Tag Archives: claims

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.

Straight-Through Processing in 2021

Straight-through processing (STP) is becoming more common in insurance underwriting and claims, especially in personal lines, individual life and small commercial. Adoption rates in more complex lines and in claims processes remain relatively low, so STP is by no means universal across the industry. However, it’s likely to remain a priority for insurers seeking to improve the ease of doing business for their distribution partners and create more convenient customer experiences for their policyholders.

Generally speaking, STP refers to the ability for insurer systems to automatically process transactions without manual intervention or input. The system ingests data digitally and completes the transaction based on decisions governed by algorithms, including predictive models and simple business rules. STP offers insurers benefits in speed, consistency, productivity and application throughput while making the customer experience quicker and more convenient.

Technological Factors

Technological improvements over the past two decades have been a major factor in the rise of STP. The most fundamental of these has been the internet, which has both enabled the connectivity underlying STP as well as shaped consumer expectations for user experience and speed.

More recently, modern insurance core systems with the ability to automatically adjudicate applications based on configured business rules, combined with modeling capabilities that can capture underwriting factors and predict outcomes, have made it possible to process applications without human oversight.

Insurers also now have access to a wealth of high-quality, third-party data, which can enable pre-fill, eliminate unnecessary questions or inform insurers of potential risk factors. AI and machine learning capabilities to refine algorithms and flag potential fraud have also contributed to insurers’ confidence in STP.

STP in Underwriting

STP in underwriting is most common in personal lines and individual life—lines that are under cost pressures and, increasingly, sold online. More than 80% of insurers selling these lines have at least some level of automated underwriting, and many personal lines insurers process straight through more than three-quarters of the time.

Large commercial and specialty lines typically don’t have high rates of STP, because these lines are generally sold through agents and brokers. Even where insurers have supported some level of automation (for example, via portals with rating components), most policies aren’t written straight through. Instead, insurers are focusing on distribution connectivity, which is itself a prerequisite for effective STP.

See also: The Digital Journey in Commercial Lines

Small commercial and workers’ compensation lines occupy a middle point. Many of these products still require manual underwriting, but they’re also seeing increasing direct sales activity, often directed at niche market segments. Carefully defining sales targets in this way allows insurers to facilitate STP for these lines, as they can design tailored products governed by specific business rules that rule out more complex risk scenarios.

STP in Claims and Digital Claims Payments

For most insurers, though, STP in claims is fairly uncommon. Nearly 60% of insurers have no STP in this area. On average, fewer than 10% of claims are processed straight through in any line. It’s most common in personal lines and (for payouts) in annuities.

Claims STP is likely to become more common, especially in personal lines and individual life, as insurers continue to improve their core system capabilities and as the availability and quality of third-party data improves. Where coverage limits are relatively low, insurers can increase their levels of automation to create faster and more convenient claims processes. 

Insurers have achieved more substantial automation in digital claims payments. A third to half of insurers process and deliver claims payments digitally (depending on the line of business), and 10% to 20% of insurers do so most of the time. Digital payments are likely to be a priority area for insurers. Since COVID-19 forced many insurers to send some workers into the office to print and mail checks, manual and paper processes of all kinds are under intense scrutiny.

The STP “Sweet Spot”

Generally, STP is most effective when four factors apply to a particular line of business or transaction:

  • Risks are well understood, which makes modeling easier
  • Data is easily accessible and generally reliable
  • Speed is at a premium to be competitive
  • Margins are thin, so productivity and throughput drive profitability

Figuring out where to enable STP isn’t always a question of looking at specific lines or products and determining whether these factors apply. Insurers can also use these principles to design new products, especially for direct distribution—for example, by defining the allowable risk profile for a particular product more narrowly so it’s limited to the cases that are most likely to be profitable.

The Future of STP

While the industry as a whole is trending toward greater automation, most insurance will never be completely straight through; there will always be some complex claims scenarios or unusual risks that will require human intervention and review. That itself is part of STP’s value, though: When technology handles the easy processes, humans have more capacity to focus on higher-value work.

See also: Insurance Outlook for 2021

Enabling STP has an upside for those human actors, as well. Investing in better data creates resources human underwriters can use, and better connectivity eases integration and improves ease of doing business for distribution partners.

Even just the process of implementing STP can have benefits. Creating the business rule framework or algorithm to adjudicate an application—or even figuring out if a particular process can be done straight through—requires insurers to examine their workflows, understand what really matters and justify what is done and why. That can lead to process and product improvements that wouldn’t have surfaced otherwise, as legacy mindsets can hide in all kinds of places.

For more on STP, please see Novarica’s recent report, Straight-Through Processing in Underwriting and Claims.

The Next Wave of Insurtech

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

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

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

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

Bringing Customer Service to the Next Level

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

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

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

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

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

See also: Insurtechs’ Role in Transformation

From Cumbersome to Swift

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

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

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

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

How COVID Alters Claims Patterns

Claims trends and risk exposures are likely to evolve in both the mid- and long-term as a result of the COVID-19 pandemic. With the reduction in economic activity during lockdown phases, traditional property and liability claims have been subdued, most notably in the aviation and cargo sector, but also in many other industries, with fewer accidents at work, on the roads and in public spaces, according to a new report COVID-19 – Changing Claims Patterns from Allianz Global Corporate & Specialty (AGCS).

Estimates vary, but the insurance industry is currently expected to pay claims related to the pandemic of as much as $110 billion in 2020, according to Lloyd’s. AGCS alone has reserved about €488 million ($571 million) for expected COVID-19 claims, especially for the cancellation of live events and the disruption of movie or film productions in the entertainment industry. 

Surged and subdued

We have seen claims in some lines of business, such as entertainment insurance, surge during COVID-19, while traditional property and liability claims have been subdued during lockdown periods. There is still the potential for claims to occur as factories and businesses restart after periods of hibernation, and given the longer development patterns for third-party claims in casualty lines.

Claims notifications from motor accidents, slips and falls or workplace injuries slowed as more people stayed at home, and with the temporary closure of many shops, airports and businesses during lockdowns across the world. AGCS also noticed a positive impact on U.S. claims settlement from the suspension of courts and trials. 

Some claimants and plaintiffs have been more open to negotiating settlements out of court rather than opting to wait a long time until their case is scheduled – a trend also highlighted in another recent AGCS publication on liability loss trends. In general, claims activity is likely to pick up again following resumption of economic activity.

Property/business interruption 

Property damage claims were not significantly affected by COVID-19, as loss drivers such as weather are not correlated. However, as production lines restart and ramp up, there is risk of machinery breakdown and damage and even fire and explosion. With fewer people potentially onsite, inspections and maintenance may be delayed or loss incidents such as a fire or escape of water may be noticed too late, increasing the severity of damage. 

COVID-19 has caused business closures and disruptions globally – which often may not be covered in the absence of physical damage as a trigger of coverage. However, the pandemic has affected the settlement of standard business interruption (BI) claims in different ways. On one hand, factories in hibernation will not produce large BI claims, as many manufacturers, their and suppliers either shut down or scale back production. When a U.S. automotive supplier was hit by a tornado in the spring, the resulting business interruption loss was lower than it would have been during normal operations. Conversely, containment measures during lockdowns can lead to longer and more costly disruptions as access restrictions prevent effective loss mitigation and prolong the reinstatement period, as a fire and explosion at a chemical plant in South Korea demonstrated. 

Liability and directors & officers (D&O) insurance

To date, AGCS has only seen a few liability claims that are related to COVID-19. However, liability claims are typically long-tail, with a lag in reporting, so general liability and workers’ compensation claims related to COVID-19 may yet materialize. A number of outbreaks of coronavirus have been linked to high-risk environments such as gyms, casinos, care homes, cruise ships or food/meat processing plants. 

A wave of insolvencies, as well as event-driven litigation, could be potential sources of D&O claims. To date, only a relatively small number of securities class action lawsuits related to COVID-19 have been filed in the U.S., including suits against cruise ship lines that suffered outbreaks. The pandemic could trigger further litigation against companies and their directors and officers, if it is perceived that boards failed to prepare adequately for a pandemic or prolonged periods of reduced income. 


The aviation industry has seen few claims directly related to the pandemic to date. In a small number of liability notifications, passengers have sued airlines for cancellations or disruptions. Slip and fall accidents at airports – traditionally one of the most frequent causes of aviation claims – have declined along with the massive reduction in global air traffic, which fell by a record 94% year-on-year in April 2020. 

See also: COVID-19 Sparks Revolution in Claims

Although a large proportion of the world’s airline fleet has been grounded, loss exposures do not just disappear. Instead they change and can create new risk accumulations. For example, grounded aircraft might be exposed to damage from hurricanes, tornados or hailstorms. The risk of shunting or ground incidents also increases and can result in costly claims.

Long-term claims trends 

COVID-19 is accelerating many trends such as a growing reliance on technology and rising awareness of the vulnerabilities of complex global supply chains. Going forward, many businesses are expected to review and de-risk their supply chains and build in more resilience. This could involve some reshoring of critical production areas because of disruption caused by the pandemic. Such a move would likely affect frequency of claims and the costs of any future business interruptions.

Meanwhile, the growth of home working means that companies may have lower property assets and fewer employees onsite in the future, but there would be corresponding changes in workers’ compensation and cyber risks. During the pandemic, cyber risk exposures have heightened, with reports of the number of ransomware and business email compromise attacks increasing. However, to date, AGCS has only seen a small number of cyber claims that are related to COVID-19. 

For additional insights, please visit COVID-19: Changing Claims Patterns.

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.