Tag Archives: clara analytics

Future of AI and ID Management

Identity management has been an obstacle for commercial insurance companies for a very long time. Many thought that problems would dissipate or at least become easier to correct by moving to digital systems, but, in reality, identity management has only grown more complex. It is obvious that we need a better way.

Now, there is fresh hope that identity management will become much easier to wrangle. Artificial intelligence (AI) is progressing rapidly, to the point where it could become a tremendous tool in identifying and cleaning up inaccurate data as well as linking the right providers to the correct claims.

Let’s take a step back and examine the key issues in identity management today to understand how AI could be used to shore up existing gaps and move the industry forward.

The Data Problem

First of all, by identity management, as it is applied to insurance, I am referring to a special case of entity resolution, i.e., the process of linking references to providers in claims, bills and other data to a single flesh-and-blood provider — the so called “single belly button.” This is facilitated by maintaining a dataset of the actual providers working all over the country, with their names, addresses, specialties and networks — essentially all the data associated with them for billing purposes. These “golden sets” also are available for attorneys in the claims space, functioning nearly the same way. Still, for the sake of clarity, I’ll focus on medical providers in this article. These lists are available through a handful of third-party vendors (and certainly some organizations have developed their own), and they must be constantly updated as the ground truth evolves.

See also: Intersection of AI and Cyber Insurance

The Missing Link

Currently, numerous different golden sets have varying degrees of accuracy and cleanliness. While this is certainly problematic, the real challenge in identity management is the linkage process itself. This is because much of the provider references in the claims, bills and other data can be considered stale or dirty.

There are myriad reasons for stale and dirty data. Doctors change their name through marriage or for other reasons; they move to other cities; they might add a specialty or change focus, Joe Smith might become Josephine Smith. All of these things and more make the process of linking these references to the correct provider very difficult. In many cases, today’s systems lack the ability to link references in claims to golden sets; instead, linking falls to claims representatives. One of the biggest identity management tasks remaining today is the ability to uniquely and accurately link a claim to the right provider with the correct billing information.

Many companies try to build their own link, but it has not been smooth sailing. Developing such functionality is an expensive, time-consuming, complex endeavor. Without clean, accurate, linked datasets, claims can go wildly off track. But there is hope.

AI Will Fill the Gap

AI has shown its effectiveness in improving claims operations processes, pulling out key insights to resolve claims quickly without attorney involvement. Now AI could be applied to solve the linkage problem as well.

AI systems that aggregate data from actual anonymized claims, bills and other data throughout the industry could be used to read massive volumes of data, recognize pattern and find the links between specific providers and claims. Systems could be trained to identify and update records, managing identities persistently and in real time.

Imagine just for a moment that you had a very high threshold of confidence in identifying the correct provider for a claim and that the provider automatically would be issued a unique ID (in the U.S., that of course is the National Provider Identifier, or NPI) that stays with him or her throughout the life of the claim so that every time a change is made — a note filed, a bill paid — the correct provider at the correct location automatically comes up. No detective work, no guesswork.

This is now possible from a technological standpoint, as we have seen in creating CLARA’s solution. I can attest that it requires a significant investment of time, effort and intellectual property to build in-house. Given the rate of AI advancement, market adoption and pressing industry need, there is no doubt that it won’t be long before nearly all identity management systems are powered by AI and machine learning technologies.

See also: Insurance Outlook for 2021

As I hope I have shown, the data available to the industry today is nowhere near sufficient. The bar for identity management — and therefore the level of investment, skill and innovation applied to this problem — will continue to increase. Those organizations that prepare to embrace new applications of AI for identity management will be the ones that thrive and modernize claims, driving down costs and increasing efficiency. The companies that resist this transformation will get left behind as they struggle to sift through their dirty, messy data.

As first published in Digital Insurance.

What’s Wrong With Commercial Auto?

Commercial auto is one of the biggest, most problematic cost centers in the insurance industry. Its loss ratios are incredibly high — and growing — particularly in comparison with other P&C lines. Carriers struggle to make money on the line, and they frequently incur fairly substantial losses.

With fewer claims than most other lines, why is commercial auto such a problem? Because claims are much larger; carriers may be looking at paying out $200,000 vs. $20,000 on different types.

Let’s examine the issues and see how we can turn commercial auto into a revenue generator instead of a black hole.

Commercial Auto by the Numbers

According to the National Association of Insurance Commissioners, commercial auto represents $39 billion in earned premiums. Yet, it pays out approximately $28 billion in losses, which excludes all of the internal administrative costs. Some folks in the industry thought that losses might go down this year due to fewer cars on the road, given COVID-19, but this has not been the case. One large carrier recently shared with me that they’ve seen a similar level of claims (both in terms of frequency and severity) because many commercial trucking organizations are working nonstop to keep the supply chain going.

If even a pandemic won’t lower the loss ratio, are we doomed? Let’s break this down a bit further.

Of the $28 billion in losses, roughly 60% went to medical costs associated with bodily injury. There is not much that can be done here from a carrier standpoint, short of offering slightly lower premiums for companies that incorporate the strictest safety standards and tools like electronic logging devices. Even this technology can be a double-edged sword given that the more advanced vehicles tend to be more expensive to fix in the event of an accident. While measures can sometimes minimize the severity of injury, accidents still happen.

Carriers have seen an uptick in accident rates involving bodily injury or death over the past 10 years, in part because more commercial vehicles are on the road traveling more miles each year. Then there is the role of drivers themselves. The trucking industry has a remarkably high turnover rate. For example, in the third quarter of 2019, large truckload carriers’ turnover rate increased to an annualized rate of 96%, according to Trucker.com. As companies basically overhaul their entire workforce each year, many don’t invest the resources into in-depth training programs, leading to less experienced, more accident-prone commercial drivers on the road.

Driver issues withstanding, the non-medical costs still leave $11.6 billion in losses. Where does it go? Very simple: Legal costs account for 40% of losses; the system is broken.

Losing Battle

Litigation tends to happen when claims are not resolved quickly or when something is perceived as unfair. While such concern is perhaps understandable, in practice it is not so innocent. A growing body of attorneys are ready to sign on to “help” plaintiffs, initiating cases that should never be filed, and they have become quite proficient at securing huge settlements.

The lawyers who specialize in auto claims know exactly what to look for and can be quite convincing in wooing potential clients and later in threatening the carriers with which they are attempting to negotiate. For example, the lawyers might spot something not related to the specific incident but that could be tied to the company. A savvy attorney would tack on additional charges, such as negligence, on top of bodily injury, pain and suffering. Tactics like these drive settlements higher. Complicating matters, states have very different statutes on bad faith suits filed by attorneys. Some states are more prone to settling cases early, and, as a result, lawyers in these areas increase the number of suits they file — which increases the cost for carriers.

One thing that many insurance firms and self-insured enterprises are just realizing is that plaintiff attorneys are rapidly becoming data-aware and using that awareness in a highly sophisticated and strategic way. Once upon a time, plaintiff attorneys were good at qualifying clients that they had a high degree of confidence could return them a large settlement. While that’s still the case, in the last half decade or so, even moderately sophisticated plaintiff firms have compiled significant datasets on enterprises and insurance companies, in many cases down to the general actions taken at an adjuster level. They use this data to plan their litigation strategies, select the most effective partner and manage each step of the process in an intelligent way. The result is that carriers and self-insured enterprises that do not have similar data-savvy practices are essentially being bled dry because they have nothing to counter this advantage.

See also: The Digital Journey in Commercial Lines

Attorneys are more than willing to try their hand in court, and juries can be quite sympathetic to plaintiffs they feel were wronged by a big company. As a result, there has been a certain degree of social inflation, as jury awards can rise astronomically if for no other reason than a desire to help the little guy fight back against the “evil” corporation — and winning verdicts keep going up. According to Shaub, Ahmuty, Citrin & Spratt, the median of the top 50 single-plaintiff bodily injury verdicts in the U.S. nearly doubled from 2014 to 2018 (moving from $27.7 million to $54.3 million).

As it stands today, there is tremendous variability in jury awards, just as there is with out-of-court settlements. Looking across claims, there might be very little difference in the facts of the case, yet one plaintiff walks away with millions while another receives a much smaller verdict. Carriers are often unwilling to risk the chance of coming out on the wrong side, hence agreeing to a settlement that may be uncalled for.

It is clear that litigation is the most significant hurdle to better loss ratios across the commercial auto line. If we can reduce, standardize or eliminate costs associated with litigation, the industry would be in a much better position.

A Process Evolved

New technologies, artificial intelligence (AI) and machine learning, in particular, can help. For example, you may want to know the likelihood of attorney involvement based on several claim factors, or you want to know which attorney is involved and what kind of settlements he or she negotiates for similar claims. But most importantly, you want to know what actions to take to prevent attorney involvement. AI and machine learning applications are emerging that can identify claims early in their life cycle that need the most attention.

Imagine how powerful it would be if you had an application that inherently understood the intricacies of commercial claims — one that would warn you of claims that were in danger of slipping to an attorney. Solutions are hitting the market that leverage capabilities like natural language processing and deep learning techniques to analyze hundreds of data points hidden within claims. They now can tap into structured data as well as the really interesting unstructured data, like notes or police reports, as well as decoding the sentiment of claimants. This collection of data provides pretty telling clues as to how a claim might progress.

An adjuster could get an alert about aspects of the claim that are troublesome, access to detailed attorney scores and ratings in case the claim escalates and, if need be, the optimal time to settle for a favorable outcome. With this information, the adjuster could take immediate action to head off the problem.

As applications get smarter, they will be able to determine what a claim settlement should look like and why with a much higher degree of certainty based on similar claims. This can be instrumental in the adjuster’s or defense counsel’s ability to negotiate with the claimant’s counsel. Armed with this kind of hard data, the organization could walk into settlement negotiations in a much stronger position and begin to counter the formattable data advantages that plaintiff attorneys have been amassing.

AI and machine learning systems also help organizations close claims faster, and, in doing so, relieve some of claims management teams’ administrative burdens. Additionally, by closing claims quickly and fairly, claimants receive settlements faster, return to their everyday lives sooner and thus generally wind up in a better financial position without ever involving an attorney.

Considering the high loss ratios of commercial auto insurance today and the propensity for them to increase further, emergent AI-based applications are our best hope for improving profit margins and repairing the commercial auto line.

As first published in Claims Journal.

How AI Will Define Insurance Workforce

Prior to COVID-19, the U.S. boasted historically low unemployment and a roaring economy. Nearly every industry was expected to face a severe talent shortage within the next 10 to 20 years. But then March hit, and the world turned upside-down.

Since then, the pendulum has swung in the other direction. The current unemployment figures are reporting as many as 10.7 million people are out of work, and, despite this sudden abundance of available workers, staffing issues remain — they’ve just become more complex.

To navigate this wildly fluctuating environment, companies will rely on data for decision-making about hiring, training and countless other matters that affect the bottom line. This will require tools, like artificial intelligence (AI), to make sense of data and to adjust quickly amid uncertainty.

The best way to examine AI’s value in today’s uncertain world is to look at how it can work within a specific industry. Doing so makes it possible to show practical applications from which lessons can then be applied to other industries.

Commercial Insurance: A Case Study

Like other industries, commercial insurance faced a significant hiring crisis pre-COVID-19. The average claims adjuster remained in the industry for just four years — about the time it takes to gain full expertise — and those workers who stuck with claims have inched closer to retirement. So, this multibillion-dollar market is at risk of losing much of the human brain trust that enables current systems to run, as new workers cannot be hired, trained and retained fast enough to balance the scales.

Fast forward to today. Commercial insurance looks markedly different. The types and volumes of claims are changing. For example, claims related to COVID-19 contact or work-from-home circumstances are rising quickly, as are post-termination claims, while traditional claims have dropped.

At the same time, access to traditional healthcare has been in flux. To combat the limitations on available providers, telehealth solutions have exploded, opening up a whole new set of providers that claims reps need to become somewhat familiar with to facilitate claims accordingly — claims that bear a greater potential for fraud and litigation, which cost companies millions of dollars each year.

In short, almost everything about claims operations has changed — and, like many other industries that have been traditionally slow to adapt to new challenges, commercial insurance faces real hurdles.

The Importance of Data and Intelligence

Data is the key to overcoming dramatic changes within a relatively static industry. Maintaining a pulse on what’s happening across a business, or with a specific claim, and how it relates to things experienced previously is important; spotting trends early is vital. Organizations require data to determine if their plans and practices are working — and, if they are not, data should be used to drive intervention and adaptation.

But thousands to millions of data points alone won’t save the day if an organization doesn’t have the capability to understand what the data is telling them. What is the context? How are points connected? If a trend continues, what will be the effects six months or two years from now?

AI systems unlock the meaning of data to make it useful, pinpointing where organizations need to make adjustments. In commercial insurance, AI could allow for expanding provider networks to offer better, faster access to care. To actually expand networks using quality providers, systems need to tap into more data to learn which providers have achieved the best outcomes on which types of cases.

What is particularly exciting about implementing AI in this rapidly changing environment is that interpretations of data are not fixed. Machine learning capabilities are constantly refining and updating insights so that organizations — and their people — can respond accordingly.

See also: How AI Transforms Risk Engineering

Designing the Future Workforce

So, if data analytics and AI become staples in modern business, how do they solve the human resource problem? What do they mean for the future workforce? The answer is threefold.

Data determines what your hiring needs actually are: In a world that is changing so quickly, your business might not need as many people specialized in a certain area, whereas new opportunities or divisions may emerge. Your business may be forced to alter its offerings to match customer needs. Data is the guide; it lets you home in on exactly what skills are required.

AI guides training: Because AI is able to analyze so much data so quickly, new hires are able to access the information and prompts they need to do their jobs well as soon as they need it. There is not as much feeling around or dependency on senior colleagues. This is not to discount the value of experience, but it means that workers can reach a competent level much faster; what they lack in experience and intuition is replaced by data-driven insights and standardized practices.

AI augments jobs: AI solutions take care of many of the rote tasks workers are routinely bogged down with today. As a result, employees can focus on making more efficient, informed decisions; they can actually use their brains more. AI flags potential errors or problems so that they can be addressed before they escalate. Reps can focus on delivering compassion at a time when people need it most.

While COVID-19 has fundamentally altered the future workforce, tools like AI help get it back on track. In leveraging it effectively, organizations will become nimbler and more responsive to conditions while employees are more knowledgeable and effective.

Case Study on Using AI in Workers’ Comp

Australia is home to a well-developed workers’ compensation system. Each state determines the design of its scheme, with some being privately underwritten by insurers and others being state-run. Claims across territories vary by industry, injury and complexity. As such, insurers need systems that can enable quality, efficient handling of claims to facilitate the health of injured parties and can get them back to work as quickly as possible.

Approximately three years ago, QBE’s Australia Pacific division, like many other insurers, was running what we would describe as a “process-compliant business” when it came to workers’ comp claims. Leadership wanted to do more to eliminate manual processes and take advantage of claims adjusters’ expertise to get the best result for customers and their employees. They knew technology was the key.

Three Core Issues

QBE had long valued the principle of getting the right claim to the right adjuster based on areas of expertise. But to spot complexities early, claims teams engaged in what I refer to as our manual triage system. Expert adjusters did a cursory look at claims as soon as they were lodged, to identify potential risks based on very simple criteria — in particular, was the employee missing work? Simply put, we needed a better way to get claims routed and assessed from the earliest stages.

Our leadership team also wanted to figure out how to lighten adjuster caseload. As is common across the industry, adjusters may handle as many as 70 to 80 claims at a time. With this volume, it was incredibly difficult to spot the more complex or problematic claims, the ones that require the most attention. QBE was seeking a tool that could surface this information quickly and easily.

Additionally, the team was committed to identifying a better way to conduct quality reviews. Instead of manually selecting which claims to examine, which is very time-consuming, we wanted to add artificial intelligence to the mix.

AI Intrigue

As QBE prepared to set its strategic initiatives for the next few years, data analytics was prioritized. With more detailed information, adjusters and leadership could make better decisions about how to route claims, what required attention and how to ensure efficient, positive resolution.

We considered building a solution in-house but quickly realized that it would take a considerable amount of time and staff resources to construct a system that mapped to our priorities. We started engaging with many of the big data and analytics consultancies, hopeful that they would be able to help. They didn’t fit the bill, either.

See also: COVID-19’s Impact on Delivery of Care

In the summer of 2017, I ran across an article about how CLARA Analytics applied machine learning to workers’ comp claims. The approach, which leveraged artificial intelligence (AI) to identify claim issues and keep them from escalating while helping to close simple claims faster, made sense. As I examined how the models worked and how the software visualizes workload allocation, I recognized that it was the way we wanted to run our business and that CLARA had a sizeable lead over what QBE could build internally.

Clear Benefits

Once we started to get past people’s reluctance to use AI, they began to understand how an AI system could make their jobs easier — the models not only saved countless hours of manual work but their accuracy made decision-making significantly easier.

The financial benefits associated with an adoption of such software have been significant. The initial reports estimate that product integration will easily deliver a 5:1 return on investment, and that could turn out to be conservative, given that the savings will extend across QBE’s entire workers’ comp portfolio.

QBE has been able to implement a more focused approach to quality assurance. Gone are the random selections of claims. Instead, we take the lead from this new system, which provides a much higher level of confidence that the review team is looking into the claims that need it most.

We believe that quality assurance shouldn’t be driven by art; it should be driven by analytics, which is exactly what we’ve been able to accomplish.

In addition to the new-found efficiencies and claim insights, we have enjoyed the competitive differentiation provided to our sales team. They love being able to showcase how QBE uses industry-leading technology to improve claims operations at multiple levels.

See also: An AI Road Map to the Future of Insurance

Continuing Collaboration

Our partnership has allowed us to enhance the software’s capabilities to create significant advancements for our industry. For example, several months ago, both QBE and CLARA started collecting perception data from each injured person’s claim, such as how they feel about their recovery. Today, we are able to collect and analyze that information at scale.

People have been talking about psychosocial flags for injury recovery for more than 20 years, and no one has solved the problem. But taking in extra data points and using them in a different way or thinking about a problem from another perspective has let us make better decisions about how to route claims, what required attention and how to ensure an efficient, positive resolution.

AI Ends Guesswork in Uncertain World

It’s been a tumultuous year. In just the span of a few weeks, COVID-19 emerged unexpectedly and abruptly altered almost every corner of the commercial insurance space. Stock market and GDP forecasts have whipsawed as economists and investors have tried to make sense of frequently shifting news. Now we’re in a contentious and unpredictable election cycle.

Divining the future is always a challenge, but lately it’s become especially difficult. During periods of intense change, traditional patterns and precedents lose their predictive power. Regression-style tools that provide data extrapolations become a useless blur. The average workers’ comp claim duration of 2019, for example, will look very different than it will in 2020. Litigation and fraud may emerge in new forms, with most new types passing undetected by screens developed from prior data.

One approach that can help companies navigate the uncertainty is artificial intelligence (AI), which is highly sensitive to new data and tends to react immediately, creating a dynamically updated vision of the future. While much of the world has been focused on COVID-19 and the related economic challenges, the underlying technologies behind AI have continued to accelerate in speed, efficiency and predictive accuracy. For organizations looking to become more resilient, it’s an ideal time to consider integrating machine learning, natural language processing and other AI techniques into their operations.

While the promise of AI is great, so, too, is the hype. As a result, many people have a misconception of what AI actually is — and what it is not. Let’s take a look at how it really functions, what it can and cannot do and how it can help future-proof commercial insurance.

Two Types

AI is typically viewed in two fundamentally different ways. There is the futuristic “AI-is-taking-over” version (think Skynet or similar concepts brought to life by Hollywood). This form understandably makes people a little nervous that machines will grow to dominate society (or at least replace jobs at a time when we’re already seeing unemployment lines expand).

Then there is a more prosaic version in which AI complements what humans do. Think of how Google automatically surfaces structured answers to questions you type in or how Amazon knows which product you might want to buy next. In these cases, AI extends your capabilities while leaving you in the driver’s seat. It is this more practical version that will get organizations and teams excited about modern AI-based applications and is the game-changing application in the commercial insurance space.

AI that augments human capability is especially valuable in businesses like insurance, where there is simply too much data coming in quickly for people to keep up. Image and language processing can be applied to the dozens of structured documents typically associated with a claim but can also be used to interpret unstructured information, such as handwritten doctors’ notes. Often, this approach finds important information — diagnostic codes that were considered, for example, but not officially associated with the case. Subtle cues can be detected across a wide range of files to create insights that would otherwise go unnoticed. Alerts bring those insights to adjusters’ attention, helping them take prompt action that can make all the difference in a claim.

In this way, AI becomes a kind of superpower for the adjuster. It helps adjusters see through the clutter and make decisions with speed, precision and scale. This helps adjusters become more productive and better able to focus on the claims that matter most. It also frees them up to handle the types of challenges that humans are uniquely suited for, such as detecting the hidden concern in a claimant’s words or enabling them to feel listened to during a challenging period.

See also: 3 Practical Uses for AI in Risk Management

Innovate Now to Secure the Future

AI can end up reshaping not just a single claim but how a business is managed. Claims leaders can now use it to optimize organizational practices, team performance and even partner networks. AI can score healthcare providers, for example, so carriers can direct claimants to highly rated doctors and even identify new ones to bring into the network. Carriers can also use AI to evaluate the effectiveness of their attorney panels based on specific outcomes. These are just a few examples of substantial business decisions that can now be driven by data and intelligence.

Despite the complexities and considerable challenges brought about by COVID-19 and other events this year, the insurance industry sits at a breakthrough moment. New uses for AI such as those highlighted above will continue to be identified and implemented, resulting not only in more efficient operations and empowered employees but also in better, faster, more valuable service to claimants.

As first published in Datanami.