect our life expectancy. As AI’s influence grows, market research firm IDC expects spending on it to reach $98 billion in 2023, up from $38 billion in 2019. But in most applications, AI performs its magic with very little explanation for how it reached its recommendations. It’s like a student who displays an answer to a school math problem, but, when asked to show the work, simply shrugs.

This “black box” approach is one thing on fifth-grade math homework but quite another when it comes to the high-impact world of commercial insurance claims, where adjusters are often making weighty decisions affecting millions of dollars in claims each year. The stakes involved make it critical for adjusters and the carriers they work for to see AI’s reasoning both before big decisions are made and afterward so they can audit their performance and optimize business operations.

Concerns over increasingly complex AI models have fired up interest in “explainable AI” (sometimes referred to as XAI,) a growing field of AI that asks for AI to show its work. There are a lot of definitions of explainable AI, and it’s a rapidly growing niche — and a frequent subject of conversation with our clients. 

At a basic level, explainable AI describes how the algorithm arrived at the recommendation, often in the form of a list of factors that it considered and percentages that describe the degree that each factor contributed to the decision. The user can then evaluate the inputs that drive the output and decide on the degree to which it trusts the output.

Transparency and Accountability

This “show your work” approach has three basic benefits. For starters, it creates accountability for those managing the model. Transparency encourages the model’s creators to consider how users will react to its recommendation, think more deeply about them and prepare for eventual feedback. The result is often a better model.

Greater Follow-Through

The second benefit is that the AI recommendation is acted on more often. Explained results tend to give the user confidence to follow through on the model’s recommendation. Greater follow-through drives higher impact, which can lead to increased investment in new models.

Encourages Human Input

The third positive outcome is that explainable AI welcomes human engagement. Operators who understand the factors leading to the recommendation can contribute their own expertise to the final decision — for example, upweighting a factor that their own experience indicates is critical in the particular case.

How Explainable AI Works in Workers’ Comp Claims

Now let’s take a look at how explainable AI can dramatically change the game in workers’ compensation claims.

Workers comp injuries and the resulting medical, legal and administrative expenses cost insurers over $70 billion each year and employers well over $100 billion — and affect the lives of millions of workers who file claims. Yet a dedicated crew of fewer than 40,000 adjusters across the industry is handling upward of 3 million workers’ comp claims in the U.S., often armed with surprisingly basic workflow software.

Enter AI, which can take the growing sea of data in workers’ comp claims and generate increasingly accurate predictions about things such as the likely cost of the claim, the effectiveness of providers treating the injury and the likelihood of litigation.

See also: Stop Being Scared of Artificial Intelligence

Critical to the application of AI to any claim is that the adjuster managing the claim see it, believe it and act on it — and do so early enough in the claim to have an impact on its trajectory.

Adjusters can now monitor claim dashboards that show them the projected cost and medical severity of a claim, and the weighted factors that drive those predictions, based on:

  • the attributes of the claimant,
  • the injury, and
  • the path of similar claims in the past

Adjusters can also see the likelihood of whether the claimant will engage an attorney — an event that can increase the cost of the claim by 4x or more in catastrophic claims.

Let’s say a claimant injured a knee but also suffers from rheumatoid arthritis, which merits a specific regimen of medication and physical therapy.

If adjusters viewed an overall cost estimate that took the arthritis into account but didn’t call it out specifically, they may think the score is too high and simply discount it or spend time generating their own estimates.

But by looking at the score components, they can now see this complicating factor clearly, know to focus more time on this case and potentially engage a trained nurse to advise them. Adjusters can also use AI to help locate a specific healthcare provider with expertise in rheumatoid arthritis, where the claimant can get more targeted treatment for a condition.

The result is likely to be:

  • more effective care,
  • a faster recovery time, and
  • cost savings for the insurer, the claimant and the employer

Explainable AI can also show what might be missing from a prediction. One score may indicate that the risk of attorney involvement is low. Based on the listed factors, including location, age and injury type, this could be a reasonable conclusion.

But the adjuster might see something missing. They adjuster might have picked up a concern from the claimant that he may be let go at work. Knowing that fear of termination can lead to attorney engagement, the adjuster can know to invest more time with this particular claimant, allay some concerns and thus lower the risk the claimant will engage an attorney.

Driving Outcomes Across the Company

Beyond enhancing outcomes on a specific case, these examples show how explainable AI can help the organization optimize outcomes across all claims. Risk managers, for example, can evaluate how the team generally follows up on cases where risk of attorney engagement is high and put in place new practices and training to address the risk more effectively. Care network managers can ensure they bring in new providers that help address emerging trends in care.

By monitoring follow-up actions and enabling adjusters to provide feedback on specific scores and recommendations, companies can create a cycle of improvement that leads to better models, more feedback and still more fine-tuning — creating a conversation between AI and adjusters that ultimately transforms workers’ compensation.

See also: The Future Isn’t Just for Insurtech

Workers’ comp, though, is just one area poised to benefit from explainable AI. Models that show their work are being adopted across finance, health, technology sectors and beyond.

Explainable AI can be the next step that increases user confidence, accelerates adoption and helps turn the vision of AI into real breakthroughs for businesses, consumers and society.

As first published in Techopedia.

Conundrum Facing Commercial Insurance

Artificial intelligence (AI) seemingly has been discussed everywhere over the last few years, and now it’s made its way into the commercial insurance industry. Organizations are using AI and machine learning for everything from streamlining operations to offering more personalized care and better customer service. There is an increasing sense of urgency about getting started on the AI journey. The question is how. Do they develop a custom solution in-house or purchase a third-party solution already on the market?

At first blush, the temptation to build can be strong — after all, you can design exactly what you want for your specific environment. In reality, it’s hard to accurately weigh the perceived benefits of a highly customized internal platform against the time and cost requirements compared with purchasing a tested, third-party solution. To help figure out the best course of action for your organization, I’d like to share some criteria that may guide you.

Staffing

Developing a quality AI-based platform that effectively addresses specific needs requires a dedicated team. To build this team in-house, your organization will need to hire more than just data scientists. Full deployment of a new solution requires product managers, software engineers, data engineers, data scientists, operational experts to develop process and operational workflows, staff to integrate data models into operations, people to manage onboarding and training of the employees who will ultimately use the solution and staff who can quantify value. It’s also important to have all these members operate as one unified team instead of spanning various organizational groups that are not 100% aligned.

For some organizations, this may not be a big deal. For others, the process of recruiting, hiring, training, managing and scaling down staff is one of the worst, and often most prohibitive, parts of embarking on the AI journey. If it’s too daunting to put together a team with the necessary skills, opting for a third-party solution that already has this figured out could be the way to go.

See also: How to Use AI in Commercial Lines  

Data

What types and how much data does your organization currently pull? If you can glean industry-leading insights and possess a treasure trove of information internally, you may want to keep it under lock and key, developing new ways to access and analyze it in-house. But this is usually the exception rather than the rule due to the complexities involved in the insurance industry. Even very large organizations with a high number of claims may lack a preponderance of data on a particular feature, injury or litigation scenario. An external vendor, however, could have data aggregated more broadly to cover all situations. External AI vendors draw on a wealth of anonymized and aggregated data from both public and private sources. This means data models can be trained more quickly and accurately.

Customization

This is an area where in-house development wins. Your organization can build something from the ground up completely specific to your needs at every turn. If you opt for a third-party solution, there are some constraints that you have to adhere to. However, it’s important to think of customization not just at a point in time but also across the entire life of the AI solution. While you might be able to build exactly what you want right now, if you don’t have continued focus, the solution will become obsolete rapidly. This brings us to the next point.

Continued Focus

Just because an AI-based solution is created and implemented doesn’t mean the work is done. It is, in fact, just the start of a journey that requires a dedicated team focused obsessively on the problem. These solutions need to evolve fast, or they will rapidly get irrelevant. Models need to refresh. And platforms and software need to be updated, maintained and optimized. When planning for this in-house, factor in both the staff and time involved to refresh models, fix bugs or add fields or features. If you go the third-party route, maintenance and improvements are typically included in the cost or subscription. If you feel uncomfortable dedicating an internal team to the project on a continuing basis, it might be better to go to a third party.

Security

When it comes to security, in-house platforms have an edge because data is not shared outside of the organization. While you still have to ensure that your networks, systems and endpoints are carefully managed, you are in control. While evaluating third-party vendors, it’s important to check their security credentials and processes to handle data. They need to be as good as your internal processes (if not better) with clear evidence of tight controls through certifications like SOC 2 Type II, HIPAA and HITRUST.

Time to Capture Value

There is a race going on to bring down cost structures dramatically. This is driven by the premium pressures in the market. The primary way to improve combined ratios is by pushing on operational efficiencies. Time matters. It’ll help to think hard about how you could capture value quickly. Ask yourself how much time it will take to:

  • Assemble the team
  • Receive data and set up a data pipeline
  • Design the solution
  • Build the solution and create a testing infrastructure
  • Operationalize the solution
  • Design and implement a way to track value
  • Continuously iterate on the solution

Cost

Your ultimate decision may come down to some basic math. Once you’ve narrowed the list of potential outside vendors and receive their quotes — which typically include a continuing fee that covers hosting, support, performance and additional improvements — you can compare them with what you estimate for the total of building a solution internally. In calculating this estimate, factor in staffing, training, infrastructure and hosting costs as well as maintenance and continuing improvements, as previously discussed.

See also: Leveraging AI in Commercial Insurance  

I hope these guidelines assist you in making the decision on how to best bring AI into your organization. There are pros and cons to both building and buying. The trick is to prioritize your needs and what is actually feasible and realistic for your company to ensure that the result more than justifies the means to get there.

Intellect SEEC’s Pranav Pasricha

Pranav Pasricha, CEO of Intellect SEEC, discusses the company’s goal of helping insurers keep the insured top of mind in their innovation efforts, and maintaining that view throughout the evaluation, development and integration of new technologies.


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How to Use AI in Commercial Lines

Last time, we discussed some of the potential benefits of AI in commercial insurance. Now, let’s talk making the business case.

Many insurers are hesitant to invest in AI without proof that these theoretically smart systems will yield real-world returns. A mature AI vendor will have the foresight to develop a team within its organization that’s dedicated to value analytics. This team — made up of data scientists and actuarial experts — will use the company’s own AI solution to run a simulation that can quantify potential savings that the solution could provide.

This capability is crucial, as insurers don’t want to wait three or four years to realize a return. The value analytics team will take an insurer’s historical data and run the simulation. It might conclude that if the insurer had implemented this AI solution two years ago, it could have saved a certain amount — such as 5% to 10% — on claims costs. This percentage of savings might be based on a specific action, such as moving injured workers from low-ranked providers to high-ranked providers — or doing the same for attorneys. Or, the savings might encompass claims that could have avoided certain scenarios, such as surgery or litigation.

See also: 4 AI Payoffs in Commercial Insurance  

Once the AI solution is deployed against live data, the models continue to run every month (or quarter) based on a pre-defined set of performance metrics. Every month (or quarter), the calculations become more accurate, moving from a rough estimate to a tighter range and eventually to a precise calculation of savings achieved.

Traditional models were challenged by the fact that claims are long-term transactions that can take as much as 18 to 24 months to close, but AI — with its machine learning — is able to handle this complexity with a high degree of accuracy.

A Holistic Approach, Not a Silver Bullet

In folklore, it’s the silver bullet that kills the wolf. This bullet has come to signify a simple solution that magically resolves an insurmountable problem. However, an important part of making AI real is understanding that, while it is powerful, it’s no silver bullet.

At the end of the day, AI is most effective when it’s part of a holistic approach. All the pieces of the puzzle must be put in place. At a high level, these pieces include the AI technology itself, operational tweaks and metrics to gauge results. Impact follows when all these components work in harmony. When these conditions are there, we’ll begin to see the needle move on costs and outcomes. For example, insurers can use AI insights to create more efficient workflows; they can facilitate more effective hiring and training practices that enable human resources to apply their expertise at precisely the right moment in the claims process. It’s iterative, with machine learning driving change in a continuous cycle.

See also: New Era of Commercial Insurance 

Although immediate savings can be achieved, an enduring competitive advantage can only be realized when the application of AI is seen as a journey. It requires continuing effort and investment. Strategic players understand it can take a few years of making improvements to truly redefine their cost structure, customer experience and position in the market. The organizations that start early on the AI path with an iterative mindset will be well-equipped. We’re looking forward to an exciting decade ahead.

As first published in Digital Insurance.

Insurtechs Are Pushing for Transparency

Over the past few weeks, I had the pleasure of meeting with a number of insurtech startups. Their mission? To create a customer-first company. One team is finding that customers believe insurance has more of a transparency challenge than a trust deficit – there is an increasing desire to know how their premium dollars are spent and how an insurance company views their risk.

Many of these meetings were held shortly after Evan Greenberg, CEO of Chubb, commented on broker commissions and fees in the commercial insurance market. Whether you agree with Greenberg’s comments, what really attracted customers’ attention is the lack of fee and commission transparency within the commercial insurance market. Furthermore, many insurtech articles stress that, for a long time, brokers have been able to capitalize on the industry’s lack of access and transparency. These articles rarely highlight existing customer rights, nor do they articulate how commissions and fees have evolved in the commercial insurance industry.

See also: More Transparency Needed on Premiums  

Regulations in a number of jurisdictions make clear that insurance buyers are entitled to request the actual level of commission and fees earned by their service provider. For those jurisdictions where customer rights are not as clear, any customer is still within his rights to request this information as he is paying for services and products.

For the past 20 years, brokers have shied away from having a frank dialogue with customers about the true costs of servicing a customer’s insurance program for fear of losing business to competitors. This fear of adequately charging for broker services, combined with decreasing standard policy commissions, led many brokers to consider alternative ways to make up revenue shortfalls. Increased commissions and fees from insurance companies provided the answer via traditional placements or the creation of broker facilities. Simultaneously, customer service has been redefined over time – many brokers now focus on reducing customer premiums as a way of evidencing value to customers.

This focus is not a true service, nor is it really reducing overall costs as broker commissions and fees are passed on to customers through insurance premiums. These increasing costs hurt an insurance company’s balance sheet. Just a quick reminder: A healthy balance sheet is required to pay claims!

Why does a healthy balance sheet matter? Have you ever experienced the insolvency of an insurer or reinsurer? Have you ever informed customers they may only receive five cents on the dollar for existing and future claims? Unfortunately, I had these experiences on a number of occasions during my early career as a claims manager — and I hope to never have the experience again! Fortunately, insolvencies are now rare events, due in part to the prudential regulatory regimes applicable to insurers, but that does not mean there is a bottomless commercial insurance company treasure chest for ever-increasing commissions and fees.

Can insurtech companies lead the way forward?

Marketing materials stress that insurtech startups are “customer-focused,” and their propositions are characterized by “convenience, on-demand, personalization and transparency.” For some of the startups, the company website and buying process stress that  “the business aims to provide transparency.”

Other startups list their fees on the company website and clearly evidence commissions on customer quotes. One insurtech broker has taken additional steps on the company website to 1) define profit commissions and 2) provide a schedule of profit commission schemes currently in place with insurance partners (none listed as of May 3, 2017).

This level of detail provides the customer with highlights of financial arrangements and improves financial transparency in the customer-broker-insurance company relationship.

The future of transparency?

Even though the insurtech industry has been progressing very swiftly, not every major insurtech startup is a roaring success.  SME customers can now compare commercial insurance products and services on offer, while improving their knowledge of products and service costs.

See also: Is Transparency the Answer in Healthcare?  

Commercial insurance brokers can lead transparency efforts by initiating frank conversations with customers about the true costs of products and customer-specific services and negotiate commissions and fees accordingly. However, as noted in my previous operations and product development articles, brokers, insurers and reinsurers must simultaneously review existing operations to create better efficiencies, reduce costs and improve customer services. These changes can be achieved through cutting-edge transformation programs, investment in new technologies or partnerships with insurtech companies.

Why is a simultaneous review important? Because customers are not only bearing the costs of current broker commissions and fees via premium payments, they are also bearing the high costs of supporting antiquated commercial insurance operations. Let’s improve all levels of service and transparency in the commercial insurance buying cycle and help customers make better informed decisions!