Blockchain innovation is gradually taking hold in the insurance sector in an unprecedented way. Insurance giants and startups worldwide are attempting to use blockchain-based solutions to deal with operational inefficiencies of the insurance industry.

While digitization and technology have always existed in the insurance sector, blockchain innovation has emerged as rocket fuel for transformative change. In fact, behavioral shifts induced by COVID-19 have unfurled a promising opportunity for the insurance industry to upskill and adopt digital technologies like blockchain.

Blockchain technology has a lot to offer to both insurers and their insureds in different segments, from claims management and fraud detection to reinsurance and peer-to-peer insurance. Acting as a digital hub for all corporate transactions and data streams, blockchain innovation can reduce a lot of paperwork that worsens the customer experience.

Meanwhile, the decentralized nature of blockchain technology allows customers to share data securely and on a confidential basis. This further enables insurers to offer customer-centric insurance products and efficient services that are more streamlined and deliver greater value to clients.

Although there is limitless potential of blockchain technology in the insurance sector, the widespread adoption requires insurers to overcome significant legal and regulatory hurdles.

See also: The Future of Blockchain Series Episode 3

Moreover, the limitations of this technology in terms of standardization, scalability and security need to be addressed by insurers before taking any concrete action.

All in all, the industry-wide implementation of blockchain requires facilitating collaboration between insurers and technology leaders and shaping an encouraging regulatory environment. It is also crucial for industry players to closely follow the breakthroughs in blockchain technology and evaluate where the most promising blockchain application areas exist.

Artificial intelligence (AI) has an immediate potential to make the insurance industry more profitable. It can cut down on inaccurate claims, reduce operating costs, help to underwrite more accurately and improve the customer experience. Yet, there are legitimate concerns about how the technology may affect the industry. This blog explores some of the most common and how an ethical AI framework will help.

People are scared they will lose their jobs

As with all major digital transformations over the last 20 to 30 years, employees fear that the technology will replace them. In insurance, employees often spend 80% of their time doing administrative tasks like manual data entry and reviewing documents. Allowing AI systems to automate low-value administrative work frees employees to be far more productive and valuable. This in turn reduces operating costs, increases profit, delivers better customer engagement and increases the value of the employees themselves.

And that’s just the tip of the iceberg when it comes to the added value of AI. In the commercial property sector, using satellite- and IoT-enabled AI technology to build near real-time digital twins of risks from over 300 datasets helps insurers and customers measure, manage and mitigate risks. They can reduce claims and losses, reduce business interruption and write more profitable business.

When I talk to insurers and show them how AI platforms can work, they understand its potential right away. So do their employees. While many people in the industry may worry that AI could take away their job, the reality is almost exactly the opposite. 

In the U.K., from 2016-2020, the insurance sector underwrote over £50 billion of commercial insurance policies and yet lost £4.7 billion on this underwriting. AI and digital twins can help insurers to deliver profitable underwriting.

Inaccurate or outdated training data leads to ethical concern

But today’s AI models and algorithms are built using training data that is often old and inaccurate. For instance, more densely populated areas often report more crime due to the number of people in the area. Therefore, AI models could predict these areas to have more crimes in the future, even though the crime per capita is often no higher in densely populated areas than in less populated ones.

In addition, most reported crime does not have an exact location of the incident, so the police station where it was reported is often put down as the crime location. If you live close to a police station, your home may be seen as being at higher risk of crime even though properties close to a police station are actually far less likely to be burgled.

In both of these cases, AI models built on this data could discriminate and say a property is at higher risk of crime than it is in reality, unfairly boosting insurance costs.

These examples show how important it is that data providers and insurers understand the existing bias in data. That way we can ensure we do not accentuate these biases in future AI models.

See also: Designing a Digital Insurance Ecosystem

Lack of transparency

Some people don’t trust AI because it’s new and they don’t understand it. AI is seen, to some degree, as Big Brother. When I attend conferences on ethics in AI, people invariably talk about how social media is using AI in potentially harmful ways.

However, when I work with insurers, local government and businesses, they see that, as long as they start with an ethical framework, AI can help them to much better serve the wider community and customers as well as doing right by their employees. 

Communication is key here, about what an ethical framework entails as well as how decisions are made. Citizens must be able to understand the AI-enabled decisions that affect them, and the industry must stand ready to give them access to that information. The more people understand, the better for all of us.  

The building blocks of a new ethical AI framework

An ethical AI framework benefits us all, from customers to insurers to data providers. That’s why Intelligent AI has been working for the last year with the U.K. government’s Digital Catapult to develop an ethical AI framework specifically for our insurance platform. 

With proper education, acknowledgment of the potential flaws in existing data and a transparent way for customers and communities to request details of how AI decisions are made that affect them, AI will be understood and embraced far more quickly. 

The sooner customers accept AI, the sooner they and the insurance industry can reap the rewards of the far more accurate data, pricing and claims information that AI brings. 

Insurance should be about helping customer to manage and mitigate risk. However, today, too much time is spent on administration and not enough time is left to reduce risk and help clients with business continuity (especially as we recover from the COVID pandemic). AI has huge potential in lowering costs and increasing customer service as long as we implement it with an ethical framework.

The traditionally stable P&C personal lines insurance industry has been shaken up due to the pandemic. Insurers have been used to adjusting to economic swings and catastrophes that affect volumes and financials, but the dramatic changes in customer behaviors and patterns fomented by the pandemic and related lockdowns are new experiences for the industry. Driving miles, personal travel, work-from-home mandates and online ordering and delivery of food and goods combined to create new challenges for carriers. Companies have had to rethink their technology plans because of seismic shifts in claims volumes, support for agents and employees working remotely and customer demands and expectations for digital interactions.

SMA has conducted a series of market pulse polls throughout the pandemic, starting in April 2020, as the initial economic lockdowns and work-from-home mandates began to take hold. Our latest survey, conducted this May, assessed how plans are changing and being reshaped as personal lines insurers move forward in the second half of 2021 and formulate strategies for early 2022. Whether we are moving toward a post-pandemic era in the U.S. is debatable, but tech-related plans are moving ahead full steam.

We have observed that after the initial tactical responses in the early stages of the pandemic, there was a great deal of rethinking, reprioritization and new activity related to technology plans. The initial wave in the spring of 2020 focused on enabling a remote workforce and providing critical digital capabilities to agents and policyholders, not to mention the significant rebates provided to policyholders. But when the fall arrived, the technology road map became the subject of major review. Many carriers stayed the course – continuing some of the shifts they initiated in the second quarter. However, some also stopped or paused projects (about one in eight insurers), and some of the more innovation-related activities were pushed out to 2022. But gaps were exposed in areas like first-notice-of-loss and payments, and the need to make these areas digital pushed them to the top of the list. Personal lines companies have been busily implementing or enhancing these capabilities, as well as the general self-service capabilities.

See also: Personal Lines Channel Plans

Fast forward to the middle of 2021. What are insurers planning for the second half and into 2022? SMA's most recent survey shows that virtually no companies plan to retrench or pause project activity in the second half. But there is a significant amount of rethinking for road maps and project reprioritizations, with almost 60% saying they are reshaping or accelerating plans. The flurry of activity signals an acceleration in digital transformation. And it is also a signal that the next couple of years are likely to see a further leap forward in the industry's overall transformation. So, fasten your seatbelts and get ready for the road ahead.

For more information on commercial lines plans for technology, see our recent research report, "Personal Lines Digital Plans and the Post-Pandemic World: SMA Market Pulse Insights." The report also includes a view of insurers' expectations for the workforce come January 2022.

This will be a quick note this week because I've been on vacation with family and then moved my younger daughter out of her apartment now that she's finished law school and taken the bar exam, but I still hope to leave you with something to ponder.

That would be the quarterly Willis Towers Watson report on insurtech, which is always interesting but which I think is especially important now because insurtech seems to be moving into a new phase.

As you can see in detail in the report, the winners and losers of the first big wave of insurtech seem to have become clear. Nearly 200 insurtechs have gone out of business, while a few big winners such as Lemonade have emerged.

The maturing of the sector has pushed startups to go beyond a focus just on a technology -- "We're powered by AI... or blockchain ... or whatever" -- and to zero in on solving a real, specific problem for established companies. That switch to pragmatism is a good thing.

And much more is coming: Insurtechs raised more money in the first half of 2021 than they did in all of 2020.

The money flooding into startups has raised concerns that valuations may be too high and that much of the money will be wasted. I've certainly seen that happen before. During the first internet boom, in the late 1990s, I saw lots of smart investor friends convince themselves that they should avoid selling their startups for just a few months more, because prices kept soaring -- only to lose big when the bubble burst. The one friend who really made out saw the trouble coming and liquidated his fund ahead of it.

I'm not predicting that insurtech will suffer the sort of cataclysm that affected internet stocks in 2000, but I do think this is a time to be thoughtful. There are certainly loads of opportunities, but they'll look different than the first round did, and there may well need to be a resetting of expectations and valuations. So, if you're at all interested in the insurtech phenomenon, I'd suggest curling up with the Willis Towers Watson report for a while.

More next week.

Cheers,

Paul

Mental health disorders have emerged as a potentially significant factor in workers' compensation. This article focuses on potential impacts that post-traumatic stress disorder (PTSD), a mental health condition that some people develop after they experience a shocking, scary, or dangerous event, may have on WC system costs.

In recent years, there has been an increase in legislative activity across the country to broaden or establish eligibility for WC benefits for PTSD. The momentum of such measures has grown, and could be accelerated somewhat, given the spotlight the COVID-19 pandemic has cast on the potential for workers to contract PTSD from their employment—particularly in the healthcare field. In this paper, we review examples of WC benefit eligibility criteria for PTSD, available data and the potential impact of PTSD on overall WC costs, including possible effects stemming from the pandemic and other stressors.

How Do WC Benefit Eligibility Criteria for PTSD Vary by State?

There is much variation in WC benefit eligibility requirements for PTSD between states. The first question we reviewed is whether PTSD—without a corresponding physical injury—is explicitly addressed as being a compensable injury or occupational disease under WC statutes.

• Statutes may generally address WC for a mental injury or illness in a variety of ways, explicitly exclude mental injuries not accompanied by physical injuries (a mental injury or disability that arises without a physical injury is also known as a “mental-mental” injury) or limit mental-mental injuries to certain situations such as crimes of violence.
• If PTSD is compensable as a covered injury or occupational disease under a state’s WC statutes, it may be compensable for all occupations or only for some subset of occupations such as “first responders” (e.g., law enforcement officers, firefighters and emergency medical technicians).
• In the case of first responders, statutes typically reference the PTSD subset of mental injuries.

If a state provides WC benefits for PTSD without a physical injury, what conditions need to be satisfied for it to be a compensable claim?

• Must the condition result from a specific incident, or can it arise over time as a result of cumulative exposure to several events? (When the condition is brought about by repeated traumatic events, it may also be referred to as continuous traumatic stress disorder, or CTSD.)
• Must the nature of the traumatic event preceding the PTSD be extraordinary in relation to the average worker’s job experiences, or in relation to other employees in the same occupation?
• What is the burden of proof required to establish eligibility for WC benefits? For example, must PTSD be proven with clear and convincing evidence or with a preponderance of evidence? Is PTSD associated with a presumption that it arose out of and in the course and scope of employment?
• What type of healthcare professional may make the diagnosis, and what standards can they use?

Some states have legislative proposals that consider establishing or expanding eligibility for WC benefits resulting from PTSD to first responders or a broader group of occupations. Some others have considered increasing coverage to all occupations under certain conditions. Several states where mental injuries may already be eligible for WC benefits have introduced legislation to provide a presumption that PTSD in first responders occurred in the course of employment.

In jurisdictions where NCCI provides ratemaking services, first responder classes only account for approximately 1.6% of privately insured costs, with most states ranging from 0.5% to 3.0%. Therefore, legislation focusing on first responders may have a relatively small impact on overall privately insured WC costs. But it may have a significant impact for the affected classifications. On the other hand, legislation affecting all classes could have a significant statewide impact.

What WC Data Is Available on PTSD?

WC data on PTSD is limited due to both the relative scarcity of PTSD claim data, in general, and limited data reporting for first responder employer groups. If legislation is enacted making PTSD more readily compensable under WC, the subsequently reported additional data could be used in future PTSD cost impact analyses. Until a significant volume of credible WC PTSD data becomes available, non-WC data may be leveraged.

What Are the Incidence Rates of PTSD?

We can get a general idea of PTSD frequency in the workplace by analyzing incidence rates from published PTSD studies and meta-studies of the general population, first responders, veterans and those in other occupations.

For the general population, a 2016 epidemiological study indicated that 6.1% of American adults experienced PTSD in their lifetime, while 4.7% had it as a condition during the most recent year. The presence of PTSD in the “most recent” year does not indicate when the event or events occurred that led to the condition. So, care must be taken if these prevalence rates are extrapolated to estimate annual WC claim frequency, which is the likelihood that a worker will file a PTSD claim in a particular year and that the claim is accepted.

See also: Workers Comp Trends for Technology in 2021

There have been numerous PTSD studies on first responders. One worldwide meta-analysis estimates a 10% average PTSD rate for first responders overall, where the average rates vary by occupation.

Care is needed when comparing the results of studies that were conducted in varying locations and time periods, and that use differing methods to identify and analyze the incidence of PTSD. Further, it is possible that reported mental illness from traumatic events in the workplace may be increasing relative to levels reflected in past studies.

What About the COVID-19 Pandemic?

While some employees have been working from home, many employees classified as “essential workers” have continued to work at their usual place of employment. This latter group may have faced a higher risk of contracting COVID-19 and possibly experienced more fears and anxiety about becoming infected themselves or infecting their loved ones. In particular, early data suggests that first responders and healthcare workers may have been disproportionately affected by COVID-19—accounting for almost 75% of all COVID-19 claims reported to NCCI as of year-end 2020. Some of these workers have provided direct care to people with the virus, often in overburdened areas. As a result, they may experience significant physical and psychological strain. For example, a sample of 571 frontline workers in the Rocky Mountain region surveyed between April and May 2020 showed 15% to 30% reported traumatic stress.

The myriad of issues faced by first responders and those on the front lines of the pandemic have been well-documented. For example, one writer opines: “Over the last year, there has been the psychological trauma of overworked intensive care doctors forced to ration care, the crushing sense of guilt for nurses who unknowingly infected patients or family members and the struggles of medical personnel who survived COVID-19 but are still hobbled by the fatigue and brain fog that hamper their ability to work.” Extended periods of stressful conditions resulting from the COVID-19 pandemic could mean that these workers may suffer trauma not necessarily from a specific extraordinary event, but perhaps from continuous stress.

Mental health was reported as a significant concern for healthcare workers before the COVID-19 pandemic, and COVID-19 appears to have increased the stress, with 37% reporting “mental health issues” in 2019 versus 41% in 2020. And 58% of healthcare workers say mental health issues have affected their work more since the COVID-19 pandemic began. If any resulting emotional impact persists, it could increase the prevalence of PTSD and CTSD among healthcare workers after the pandemic.

Some other workers, such as nursing home staff, have also disproportionately suffered from COVID-19-related stress. One survey taken between May and June 2020 revealed some of the same adverse conditions for these workers as noted above for healthcare workers.

At this writing, it remains to be seen how COVID-19 will affect the propensity to file PTSD claims in those states that have chosen or will choose to make PTSD or other mental-mental claims eligible for WC benefits.

What About Other Stressors?

The COVID-19 pandemic is an example of a potential large-scale stressor on the WC system because it may contribute to an influx of claims involving not only physical injuries but also mental trauma. However, this is not the only potential source of escalation in mental trauma claims. Other types of events could have similar impacts at a state or local level, such as shootings, natural disasters and domestic terrorism.

Events involving mass injuries or casualties could lead to spikes in reported PTSD cases, which would present a challenge when projecting future PTSD incidence rates.

Understanding How PTSD May Affect WC System Costs

In all situations, one must account for how state law treats physical-mental, mental-physical and mental-mental events when assessing their potential impact on WC system costs.

When analyzing how proposed PTSD legislation may affect a state’s WC system, one must first consider issues related to estimating the number of compensable claims, including:

• Nuances in current and proposed WC statutes—What are the eligibility requirements, and how are they changing?
• Studies of PTSD prevalence rates—Are the prevalence rates for the lifetime or a recent period? What were the exact occupations of the participants in these studies? What DSM (American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders) standard was used? Did the study use clinical reporting or self-reporting? If the latter, what cutoff point of scores was used to determine the presence of PTSD? Were the participants selected at random or by convenience? What was the sample size? When was the study conducted? What country did the participants live in?
• Other factors that might cause actual claim frequency to be different than otherwise expected—Will frequency be lower due to any potential perceived stigma associated with reporting a mental injury claim? Conversely, will frequency be higher if there are financial incentives for being diagnosed with PTSD?

WC benefits available to workers with PTSD may vary significantly by state and by occupation. Considerations when estimating the average WC cost of a PTSD claim include:

• Types of medical treatment provided—Beyond the cost of counseling sessions and primary care office visits, what medicines may be prescribed to sufferers of PTSD? What health issues may arise directly related to PTSD or related to the treatment provided, such as side effects of prescription drugs? Might there be cases of self-harm?
• Temporary versus permanent disability—How might healthcare professionals determine when a worker has reached maximum medical improvement? How likely is it that a worker will be found to have a permanent total disability due to PTSD? How will benefits be determined in cases where the worker has a permanent partial disability? How will statutory time limitations affect expected costs?

See also: Addressing PTSD in the Workplace

Conclusion

Mental health conditions in the workplace are receiving greater attention, whether they arise from a single event or continual exposure to an unusually traumatic work environment. Many state legislatures have been considering establishing or expanding the eligibility for WC benefits to those suffering from work-related PTSD. And COVID-19 could intensify this trend.

To gauge the impact of PTSD claims by occupational group and on overall costs, one needs to estimate the number and average cost of such claims. Recognizing the scarcity of WC-specific PTSD claims data, a projection of the potential impact that PTSD claims may have on WC costs may result from analyzing available literature and incorporating key considerations. These considerations include the extrapolation of prevalence rates to estimate annual WC claim frequency and the applicability of historical studies to the current situation, given the locations, time periods and methodologies underlying such studies.

Finance and insurance companies have been leveraging synthetic data for many years to improve their workflows while ensuring information confidentiality. With the COVID-19 pandemic, scientists who are striving to find ways to combat the virus have considered synthetic data. How can this technology be of use in healthcare, and how does it help to cope with the pandemic?

What Synthetic Data Is

Without going into convoluted definitions, synthetic data is artificially generated data. It is similar to real data but doesn’t copy it. Synthetic data is generated automatically with the help of dedicated algorithms. It can be in the form of text, video, image, audio or information from tables. 

Synthetic data can be applied in various areas. Waymo uses it to train its driverless cars. American Express uses artificially generated financial information to improve its fraud detection system. Synthetic data helps companies calculate risk accurately while protecting real customers’ data. The OpenAI team has taught the language model GPT-3 to compose texts similar to those that a human would write. A program belonging to Nvidia creates photos of people based on images of real individuals.  

In healthcare, using synthetic data means that important analysis can be done without associating particular people with their medical records. After the outbreak of the coronavirus pandemic, the need for applying such data in healthcare has increased. 

Synthetic Data in Healthcare

Secure data exchange is one of the major concerns in healthcare. According to the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), any confidential information can’t be disclosed without the consent of the person it belongs to.

Information from patient records must be stored and transferred securely Using the data without specifying the name of the patient is prohibited, too, as it is possible to identify an individual based on the data set. 

That’s why it is more lawful and secure for researchers to create synthetic data as they conduct studies crucial for humanity. Prototypes of training software for machine learning models are trained with synthetic data so they can work with real patient data later. Developers don’t have access to the real information -- they can’t read it, extract it from software or use it in any other way.

See also: Avoiding Data Breaches in Healthcare

How Synthetic Data Is Generated

If the true patient is not at risk of being identified, information from real medical records can serve as the basis of synthetic data, though joint case records are much more commonly used. There is also the sort of approach that Mitre offers via Synthea, an open-source tool that allows for creating fictional patients based on publicly available information: scientific research data, disease statistics, demographics and so on. Although the generated dataset is not as reliable as “fakes” of the real medical records, the platform continues to be improved under the auspices of the U.S. government.

Although synthetic data is not suitable for studying real diseases and treatment methods, it can be the basis for the development of applications that allow for using real data without breaking the law. 

Thus, synthetic data opens access to research and development of new technologies in healthcare. 

Practical Applications of Synthetic Data 

Soon after the pandemic outbreak, Israeli scientists began testing synthetic data technology based on EMRs from the last 20 years. Sheba Medical Center -- the country’s largest hospital -- used the MDClone platform to synthesize the data of its coronavirus patients.

The healthcare facility invited analysts who collected all the information about the virus from the data set. The result of the cooperation of medical researchers and software developers was an algorithm that helps the hospital staff decide when to prescribe medications or when inpatient treatment is needed. 

The software allowed Sheba Medical Center to combine the data from its EMRs with the data belonging to another Israeli healthcare facility -- Maccabi HealthCare Services. This provided scientists with a broad view of the course of the individual disease, helping estimate coronavirus outcomes for each person. Without synthetic data technology, the project would have taken much longer as permission to use confidential information would have been required.

Of course, medical scientists can’t rely solely on synthetic data in their research, but the data lets them easily analyze an unlimited number of hypotheses that can lead to significant time savings during the approval of new drugs for real patients.

See also: Wake-Up Call on Ransomware

Although some data security experts doubt that synthetic data in healthcare can ensure patients’ anonymity, this data is extremely useful in prognostications, survival analysis, clinical trials, decision-making and more. Such technologies will accelerate innovation in healthcare while helping scientists comply with legislation. 

AUGUST 2021 FOCUS OF THE MONTH
Cognitive Technologies

 

 

FROM THE EDITOR

 

 

Cognitive computing is a funny beast. Every time you hit your target, you find that another pops up off in the distance.

 

When I first saw a demonstration of speech recognition, some 30 years ago, I was mightily impressed that the computer understood a few words. If I had seen what would be possible today, I'd have been stunned. But now? Oh, that's just Siri or Alexa. And why didn't auto-correct guess exactly what I wanted to say?

 

 

Something like artificial intelligence is also hard to get our heads around because of the halo it has. At a mention of AI, we tend to think of some all-knowing creature that can solve all our problems. (Or we think of some dystopian scenario such as those in Minority Report or War Games, where an AI run amok could overwhelm humanity.)

 

 

In fact, AI and other cognitive computing techniques such as machine learning and advanced data analysis are simply tools. They are remarkably powerful tools, perhaps the most important we have, but they are just tools. Sometimes, they don't even work -- lots of attempts to use AI to predict the course of the pandemic failed because there just wasn't enough known about the virus to provide the right data to the computers.

 

 

Fortunately, the insurance industry seems to be coming to grips with cognitive computing and using the techniques to address specific problems and opportunities, rather than viewing cognitive computing as an all-encompassing solution. The tools are addressing specific issues in underwriting and claims, by incorporating data from new sources and generally speeding the process. The tools are allowing for better understanding of customers, especially as more of the interactions with them are digitized, providing loads of data for analysis. And so on.

 

 

I'm optimistic that we'll solve those problems soon. And then it'll be time for the next set.

 

 

- Paul Carroll, ITL's Editor-in-Chief

 

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6 QUESTIONS FOR JIM MCKENNEY

We spoke with Jim Mckenney, chief strategy officer, Intellect SEEC, about artificial intelligence and the implications for the insurance industry: People have been talking about artificial intelligence for a long time. In a lot of ways, you can go all the way back to the 1950s. How far along do you think we are in implementing AI in insurance. The use cases in insurance are endless, everything from automating a simple process to having a level of sophistication where artificial intelligence takes in a bunch of multi-dimensional information, applies machine learning models and makes accurate decisions. But if we look at where the insurance industry is right now, those that are successful with AI are really looking for point solutions... Read More

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WHAT TO READ

	The Future of AI in Insurance Organizations hoping to deploy artificial intelligence have to know what problems they’re solving — no vague questions allowed. Read More
	3 Ways AI Can Boost Customer Retention Not only does AI improve the speed of crafting policies, the technology can speed underwriting, as well as the claims process. Read More
	From Risk Transfer to Risk Prevention IoT provides the means for evolution from pure risk transfer to a "prescribe and prevent" scenario. Read More
	Top Problems That AI, ML Help Solve As insurance carriers get better at leveraging data and predictive analytics, the focus will shift from product-led to customer-centric models. Read More
	How AI Is Moving Distribution Forward AI improves risk analysis and fraud detection while providing more sophisticated pricing and faster, more personalized customer services. Read More
	Insurers Turn to Automation When automation is a core technology, transformation can occur at speed, meaning faster return on digital investment. Read More
	ON DEMAND WEBINAR Modernizing Commercial Underwriting using AI Intellect SEEC partners with Novarica to present a two-part webinar series discussing the key considerations to take while adopting AI into commercial insurance operations to improve efficiency and automation. Watch Now

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WHO TO KNOW

Get to know this month's FOCUS article authors:

Christophe Bourguignatz	Matteo Carbone	Isabelle Flückiger
Ryohei Fujimaki	Karin Golde	Simon Pickersgill
Sathyanarayanan Sethuraman

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As part of this month's ITL FOCUS on cognitive technologies, we spoke with Jim Mckenney, chief strategy officer, Intellect SEEC, about artificial intelligence, data, and the implications for the insurance industry.

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ITL

People have been talking about artificial intelligence for a long time. In a lot of ways, you can go all the way back to the 1950s. How far along do you think we are in implementing AI in insurance.

Jim Mckenney, chief strategy officer, Intellect SEEC

The use cases in insurance are endless, everything from automating a simple process to having a level of sophistication where artificial intelligence takes in a bunch of multi-dimensional information, applies machine learning models and makes accurate decisions. But if we look at where the insurance industry is right now, those that are successful with AI are really looking for point solutions.  

If you think about insurance from a macro level, we take in information about potential risk and process it and then try to predict an outcome. Today, in the commercial lines space, there’s a highly inefficient exchange of information from customer to agent and broker to carrier. That’s where we see point solutions very specifically solving for the gathering of information. This is where we've made most progress.

ITL

Could you maybe draw that out a little bit and talk about how AI can change an underwriter’s daily practice in a way that benefits the underwriter while speeding the whole process?

Mckenney

There's a lot of information, and much of it is processed manually. AI can efficiently manage the information and flow from customer to broker to carrier, so there’s a quicker response to the broker, which is highly valued. But the AI is also improving the data quality and providing a larger footprint of relevant information for the underwriter to act on. The information is more granular, too. In a manual process, folks would just roll the information up to high-level categories because it would take too long to go into detail. But AI lets you differentiate much more than you have in the past. And if you out-select your competitors, you’re going to outperform them.

ITL

Have you had a particular “Aha!”? Is there something people weren't getting at before that they're seeing now?

Mckenney

Yeah, the pleasant surprise is that successful carriers are creating efficiency and reinvesting into sophistication. Their response times have gotten a lot quicker, and they’re investing the savings in more information. For example, there is a lot of third-party data that they can use to become more sophisticated than their competitors.

I firmly believe that some carriers will fall behind, and, over time, there will be consolidation. Unlike personal lines, where three to five carriers dominate, the commercial lines segment is a very fragmented market.

ITL

Is there a particular kind of third-party data you see people going after that they couldn't go after before?

Mckenney

There's so much out there, everything from just pure structured data, from endless data providers with new elements, to unstructured information that's out there on a particular company. And then there's all the Internet of Things information that can even help manage risk.

In our business, we have over 3,000 data elements that we bring to our carriers. And there are others out there that boast of having even more. That's a combination of structured and unstructured data that we make real for carriers.

ITL

We've talked a fair amount about underwriting. Where else do you see AI having a big impact in the near term?

Mckenney

AI is starting to take off in the claims process, as well. At the end of the day, AI is about making predictions. The insurance industry has had predictive models for years, but computing power is so much cheaper and more available that AI can just spread to wherever predictions are needed.

ITL

Any last words?

Mckenney

Many carriers have gone down the road of leveraging AI, and many haven’t seen the success that they expected. So, one of the things that we really coach our customers through is the whole idea of change management. Change management is critical. Where we see folks trip and fall the most is in where AI interacts with the human. There's a lot of mistrust by an underwriter, for example, about what's coming out of the machine. So, a key to success is building your AI in a way that the user can trust but verify. If answers just come out of a black box, people tend not to trust it, then they tend not to use it and then you ultimately don't get the value that you're seeking.

So, there are issues. But all industries are leveraging data better than they did before. And yet we're just at the tip of the iceberg.

ITL

I very much agree. It'll be fascinating to see where we go from here. Thanks for taking the time.

Data breaches are perhaps more harrowing an experience than many realize. In the latest, the vast majority of LinkedIn users—about 92%—have been affected. Experts say it's not just the magnitude of this breach that's worrying; it's the type of information compromised that should give LinkedIn users pause. The breach was announced June 22 by the alleged hacker, offering the data of 700 million users for sale. The breach reportedly contains email addresses, full names, phone numbers, physical addresses, geolocation records, LinkedIn username and profile URL, personal and professional experience or background, genders and other social media accounts and usernames. It's easy to imagine creating a digital profile impersonating any of the 700 million victims. 

As urgent as this sounds, most breach victims do very little, if anything, in response. Some may find slight solace in the fact that many others (and, by many, we mean millions) have also been affected in the same way. This feeling can sometimes help to dampen the blow, along with the fact that most consumers are realizing there is virtually nothing they could have done to prevent the breach from happening in the first place: Data breaches are just par for the course in today’s digital world. 

But this limited consolation offers nothing to protect victims from the uncertain, but very real risk of future identity fraud. Over time, with breach after breach, the uncertainty grows, consumer trust in businesses weakens and new anxieties take hold. There’s an oft-overlooked psychological aspect to it all that even consumers themselves may not realize is happening. 

Living in a constant state of worry about being affected by identity theft can be exhausting. And while it may not be a frequent topic of conversation—and breach notifications often aren't triggering specific precautions to be taken—we still know it weighs heavily on consumers' mental state. Customer trust in organizations is eroding, and it's happening rather quickly. Customers no longer have confidence that corporations can safely house the data they’re asking of customers. 

Moreover, there's a subconscious pleasure that comes with feeling loyal to and cared for by organizations that one chooses to spend money with; all that goes away when the company experiences a data breach. The consumer no longer feels appreciated or valued and may be left wondering, are there any organizations left that I can trust with my data? Sadly, the answer may be an unstated but emphatic "no."

See also: How Machine Learning Halts Data Breaches

The more definable and palpable sentiments of the consumer are still telling, though. Over the last few years, we at Generali Global Assistance have been keeping our finger on the pulse of the consumer to gain a better understanding of the consumer mentality. Earlier this year, we released findings from our second market study and found that, since 2017, concerns about identity theft—and especially about cybercrime—have continued to increase.

The very large majority of consumers (90%) believe that becoming a victim of identity theft or cybercrime is something that could happen to them at any time; this is up five points from 2017. Eighty percent of consumers said that they’re often scared by the number of ways in which their identity may be compromised, which is up 6 points from 2017. And 76% of consumers agreed that companies and institutions are not doing enough to protect their personal information. A holiday survey of ours in 2017 showed that 40% of consumers believe that businesses are not doing all they can to protect their personal information. The rate at which this gap has widened is striking.

Outside of the psychological impact, we must not forget what it actually means when one's personal data is exposed. While a leak of your email address may leave you shrugging your shoulders, the combination of your name, address and birthdate can be ample data for a fraudster to do damage. It's the aggregation of years' worth of mega breaches that are leaving consumers wildly exposed. We already got a sneak peak of that this past year when unemployment fraud reached frightening levels in the midst of the pandemic—in our Resolution Center alone, we saw a 5,630% increase in employment-related fraud from 2019 to last year. Unfortunately, that may have been just the beginning. 

So while a singular data breach may not have an immediate effect on the consumer, each one chips away at the security of their identity. Data point by data point, our identities will be left in such a fragile state that the resulting fraud will not only be inevitable, but difficult to come back from. 

At the risk of sounding trite, the time is now for consumers to take action. It's true that one cannot prevent hackers from infiltrating the companies they choose to do business with, but there are certainly ways to strengthen their identity so that any potential damages are greatly reduced.

My first job after serving in the Peace Corps was working at Levi Strauss on technology, data and vendor supply chain projects. I remember hearing the famed story of how Levi Strauss had the foresight and innovation to tackle the gold rush from the picks and shovels angle, building out infrastructure that far outlasted the explosion of miners rushing to California to seek fortune. People often drew parallels between Levi Strauss and the tech boom, but it wasn’t until recently that I truly connected the dots.  

I went on, like many others, to join the tech sector, eventually jumping into the excitement of insurtech -- a legacy industry faced with the growing challenges of modernization. My last company, Sheltr, which I co-founded with Praveen Chekuri, provides homeowners with routine preventative maintenance service and diagnostics to offer data-driven care to catch issues before they become costly repairs. After Sheltr became the first acquisition made by insurtech unicorn Hippo, we decided to leave homeowners insurance and build payments infrastructure on the commercial side.

We did this because we saw a pattern. Insurtech continues to receive huge amounts of attention and investment. In Q1 of 2021 alone, insurtechs raised $2.2 billion across 110 deals. But much of this insurtech investment has been focused on distribution-related companies aiming to sell insurance directly to customers by promising a better product, novel ways of acquiring customers and improved end-customer experience. A big piece was missing.

The unsolved problem in insurtech

While many billions of insurtech investment dollars have flowed into insurance, this has primarily centered on companies trying to reinvent insurance businesses from the ground up. The insurtech community hasn’t focused enough on improving the critical infrastructure and tooling that insurance carriers and distributors need to be successful in today’s competitive, digital world. 

While some strides have been made, we’re still in the early stages of insurtech infrastructure. Embedded insurance, usage-based insurance (UBI), advanced telematics are all emerging trends that will no doubt continue to evolve in the years to come. However, many of the most critical pieces of insurance infrastructure remain untouched. Behind the elegant customer experiences of distribution insurtechs is technological shoe-string and duct tape holding it all together. Said bluntly, infrastructure has not kept pace with customer expectations in insurance.  

See also: How Digital Health, Insurtech Are Adapting

Why have we, as an industry, fallen behind in meeting what our customers need from us? As with many legacy industries, established incumbents with millions of customers have a hard time moving quickly and adapting--and this is especially true in sectors where expenses have to be prioritized on compliance and risk. But innovating insurance infrastructure is also a challenge for startups. As I’ve learned working at various early stage companies, the customer-facing interactions are always tackled first, while behind the scenes is an operational mess. What’s happening at the macro-level with insurtechs -- dealing with the front end before dealing with the back end -- parallels what happens at most startups at the micro-level.

As an industry, we reached an inflection point, where everyone on the distribution side -- from brokers to agents to carriers -- suddenly had no choice but to digitize. All aspects of insurance including underwriting, distribution and servicing are moving from offline to online. This affects all consumers, both personal and commercial. We increasingly expect high-quality experiences in our professional lives just as we do in our personal lives -- and this growing sentiment was put in overdrive when COVID-19 hit.

Where to start?

Recognizing the need for infrastructure innovation is the first step, but how one goes about implementing and executing a fully end-to-end digital infrastructure solution is critical. 

Payment is the first logical step. Payment is one of the most under-innovated and complicated pieces of any insurance system. As a consumer, it’s no secret that paying for insurance is much harder than buying nearly anything else in 2021. Imagine buying groceries and not knowing who to pay (the brand or the retailer), how often to pay or when to pay (when you eat the food or when you take it home), then having to write a paper check to mail off. Payment is also laborious and expensive for the sellers and backers of insurance. Just thinking about the amount of hours spent by agencies, managing general agencies (MGAs), wholesalers and carriers reconciling transactions is likely making some of you want to walk away from your computers. 

The second step is to listen and pay attention. The question shouldn’t be who will win -- incumbents or startups -- but rather how can we deliver the best customer experience and sustainable product? How can we pay attention to successes and failures so that, collectively, we can get closer as an industry to meeting the needs of our modern customers? Easier said than done, but we will get there. 

The dangers of ignoring infrastructure

The reason why we, as an industry, will undoubtedly find ways to innovate is that the risks of not doing so are too high. Companies that don’t adapt and build proper infrastructure for their core actions -- such as payments transactions -- will ultimately fail to meet rapidly evolving customer expectations. You can only go so far with a facade of streamlined customer experience because, at scale, you’ll eventually need a robust digital back end to support your systems and stay competitive. 

You’ll need the picks and shovels.