Ukraine (UKR) is one of the largest insured risks for political risk insurance (PRI) and trade credit insurance (TCI). This predates the current situation in Ukraine and started immediately after the country's accession to sovereignty in 1991.

Based on data from the Berne Union, the trade association representing credit and political risk insurers, PRI carriers issued $19 billion of new coverage globally in 2021, of which $2 billion went to cover risks in Europe and $1.6 billion to cover risks in Russia. Based on these figures and other published data, we estimate that PRI insurers have insured between $1 billion and $7 billion in Ukraine risks over the last five years.

New PRI losses in Ukraine due to Russia's invasion will likely be material but well within the ability of private carriers to perform on their obligations. Several factors are contributing to reducing private PRI insurers' exposure in Ukraine:

• Some PRI and TCI carriers stopped writing Ukraine risk in 2014
• Carriers with existing Ukraine risk have likely taken reserves against future losses in Ukraine
• 80% of PRI coverage is provided directly or indirectly by government agencies such as OPIC in the U.S. and by multilateral agencies such as the World Bank's MIGA.

Insured Losses in Ukraine

In Ukraine, PRI and TCI tend to be primarily purchased by foreign companies with cross-border trade or investments in the following industries:

• Energy
• Manufacturing
• Infrastructure/Project Finance
• Natural Resources

Losses due to Russia's invasion of Ukraine fall under comprehensive political violence, and more specifically under war and civil war and strikes, riots and civil commotion. The full range of PRI and TCI coverages includes:

• PRI: political violence, expropriation and breach of contract
• TCI: Short-term credit, medium-/long-term credit and other cross-border credit

PRI coverage protects primarily against loss of assets and profits. PRI's political violence protects against losses due to strikes, riots and civil commotion (SRCC) and war and civil war. TCI's credit default coverage protects primarily against loss of profits due to force majeure; failure to perform on letters of credit due to force majeure; and loss of profits and cost of penalties due to force majeure. PRI and TCI tend to also provide compensation for the cost to the insured of its failure to perform on obligations to third parties, such as penalties for non-delivery of goods or services due to covered risks.

PRI political violence's war and civil war coverage excludes declared war between the permanent members of the U.N. Security Council, which includes Russia.

Loss of profits due to sanctions may be covered by PRI and TCI policies. However, the coverage is structured to cover sanctions by the host (ex: Ukraine) or third-party countries (ex: Russia), not the insured's home country. For example, a U.S. company investing in Ukraine may or may not be covered for sanctions imposed by the U.S.

See also: How to Achieve Cyber Resilience

Markets and Policies

The majority of private carriers providing PRI insurance are based in the U.S., at Lloyd's and in Bermuda. PRI providers include AIG, AXA XL, Chubb, Sovereign, Caitlin and Zurich. The largest providers of TCI coverage are Coface, Heuler Hermes and Atradius. Government or multilateral agencies providing either PRI or TCI or both include OPIC (U.S.), EXIM Bank (U.S.), EDC (Canada), UKEF (U.K.) and MIGA (World Bank).

PRI insurance policies coverages tend to range between $100 million and $4 billion. TCI coverage can range from $250,000 to $2 billion. The main buyers of PRI come primarily from the extraction sector, manufacturing and infrastructure. Those industries are also significant buyers of TCI, in additional to import-export businesses in soft and hard commodities. Rates for PRI tend to range between 0.5% and 2% and for TCI between 0.1% and 1.5%. Above such rates, the products tend to be become either less attractive to buyers or unavailable due to the risk profile of risks. When coverage is available, rates for Ukraine would be at the higher end of these ranges.

Domestic U.S. Cyber Risk

The main risk associated with Russia's attack on Ukraine for business in the U.S. and the E.U. is state-sponsored Russian cyber attacks on U.S. business and critical infrastructure. This is regardless of whether they have operations and investments or do business in Russia or Ukraine. Indeed, Russia has a well-documented history of unofficial cyber attacks on Western assets. The impact of declared or undeclared Russian cyber attacks on U.S. businesses would be covered by a company's cyber risk policy. A PRI policy is not necessary to cover Russian cyber attacks against U.S. businesses in the U.S. Because some cyber policies exclude state-sponsored actions, insureds should contact their insurers to confirm whether state actions are excluded from their coverage.

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Breakthrough Technologies for 2022 Paul Carroll, Editor-in-Chief of ITL The MIT Technology Review's annual list of breakthrough technologies came out last week, and, as always, it's worth a look, both because of the general framing it offers about technology trends and because of some specific implications for insurance. From an insurance standpoint, the two most directly relevant are what TR refers to as "the end of passwords" (yay!) and the growing availability of "synthetic" data to train AI in situations where there isn't enough real data available. But the other eight on the list of 10 are intriguing, too, especially for what they suggest about how we might be able to build on the technologies behind the mRNA vaccines against COVID to make additional, huge advances in healthcare. continue reading > New Research Don't miss Majesco's annual consumer report to better understand how the insurance buyer sweet spot has shifted to Millennials and Gen Z and what emerging insurance trends are driving new opportunities. Download Report SIX THINGS Lemonade: No Sign of Disruption Yet by Matteo Carbone For each dollar of premium, claims cost Lemonade 90 cents, marketing cost it 60 cents and other costs added 40 cents. Feel a little betrayed? Read More Insurtech's Lasting Role in Insurance by David Fontain Thousands of lines of insurance haven't been innovated in 30 years. With so much opportunity, it's time to think about insurtech as a permanent fixture in the larger ecosystem.  Read More How IT Savings Can Fund Innovation Sponsored by Rimini Street While IT infrastructure is a business necessity, aging IT systems can inflate expenses and limit the ability to spend on initiatives that will really move the needle for a business. In this webinar, we discuss how insurers can optimize their data centers and generate savings that can fund innovation, while improving security. Read More Auto Insurance in 2022: What to Expect by Rochelle Thielen 2022 will look a lot like the second half of 2021, but opportunities are emerging for auto insurers to differentiate themselves, especially through telematics.  Read More Setting Record Straight on Auto Claims Severity by John Kanet While the assumption has been that higher repair costs for advanced driver assistance systems features offset ADAS loss-cost benefits, a new study finds significant benefits. Read More A Patient-Centered Approach to Claims by Dwight Robertson Properly applied, AI produces better medical outcomes, gets employees back to work faster, reduces litigation risk and lowers the costs associated with claims. Read More How to Achieve Cyber Resilience by Rajeev Gupta If an organization does not have a well-thought-out incident response plan with trusted and tested backups in place, a cyber attack can be devastating. Read More InsurTech Ohio Spotlight with Ron Rock Sponsored by JobsOhio  Ron Rock discusses how the insurance industry is rapidly evolving, and the importance of recruiting and retaining top software and programming talent.  Read Now MORE FROM ITL February Focus: Blockchain Sponsored by Gigaforce While blockchain has been an intriguing topic for some time now, interest has skyrocketed in recent months.  Facebook said late last year that it believes so much in the "metaverse" that it was changing its name to Meta. Microsoft followed quickly with its own vision of how technology could enable a whole new sort of world, where our selves are disembodied and our interactions occur via virtual reality. Venture capital firms such as Andreessen Horowitz are all over the investment opportunities in the metaverse, sometimes referred to as Web 3.0. And pundits are making bold proclamations about how life as we know it is about to change forever.  Read More Reconfiguring Your Approach to Underwriting  Sponsored by Intellect SEEC Learn how you can vastly increase the speed at which you deliver capabilities and new systems - cutting deployment times to hours or days, from weeks or months, while delivering a clear ROI. Read Now   Partner with ITL to create expert thought leadership content. Custom Content Promoted Content Display Advertising Custom Webinars Monthly Topic Sponsorships ITL Partner Packages and more Learn more and get the 2022 Media Kit

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FROM THE EDITOR For an industry that has long been considered sleepy, life insurance has a lot going on.  The efforts getting the most attention have probably been those related to improving the customer experience, largely by speeding underwriting by reducing or doing away with the need for medical exams and the providing of blood and urine samples.  But other big changes are afoot, too.  Artificial intelligence is being deployed in ways that range all the way from the mundane to the potentially profound. The mundane should still have a big effect, because AI will allow underwriters to capture and manage information much faster and more efficiently than they now can -- for instance, by having the AI take data in a wide variety of formats, including doctors' infamously bad handwriting, and present it in a standardized, easily accessible format. And the sky is the limit on the most ambitious AI initiatives, which could let underwriters do a much better job of assessing risks and could even help insurers reduce clients' risks. (For a great example, check out the article below on the potential for genomics.) Private equity, always a major change agent, has its sights on life insurance, if only as a source for "permanent capital." This McKinsey article explains in detail. And I continue to believe that there are many opportunities to embed life insurance in other offerings, such as for mortgages or commercial loans, and for financial advisers to include various forms of life insurance and annuities in their services. If all that isn't enough, please consider the interview I did with Ronnie Klein, attached below, on the aging crisis that cries out for aggressive action by the life insurance industry. Cheers, Paul   INTERVIEW WITH RONNIE KLEIN As part of this month's ITL Focus on life and health, we spoke with Ronnie Klein, a senior adviser at Afiniti and founder of Obtutus Advisory, who has a long history in life insurance, with a focus on aging. He describes what he sees as a full-blown crisis--one that life insurers are uniquely positioned to address.  ""We've seen populations aging and fertility rates dropping for decades. Actuaries, economists and demographers have been predicting this crisis for a long time."" -Ronnie Klein Read the Full Interview READ MORE World in Flux: Can Insurance Fill the Gap in 2022? The level of change has never been greater, and insurance’s opportunities have never been more numerous. Read More Predictive Modeling’s New Mantra With the power of predictive modeling and its resulting efficiencies, life insurers can simplify underwriting, reducing both time and complexity. Read More The Need to Offer Better Perinatal Plans Comprehensive perinatal education helps employers address at least five related opportunities. Read More How AI Can Solve Prior Authorization Physicians spend nearly two full business days per week on prior authorization requests as part of an antiquated, manual process Read More 2022 Will Challenge Health Insurers COVID has transformed health insurance and what it means for coverage, pricing and patient care for years to come. Read More Genomics Revolution in Life Insurance Here’s where blockchain stands today — plus how to see the inherent risks and opportunities of using this exciting technology in insurance. Read More FEATURED THOUGHT LEADERS David Zhu Denise Garth Jenny Morrow Calvin Beyer Mark Scott Michael Giusti View all ITL FOCUS topics Share Share Tweet

ITL:

To start, could you provide a little perspective and frame the aging crisis?

Ronnie Klein:

Prominent colleagues of mine call it a predictable surprise, and I like that term. We've seen populations aging and fertility rates dropping for decades.  Actuaries, economists and demographers have been predicting this crisis for a long time.

The total population of the world is projected to increase through the end of the century, then it's supposed to plateau and come down. However, a lot of that population growth is coming from the non-mature countries. In a lot of the mature markets, we’re seeing population decline or slowing population growth.

It's a major concern because Pillar I [such as U.S. Social Security] pension systems will have a difficult time keeping up with aging populations. There’s going to be a lot of stress on these systems as contributions from fewer workers have to support a growing number of retired people. At some point, benefits will have to change, and people will have less income to live on, unless they have their own personal insurance, including annuities.

It’s a big issue.

ITL:

We’ve heard a lot about aging in Japan. Would Japan, in fact, already be facing the aging crisis, and what countries, including the U.S., would you see following reasonably close on after Japan?

Klein:

Japan tried a program where it was trying to keep up with Pillar I contributions in an actuarial manner as if it was like a private pension plan, but what was considered to be the normal retirement age was getting to be so old. It becomes difficult to sell the public, who have 65 years old in their minds, on the idea that the normal retirement age is now 72. What are you supposed to do: Work from 65 to 72?

That doesn't work for much of the population, whom we might term the blue-collar population. Sure, a white-collar person could possibly work to age 72, but what about someone who's in construction or a bus driver? These people perform important services for the world and deserve a comfortable retirement.

In terms of countries, you have to look at Malaysia, South Korea, Japan and Italy. Spain has real problems, as well, with few people having children. People are just living longer, too, in the southern European nations. It must be those Italian or Spanish wines.

In addition, mass urbanization of the world is contributing to both climate change and global aging. When you move to a big city, you usually live in smaller quarters, which provide less room for families. It’s also very expensive to live there, and, when people have less money, they typically have fewer kids.

ITL:

You’ve certainly painted a picture of a daunting problem. How can the life insurance industry start addressing it?

Klein:

The place to start is education. I should probably get off of my soap box about this, but I think the insurance associations of the world should get together and lobby for teaching financial literacy in schools.

When I moved to Switzerland years ago, the youngest of my four kids was seven, and she told me about how her class went into the forest and learned how to use their Swiss Army knife to whittle wood and make a fire and all that kind of stuff. While learning how to survive in the forest is certainly an important skill, so is financial literacy. School should teach about saving for retirement, the power of compound interest, how life insurance works, auto insurance, homeowners insurance, etc.  It's really important.

ITL:

Beyond education, is there any way for life insurers to work with government?

Klein:

Government is actually our biggest competitor. What I mean by that is, it seems like whenever there's a major disaster, the government steps in and helps people out to the point where they say, “Why do I have to take care of myself? The government will help me if there is a disaster.

After 9/11, I saw an interview with a young widow with two kids whose husband was a senior executive at a firm whose offices were at the top of one of the towers. They lived in one of the most exclusive parts of Long Island. She was crying as she explained that, as the firm allocated funds to the families of victims, she was receiving only $500,000. I thought, “You’re telling me you and your husband didn’t have a $20 million policy on his life?” For that matter, why didn’t the firm have a group policy on all its senior executives?

A $20 million policy is what an adviser would have recommended on someone with his salary. But there’s a sense of entitlement that keeps people from thinking they need to take care of themselves.

The industry has to get together on some sort of a campaign and stop worrying about whether it will somehow stir up trouble. The life insurance industry has always worried about speaking up. Most industry associations just get together for meetings and issue papers, but there has to be a concerted effort by the insurance industry to get out there.

The first thing to do is to recognize the problem and to make the world recognize that we have an aging crisis. Lobby for financial literacy to be on the syllabus for schools. And then explain to the government how life insurance will help. The more people who have adequate life insurance, including annuities, the less there is for the government to cover.  

There should also be changes in Pillar Two [private contributions by individuals and employers for retirement savings]. In an ideal world, things like 401Ks in the U.S. would be mandatory. Pensions should also be portable, as they are in Europe, so you carry your pension with you from company to company. I have three different pensions for the three companies I worked for in the U.S.

The pension problem is only going to get worse, with all these gig workers. These kids are not saving for the future. There's no retirement savings pool for gig workers, and they're not putting money into an IRA.

There are models that work in different countries that can be copied.

ITL:

Thanks. This has been super challenging but also enlightening.

When the pandemic hit in 2020, many property and casualty commercial insurers had to pause, stabilize and reprioritize their digital transformation plans amid lower business volumes, economic lockdowns and an uncertain financial environment. But that all changed in 2021 as commercial insurers pressed the pedal to the metal. Now, a new study from SMA indicates that commercial insurers will continue to advance with their transformation strategies in 2022. However, their focus will look a little different than it did a year ago.

SMA's recent research report, "2022 Strategic Initiatives: P&C Commercial Lines," features market insights from an annual survey of insurance executives about the state of their strategic plans. Over the past two years, the insurance industry has shifted its priorities in response to changing consumer demands, altered business patterns and the workforce revolution. This year, 100% of commercial lines carriers are at some stage in their digital transformations. But, as insurers move past the plans implemented during COVID-19, many realize that they are not as far along in their transformations as they initially believed, and there is still a significant opportunity to transform their businesses. 

When looking at differences between insurers focused on small commercial versus mid/large commercial lines, there is significant variance in the business drivers pushing technology investments forward in 2022. In the small commercial segment, growing existing lines, markets and geographies is the number one driver this year. As insurtechs and new entrants increase market competition, small commercial insurers are more likely to focus on growth than their mid-market/large commercial peers. Increased competition is also why more than half of small commercial insurers are also focusing on cost containment.

See also: Climate Change and Product Liability

On the other hand, most mid-market/large commercial insurers are aiming for business optimization and transformation with their technology investments to reduce complex manual processes. Currently, there is a wealth of opportunity for them to automate operations and leverage insights for greater efficiency.

What is clear in 2022 is that commercial insurers seem to have solid road maps for their digital transformation, even if many are still in the early phases. As the year unfolds, technology investments will be necessary to build a platform for innovation and growth. Rethinking talent strategies will also be critical now that employees' expectations have forever changed due to the pandemic. A major change is underway, driven by key strategic initiatives evolving traditional and transformational aspects of the business, and insurers will need to be bold and embrace change to be successful in 2022 and beyond.

For more information on specific strategic initiatives of commercial lines insurers, including the strategies of the small commercial and mid/large markets, read SMA's recently published research report, "2022 Strategic Initiatives: P&C Commercial Lines."

The discourse around algorithm fairness has garnered increasing attention throughout the insurance industry. As the use of machine learning has become more common, from marketing and underwriting to claims management, regulators and consumer rights organisations have raised questions about the ethical risks posed by such technology.

A catalyst of this social discourse was an article titled Machine Bias published by the investigative news organization ProPublica. The paper focused criticism on a law case management and decision support tool used in the U.S. judicial system called Correctional Offender Management Profiling for Alternative Sanctions, or COMPAS.

A conclusion of the academic research surrounding this debate is that there are competing definitions of fairness, and these definitions may be incompatible with one another. The concepts of fairness at the center of the discussion are calibration (a.k.a. predictive parity) and classification parity (a,k,a, error rate balance). There is also the concept of anti-classification, which calls for sensitive (a.k.a. protected) attributes not to be explicitly used in decision-making.

A stylized example, designed for educational purposes, illustrates the intrinsic incompatibility of predictive parity and error rate balance. The example uses the Adult dataset, which figures prominently in studies on machine learning. Gender is the sensitive attribute of choice. It is demonstrated that a classification model that satisfies predictive parity across two groups cannot satisfy error rate balance if the baseline rate of prevalence differs between the groups.

The dataset, which is publicly available, comprises 48,842 anonymized records of annual income and personal information (such as age, gender, years of education, etc.) extracted from the 1994 U.S. Census database. The prediction task is to determine whether a person makes more than $50,000 a year.

In the dataset, 24% of individuals are high earners. This baseline rate is higher for males (30%) than for females (11%). The dataset has been an object of research for its imbalance on gender, as females make up only 15% of high earners but 33% of the entire data set.

The algorithm satisfies predictive parity at a chosen threshold of predicted probability of being a high earner if in the category of predicted high earners the empirical probability of being a high earner is independent of group membership, where group membership is defined by the sensitive attribute. In plain English, the Positive Predictive Value (PPV), defined as the ratio of true positive count to the sum of true positive count and false positive count, must be equal across groups within an acceptable margin of statistical error. As shown in the box plot below, for a threshold of 32% of predicted probability of being a high earner, the (mean and median of the) PPV of the high-earner category equals 61% for both groups. In the box plot, the median is represented by a horizontal bar within the box, and the box marks the range between the first and third quartile.

Turning to the concept of error rate balance, the algorithm satisfies this concept of fairness at a chosen threshold of predicted probability of being a high earner if the false positive and false negative error rates, respectively, are equal across groups. A direct consequence of a scoring model satisfying predictive parity is that it violates error rate balance if the baseline prevalence differs across groups. Thus, despite satisfying the concept of predictive parity, the algorithm has a disparate impact on the two groups.

See also: The Challenges of 'Data Wrangling'

In the stylized example, the group of female earners has a lower rate of high income. Thus, in the presence of predictive parity, the group of female earners experiences a lower rate of false positives (see box plot below). Compared with a false positive rate of 23% for the group of male earners (median, displayed as horizontal bar within box in right panel), this rate equals 5.1% for the group of female earners (left panel).

Correspondingly, the group of male earners experiences a lower rate of false negatives (see box plot below). Compared with a false negative rate of 36% for the group of female earners (median, displayed as horizontal bar within box in left panel), this rate equals 20% for the group of male earners (right panel).

In applying algorithms, it is important to recognize the trade-offs between different concepts of fairness and the presence of disparate impacts. Fairness is ultimately a societal, not a statistical concept. Insurers have an opportunity to shape the discussion on algorithmic fairness by demonstrating awareness of potential societal implications of their algorithmic decision-making.

This article first appeared at GenRe.com. 

The widespread digitization of the economy has created a wealth of data on risk exposures for insurance companies. But how insurers exploit what has become known as big data has prompted widespread discussion around data ethics, especially in regard to transparency and fairness where consumer data is used to market and to price risk.

Insurers are already familiar with the concept of adverse selection. Now they are getting to grips with the new concept of inverse selection that arises with big data.

It’s useful to look at a basic risk spectrum to explore how big data can fundamentally change the information balance of insurers and insureds, and the latter’s perception of risk.

Usually, the consumer has private information about where s/he is located on the risk spectrum, exposing the insurer to the risk of adverse selection. Using big data combined with machine learning, the insurer might be able to uncover the location and eliminate the information asymmetry. Where the consumer’s risk perception is poorly calibrated, the insurer might be able to reverse the information asymmetry in its own favor by having a more precise read of the risk than the consumer has, and even determine the consumer’s perceived location.

With this reversal in information asymmetry in mind, how should the insurer price risk – on the basis of actual risk or the consumer’s perception of risk? And should the insurer make big data available to the consumer or to society?

Consider the basic consumer risk spectrum (see below). The insurer has a read of the average risk on this spectrum but doesn’t know the locations of the individual consumers. The consumers on the other hand know their locations.

Knowing only the average risk, the insurer prices the policy to the center of the spectrum. All higher-risk consumers will purchase insurance and all lower-risk consumers will not. In consequence, the insurer won’t break even. It’s a simple representation of adverse selection.

When the insurer is equipped with big data, this situation changes, however. The insurer now has granular consumer information, potentially observed at high frequency, and can gauge the consumers’ true locations on the risk spectrum – and differentiate premiums accordingly.

In this case, every consumer will purchase insurance. The lower-risk consumers now have access to insurance at premiums commensurate with their actual risks. The higher-risk consumers will purchase insurance also, albeit at higher premiums, no longer collecting an information rent. And the insurer will break even. Clearly, a socially desirable outcome.

See also: Why Exactly Does Big Data Matter?

What happens if the consumer’s perception of risk is not well-calibrated? Let’s look at consumers 4 and 8 (see below). Consumer 4 erroneously believes him/herself to be lower-risk, whereas consumer 8 errs in the opposite direction. If the insurer prices to the actual location on the risk spectrum, then consumer 4 will not purchase a policy; consumer 8 on the other hand will still purchase a policy, at a premium that s/he perceives as a bargain.

Inverse selection arises if the insurer sets the premium to the maximum of the actual risk and the consumer’s perceived risk. Then the insurer will collect an information rent on consumer 8.

If consumer 4 knew that s/he is higher risk, then s/he would purchase insurance at the quoted premium, and s/he would be better off. This situation of the consumer’s misperception of risk raises the question of whether the insurer should make its information available to the consumer.

Objectively, big data has the potential to broaden access to insurance by removing information asymmetry – the elimination of the consumer’s information rent comes at no loss to society.

However, there is the potential for shifting the information balance in favor of the insurer, which would allow the insurer to earn an information rent – the broader availability of insurance remains.

Consumers with poorly calibrated risk perception would benefit from having as equally a precise read of their risks as the insurer. That’s why some have called for making the insurers’ big data available to the consumer or, more generally, to society.

This article appeared originally at GenRe.com.

Insurers still struggle with PDFs, images and handwritten documents. Countless human hours are required to manually extract the data into a machine-readable format. This process is known as ETL (extract, transform and load). Insurers that can maximize their ETL capabilities have a powerful competitive advantage. 

Enter optical character recognition, also known as text recognition. OCR, which converts text from scanned paper documents, photos, books and PDF files into a machine-readable format, isn’t new. What is new is coupling OCR with AI and machine-learning algorithms to reliably generate text that can be processed, indexed and retrieved. 

This technology can leverage existing data sets that comprise thousands of scanned documents and images already interpreted by humans. Smaller, manageable tasks are tackled first. AI and machine learning train and optimize algorithms, and over time the need for human intervention decreases in all departments.

Boosting Marketing and Sales

In employee benefits insurance, AI-enabled OCR can assist sales and underwriting teams by automatically extracting key details from RFPs and lengthy policy documents. It can automatically scrub RFP emails, multiple PDF documents, plan booklets and even scanned images of policy documents for key details. This data can then be loaded into the insurance company’s sales and underwriting systems, such as a quoting and rating engine, to create an initial “shell” quote in seconds.

Insurance companies typically have vast quantities of historical data in unstructured formats. You can call those vast quantities big data--or just an information goldmine!

AI-enhanced OCR can capture relevant data, enabling leaders to identify trends, make predictions and develop products in response to customer preferences.

Speeding Claims 

A quick, painless claims experience is key to maintaining customer loyalty and brand reputation. But you can’t sacrifice quality for speed. Insurers must have accurate data for claims processing to avoid overpayments, fraud and legal challenges and penalties.

OCR-enabled claims processing automation software can help speed claims with little or no human intervention without sacrificing quality. For example, clients can take a picture of the receipt from their dentist or car mechanic and send it to their insurer. OCR software can structure the data from the image of the receipt and confirm that the transaction is legitimate and process the claim–all without human intervention.

In 2016, Lemonade famously set a world record for the fastest-processed insurance claim. Their digital assistant, equipped with OCR, received a claim for a stolen $979 coat, checked the claim against the policy, ran 18 anti-fraud algorithms and made the payment in less than three seconds.

See also: Untapped Potential of Artificial Intelligence

Giving Customers a “Wow” Experience

Redundant data entry and drawn-out document-processing times contribute to a poor customer experience. McKinsey found that 30% to 40% of an underwriter’s time is spent on administrative tasks such as re-entering data or manually executing analyses. 

Today’s clients demand rapid turnaround for quotes, enrollment and claims. Insurers that fail to respond rapidly will lose clients.  

OCR reduces human errors and lets underwriters and other professionals focus on more valuable work. OCR also lets clients process their own documents, which can help insurers speed processes. 

Challenges and Best Practices

Like any emerging technology, AI-boosted OCR has limitations. Insurers should look to solutions that emphasize flexibility and configurability. Effective OCR applications can help insurers extract, transform and load text data and present it in any fashion they’d like, whether it’s tables, line items or any other format conducive to business needs.

Ineffective applications can result in losing time and money on technology that doesn’t work well.

Image quality is an important consideration. Thanks to machine learning, OCR applications can interpret more challenging data sources over time. To produce the best results, they should be fed a steady stream of high-quality image data with appropriate lighting, contrast and resolution. Camera applications that contain real-time image quality assessments can help users submit documents for clean processing.

OCR, like all AI-based technologies, requires continuing human inputs and oversight. The more data collected and verified by humans, the faster an OCR application will be trained. Transparency and explainability are always important when dealing with AI, and OCR applications must enable humans to easily understand AI-based outcomes and correct any problems.

See also: 20 Insurance Issues to Watch in 2022

How to Get Going

Insurers need OCR applications that are flexible, straightforward and customizable. But they don’t often have the required internal resources. As a result, many insurers are turning to industry partners with access to AI specialists, large data sets for training algorithms and cost-effective OCR services provided at scale. 

To take full advantage of OCR, insurers need modern sales, underwriting, policy processing and claims systems. Legacy systems can sharply limit the effectiveness of OCR and make integration costly.

Getting the most out of this emerging technology takes research and planning. But it’s becoming increasingly important to adopt it to stay competitive.

The MIT Technology Review's annual list of breakthrough technologies came out last week, and, as always, it's worth a look, both because of the general framing it offers about technology trends and because of some specific implications for insurance.

From an insurance standpoint, the two most directly relevant are what TR refers to as "the end of passwords" (yay!) and the growing availability of "synthetic" data to train AI in situations where there isn't enough real data available. But the other eight on the list of 10 are intriguing, too, especially for what they suggest about how we might be able to build on the technologies behind the mRNA vaccines against COVID to make additional, huge advances in healthcare.

The article in TR says, "New forms of authentication will finally let us get rid of [passwords] for good. Instead, we’ll use a link sent via email, a push notification, or a biometric scan.... The process is already underway. Enterprise-oriented companies like Okta and Duo, as well as personal identity providers like Google, offer ways for people to log in to apps and services without having to enter a password. Apple’s facial recognition system has taken biometric login mainstream. Most notably, Microsoft announced in March 2021 that some of its customers could go completely passwordless, and it followed up in September by telling people to delete their passwords altogether."

If you believe the analysis in "A Brief History of a Perfect Future" -- and I do, because I wrote it, along with longtime colleagues Chunka Mui and Tim Andrews -- the email links, push notifications and biometric scans are just the beginning. We'll all be able to increasingly triangulate to verify our identities based on the concept of "something you know, something you are and something you own," and the somethings you are and own will get increasingly numerous and sophisticated -- contact lenses that can identify you to your phone or computer, devices smaller than a grain of rice that you can swallow, even a DNA scan using an app on your phone.

Doing away with passwords will not only save us all time and eliminate the frustration that comes with forgetting and resetting passwords but will vastly improve cyber security. Phishing and other approaches to stealing passwords have provided hackers entree into many organizations, and those risks will diminish for individuals, companies and the insurers that cover them. 

This won't happen overnight -- none of what TR promises will come to fruition this year or even next year -- but the notion of a password-free world is, like most of the promised breakthroughs, close enough to reality that we all ought to be thinking about the implications and perhaps even gaming out the effects.

"Synthetic data" for AI is actually an idea that we've occasionally published on at ITL for a couple of years now -- for instance, "How Synthetic Data Aids in Healthcare" last August, "How to Put a Stop to AI Bias" in February 2021 and "Evolving Trends in a Post-COVID-19 World" in May 2020. (Yes, an article envisioning a post-COVID world almost two years ago was more than a little optimistic.)

Basically, if you're trying to train an AI to spot fraud, for instance, but don't have the massive numbers of actual cases that machine learning requires, you have an AI create lots of "cases" by synthesizing them out of the data you do have. There's an obvious limitation: The synthetic cases are only as good as the information you feed the AI that creates them. If the examples you feed the AI don't include a certain type of fraud, well, then the synthetic data won't, either. But AI developers are still finding synthetic data useful. 

As TR explains the concept of synthetic data: "Training AI requires vast amounts of data. Oftentimes, though, that data is messy or reflects real-world biases, or there are privacy concerns around the information included. Some companies are starting to create and sell synthetic data to avoid these problems. It’s not perfect, but it could be a better way to train AI."

Some of the breakthroughs that TR predicts could have significant implications for COVID, for future pandemics and for other diseases. For instance, TR says the massive investment in genomic sequencing that undergirded the mRNA vaccines against COVID is allowing for much better tracking of the virus and its variants.

The work has even produced "a pill for COVID." TR says, "Given to people within a few days of infection, an antiviral from Pfizer slashes the chance of hospitalization by 89%. The U.S. government has already placed orders for $10 billion worth of the new drug, called Paxlovid.... Pfizer’s drug could also be a ready defense against the next pandemic."

TR also holds out great hope for a vaccine for malaria, which kills more than 600,000 people a year, most of them younger than five. The GlaxoSmithKline vaccine, approved last October by the World Health Organization, is still in its early days. TR says, "It requires three doses in children between five months and 17 months old, and a fourth dose given 12 to 15 months after that. Given to more than 800,000 children in Kenya, Malawi, and Ghana, the vaccine had an efficacy of about 50% against severe malaria in the first year, and its effectiveness dropped dramatically over time." Still, combined with other measures, such as bed netting treated with insecticides, the vaccine "is expected to reduce malaria deaths by as much 70%, compared with the death rate in children given existing drugs," TR says. That could mean hundreds of thousands of lives saved each year, and second-generation vaccines are already on the way. The vaccine, which is the world's first for a parasitic infection, also holds out hope that others can be beaten back, too. 

Citing potentially even broader implications, TR singled out the work by DeepMind that has allowed AI to predict how proteins will fold themselves, removing the need to use a complicated chemical process that could take a year and cost $120,000 just to determine the shape of a single protein. Knowing the shape, and not just the sequence of acids in the protein, is key to understanding how proteins interact with other and, perhaps, with drugs that might halt or reverse a disease. I was so intrigued when DeepMind made its initial announcement in late 2020 that I wrote about it at the time, and the subsidiary of Alphabet has made remarkable strides since then.

The shape of some 170,000 proteins had been determined chemically over many years when DeepMind made its initial announcement. TR now says, "DeepMind has... set up a public database that it’s filling with protein structures as AlphaFold2 predicts them. It currently has around 800,000 entries, and DeepMind says it will add more than 100 million—nearly every protein known to science—in the next year."

Finally, TR holds out some hope on climate change. It describes promising developments in grid-scale batteries -- the sort that will be needed to ensure smooth provision of electricity as renewables are increasingly integrated into our electricity supply. TR also describes the opening of a plant in Iceland that is the largest to date that removes carbon dioxide from the air. Again, this is just baby steps. The facility can capture 4,000 metric tons a year -- about the output of 900 cars -- while the world produces some 51 billion metric tons. And the cost per ton removed is $600 to $800, while it needs to be $100 a ton or even lower to be broadly practical. But bigger plants are on the way, and, importantly, some companies are willing to pay today's high costs as they strive to cancel out their emissions. Those companies are providing crucial revenue that will help carbon removal efforts scale. TR even touts a major improvement in the magnets that are needed to contain the super-hot plasma required for nuclear fusion, which has long been the Holy Grail because it would provide us the energy of the sun without the radioactive waste that comes from nuclear fission.

Having written a great deal about climate in "Perfect Future," too, I think these technologies will kick in further down the road -- especially in the case of fusion -- than the others, so I won't go into them at length here. But I encourage you to read the TR piece. I always find their annual list stretches my thinking and opens up possibilities.

Cheers,

Paul

As a physician, I can’t help but view our industry through a distinctly patient-centered lens. Our very reason for existing is to help injured employees get well, ideally to get them back to their pre-injury status in days or weeks. When that’s not achievable, we aim to help each worker get the best possible medical outcome given their unique circumstances.

A strong technology foundation can help deliver that kind of patient-centered experience. At first glance, that may sound contradictory. After all, early efforts to automate human-centered processes in other industries often backfired, creating the impression that efficiency often comes at the cost of poor customer service. In today’s world, though, that doesn’t need to be the case. With the right technology, we can deliver much better outcomes than would otherwise be possible.

As time marches on, we’ve learned what works and what does not. Technology improves. We can apply it in new ways. We can work smarter instead of harder. In the context of claims management, we can empower our people to spot potential concerns in a worker’s case, identify the best providers for specific injuries and ultimately deliver better outcomes for injured workers.

Patient-Centered Thinking

When it comes to workplace injuries, the human element remains absolutely critical. A single incident can profoundly affect a person’s life as well as the lives of their loved ones. It can have a lasting impact ranging from chronic pain to disability, depression and even addiction.

By facilitating better medical outcomes, we can get injured employees back to work faster. We can steer clear of the pernicious traps that can lead a patient into a downward spiral, both medically and psychologically. Better, faster medical outcomes are a win-win for injured workers, for their employers and for insurers and policyholders.

We already know about many of the key factors that influence positive outcomes. When a medical professional is involved early in the process, for example, an employee is more likely to recover quickly. But what about some of the more nuanced factors? What are the ingredients for an optimal patient experience?

See also: How to Use AI in Claims Management

Working Smarter

To answer that question, we need to bring greater intelligence to the claims management process. We need to inform and empower the professionals who guide each claim toward a successful conclusion. Today’s technology can help us achieve that. Artificial intelligence (AI) and machine learning give us an edge that we’ve never had before. They help us understand what works and what doesn’t. They help us zero in on the things that matter most by constantly monitoring claims activity and generating customizable alerts for claims managers.

In fact, AI technology isn’t replacing the human element at all. On the contrary, this technology is making it possible to enhance patient care and improve medical outcomes. It’s helping claims professionals to be far more effective.

At Employers, for example, we’re using AI claims management technology from CLARA Analytics to identify the healthcare providers most likely to produce positive outcomes for specific types of patients and injuries. This goes well beyond a “gut feel” assessment; it gives us intensively data-driven recommendations to help identify the best possible provider for each claimant.

The results? When providers are ranked along a five-point scale and matched with specific patients and injuries, the top tier of providers helps workers get back on the job faster, with average medical payments of just over $1,100. Workers treated by providers in the bottom tier average medical costs of over $7,000 per incident. In cases involving indemnity for lost wages, data shows an average of fewer than 30 days of missed work for cases treated by top-tier providers. Injured workers treated by low-performing providers, in contrast, averaged over 570 days of missed work.

AI helps claims managers to be more effective, as well. Consider an example in which a physician’s notes highlight a specific medical complication that could lead to the patient’s prolonged absence from work. For a claims manager tasked with poring through mountains of medical records, injury reports and other documentation, that kind of detail can sometimes slip through the cracks. AI can spot meaningful information virtually the moment it becomes available, then bring it to the attention of an experienced claims manager.

Patient-Centered Care at Scale

This is good news for everyone involved. The even better news is that this works well at scale. AI helps us improve outcomes for injured workers, including those whose cases aren’t managed proactively by their employer. My organization frequently works with very small businesses that generally don’t have the resources to support comprehensive risk management programs. AI enables us to provide a level of high-touch care that employees at these companies would not otherwise receive.

If you ask the average person on the street about the societal impact of AI, you’re likely to hear concerns about the dehumanizing effects of technology and the replacement of workers by intelligent machines. The reality is far different. Properly applied, AI produces better medical outcomes, gets employees back to work faster, reduces litigation risk and lowers the costs associated with claims.

As a physician, that’s something I can feel good about.

As first published in WorkCompWire.