Current pricing trends in the directors’ and officers’ insurance market could yield premiums that prove inadequate to cover potential claims, owing to growing risks for which senior corporate directors and officers are held responsible.

After a decade of woefully inadequate rates in the U.S. D&O insurance segment that led to consistently unprofitable underwriting results, aggressive pricing increases in 2020 and 2021 finally made inroads for D&O insurers and led to much-needed market stabilization. These pricing increases also attracted capital to the market from strong underwriters with new, creative approaches to public D&O underwriting. 

However, beginning in the second quarter of 2022, D&O premiums began to decline materially in most sectors, as did retentions, resulting in much more favorable pricing for insureds. (Insured retentions tend to decline as markets grow softer, as clients can shop around for coverage.) Direct premiums written declined by nearly 10% in 2022, due largely to the economic environment that made M&A and IPOs less attractive. Less M&A and IPO activity diminishes demand for public D&O coverage.

D&O pricing in 2023 continued the momentum from the prior year, and renewal pricing was essentially flat, on average, by the end of the year. This has offered a reprieve for risk managers and brokers, but with pricing changes sinking below economic inflation, fear began to rise that the pendulum may be swinging back to rate inadequacy. 

See also: The 'B' Word: Bankruptcy and D&O

Third-quarter 2023 results show an uncertain picture for the D&O line going forward. Premiums declined for a sixth straight quarter on demand shifts and pricing changes; AM Best’s estimate for 2023 is $12 billion, which would constitute an 11% drop from the previous year. The number of IPOs declined again, to 72 in 2023, the lowest since 2009 during The Great Recession. With results leveling out and demand for transactional liability coverage declining, average pricing on D&O accounts has since fallen dramatically.

At the same time, the direct calendar year loss ratio for the line has also fallen, reflecting the realized benefit of the substantial up-pricing of renewals and new business from 2020 through early 2022. The direct loss ratio of 51.5 through third-quarter 2023 is on track to be the lowest in nine years, improving the industry’s underwriting performance. If results continue to be favorable and prior accident-year reserve development does not trend adversely, additional capacity may flow into the D&O market. That loss ratio does not factor in the potential impact that adverse loss reserve development could have on the net, bottom-line results for D&O insurers. With more lawsuits being settled or adjudicated as courts fully open following pandemic-driven closures and delays, the true profitability of policies underwritten is still maturing -- prior accident years 2017-2020 remain unknown.

AM Best is assigning a negative outlook to the D&O liability insurance market segment for 2024. Prior to this year, the D&O market outlook was captured under the professional liability catch-all, even as D&O represented the dominant line. That outlook was revised in 2020 to negative, so the new outlook on the D&O segment for 2024 is not a deviation from the previous few years.

An increase in players in the D&O market is a factor in the outlook, as the influx brings additional capacity and declined pricing amid growing exposures. Also included in the negative D&O outlook are expanded liability risks, as the business environment has become increasingly more complex and connected. Cyber and data breaches are becoming ever more pervasive in all different business sectors, particularly for businesses that rely on personal data. At the same time, new data protection laws and related disclosure requirements have added to the minefield senior executives must navigate to protect against potential litigation. The rise of generative artificial intelligence (AI) opens a whole new avenue for the plaintiffs’ bar, as well. Depending on how companies integrate AI into their decision-making process, corporate directors and officers could be accused of algorithmic biases in risk selection or underwriting decisions.

Inflation over the past 18 months, as well as interest rate increases to fight inflation and market volatility, have created negative pressure for stock prices and funding for privately held companies, which could result in shareholder and derivative actions. The bank failures of last year also have led to intense scrutiny of banks’ executive leadership and boards of directors. 

The rising cost of litigation owing to societal trends, such as broad interpretations of liability contracts, legal advertising and more plaintiff-friendly juries continues to drive increases in loss frequency and severity, and large, public-facing companies are particularly vulnerable. Litigation financing has been used for a range of lawsuits, including complex multidistrict and class-action litigation, in which D&O liability cases can specifically come into play. Third-party funders provide financial help in exchange for an interest in any recovery, and the financing can be offered to law firms that are unwilling or unable to fund a potentially protracted case or to help underfunded claimants directly. For D&O insurers, litigation funding pressures claims costs by driving up the magnitude of awards and settlements.

See also: Insuring Risks Amid AI's Constant Evolution

As the costs of litigation continue to rise, companies are becoming more likely to explore settlement opportunities even when the facts may be on their side. According to Woodruff Sawyer, settlement dollars during the first half of 2023 were up slightly more than 29% year over year, with settlements over $20 million up about 33% from the same period in 2022. These trends are apparent in underwriting results near year-end 2023.

If interest rates remain high and inflation persists, access to credit markets will become increasingly difficult, as companies struggle to balance the need for profits with prudent debt management. D&O insurers will need to better assess and adequately price for the ever-changing risk exposures while effectively focusing on evolving regulatory requirements.

Credit card fraud would seem to be a frequent, lucrative method of monetary gain for cybercriminals. While this was the case for many years, activities over more recent years have shown otherwise. The number show that cybercriminals were losing interest in selling and buying stolen credit card information. But lately, the global underground market for compromised cards seems to be on the upswing again, climbing steadily from the end of 2023 and continuing this year.

It's both surprising and sadly predictable -- a reminder that there's rarely a "done and over with" in cybersecurity battles.

See also: Cyber's Evolving Threat Landscape

Understanding the world of stolen credit cards

At Cybersixgill, we collect millions of pieces of data from the deep, dark and clear webs each day to better understand the actions of digital fraudsters who are continually seeking ways to enrich themselves, so we can help businesses protect themselves.

In 2019, more than 140 million compromised credit cards were listed for sale on underground markets. The number then plummeted each year, and by 2022, the total number of compromised cards dropped to 9.1 million. The price of cards dropped, as well.

In September 2022, we analyzed why the number of cards declined and attributed it to a fall in both supply and demand. On the supply side, it became more difficult and less attractive to compromise cards. For those on the demand side, security measures were making it harder to use a card in fraudulent ways. Our conclusion was that those who looked to profit from cybercrime pursued other methods, such as ransomware.

A change of course in late 2023

Through the first 10 months of 2023, average sales of compromised cards were about the same as in 2022. But beginning in November, sales shot up dramatically -- so much so that overall annual sales jumped 25% from 9.1 million in 2022 to more than 12 million in 2023.

These trends continued into January and February 2024, setting the number of compromised credit cards sold on pace to exceed 34 million this year, representing a major reversal after years of decline.

Seeing this data, we had to dig deeper and figure out what was going on.

What's the reason?

Our research shows that a particular dark web market -- one that had been dormant for a while -- recently re-opened. The re-emergence of this market has shifted the entire landscape of compromised credit cards; since mid-November 2023, it has listed more cards than any other market. In fact, it has accounted for about 65% of ALL compromised cards for sale and is solely responsible for the overall rise in cards.

See also: Cyber Insurance at Inflection Point

Why is this market bucking such a significant trend?

First, we posited that the universalization of EMV chips and better e-commerce site security made it more difficult to compromise cards compared with the heyday of Magecart attacks of 2018. (If you're not familiar, Magecart is a collective of cybercriminal groups that inject digital credit card skimmers on e-commerce and payment websites. These groups have been active since 2015 and gained momentum in 2018 with successful breaches of well-known brands such as British Airways and Ticketmaster.) It could be, however, that the operators of this renewed market discovered a new attack technique or carried out a major breach, enabling them to accrue a massive supply of cards.

Second, we considered that threat actors had far better opportunities to make money, specifically with ransomware and crypto-exchange hacking. However, things have changed over the last few years. With so much effort on the part of organizations and governments to fight ransomware, only the most sophisticated groups can carry out successful attacks. And crypto-exchange hacking appears to have declined significantly, as well.

Therefore, it could be that the operators of this market determined that carding was indeed their most lucrative and least risky option, and they reincarnated their old operation.

Now what?

It remains to be seen if recent activity in this fraudulent card market is an anomaly or if it represents a new trend. Will it be able to sustain the number of cards? Will other markets increase their supplies? Will new markets emerge?

Fortunately for consumers and issuers, the Payment Card Industry Data Security Standard (PCI DSS) is undergoing changes that promise to make credit card payments safer than they were previously. These broad changes, which represent the first major update since 2016, begin this month and go into full effect a year later.

Governmental action also plays an important role: Banks are under increasing regulatory pressure to minimize credit card fraud. Additionally, law enforcement may choose to pursue markets for stolen credit cards more aggressively, as they do with ransomware.

Consumers need to be wary of suspicious activity and check their accounts regularly to spot any charges they don't recognize. They should use a card with an EMV chip to prevent skimming attacks and be highly cautious so as not to fall for phishing email schemes.

In our increasingly interdependent digital world, cybercriminals are permanent residents, not temporary visitors. We need to acknowledge their presence and stay diligent.

Adopting new technology creates a lot of anxiety for insurance organizations. Cost concerns and the potential risks around service interruption often keep them in a holding pattern where they remain dependent on outmoded systems that lack the features and scalability they need to move forward. However, many insurance leaders have reached an inflection point.

Think about how quickly your customers’ digital experience expectations have skyrocketed in the last five years. The pandemic and “work from anywhere” lifestyle have forced many insurance organizations to take an unflinching look at all of their technology investments. The question becomes: Are these investments helping your business to grow and scale?

Recently, we surveyed hundreds of insurance CEOs, CTOs, CIOs, CFOs and COOs about the current state of insurance technology. We learned that, too often, their technology systems are not helping them meet their growth goals or delivering high-quality data to help them make decisions. We also learned that they are fully aware of the shortcomings of their current technology systems and are planning to make significant changes. 

Consider this: 76% of insurance organizations with over 5,000 employees juggle an average of six to 10 technology systems. More often than not, these are legacy systems with an average duration of use of four years. When it comes to insurance-specific agency and broker management platforms and policy administration systems, 41% of respondents admit to keeping the same system for five to 15 years. In short, their current investments are aging out.

As these insurance leaders go through their due diligence and issue RFPs in search of new, purpose-built software platforms, there are a few key things to remember. 

See also: How to Transform Your Core Platforms

The seven most important attributes of an insurance management platform are:

1. Scalability: Growth is the name of the game, and the right software platform should be able to manage an insurance organization’s growth at scale. That means the power to accommodate a growing number of users, agents, partners and policy transactions and increasing amounts of customer data without significant reengineering efforts. Further, the software platform of choice must have the ability to understand and streamline insurance workflows across the organization. Scalability also means being able to incorporate acquisitions, including the addition of a different operating model than the acquirer. 

2. Configuration and Flexibility: The insurance industry is too complex and varied for a one-size-fits-all approach to software platforms. This is where the platform-based approach can bring tremendous value, with configurable features and modules that can meet a specific insurance business need. This flexibility enables businesses to create unique solutions without starting from scratch.

3. Interoperability and Ecosystem Building: With the growing popularity of insurance ecosystems, software platforms must be built for today’s technology partnerships. For example, platforms with open API architecture have the power to support integration with other software systems and services, enabling seamless data exchange and interoperability. This interoperability reduces data silos and enhances collaboration within and between organizations.

4. Cost-effectiveness: Leveraging a software platform can be more cost-effective than developing a custom solution from scratch or combining multiple point solutions that aren’t built for the insurance sector. It’s also less risky given the likelihood of failures of in-house software builds. Platforms often offer subscription-based pricing models or pay-as-you-go options, reducing upfront costs and providing predictable expenses.

5. Support for “Work from Anywhere:” Cloud-based insurance management platforms are accessible from anywhere with an internet connection. This accessibility expands the reach of applications and services, catering to a broader audience and enhancing user engagement. It also helps to support remote work for agents and brokers.

6. Data Insights and Real-Time Analytics: This is probably the most vital feature of an insurance software platform, solving a major challenge for insurance organizations. Our research shows that data quality issues are the number-one concern (41%) of insurance leaders regarding their current technology. The built-in analytics and reporting capabilities of platforms provide valuable insights into user behavior, trends and performance metrics. These insights empower businesses to make data-driven decisions and optimize their operations.

7. Security and Compliance: Many platforms invest heavily in security measures and compliance certifications, such as GDPR or SOC 2, to ensure data protection and meet regulatory requirements. This commitment to security and compliance instills trust and confidence among users and stakeholders. Compliance with regulatory requirements is another critical aspect of the insurance industry. Software platforms can help ensure compliance by incorporating features such as automated reporting, audit trails and adherence to industry standards and regulations.

See also: 5 Must-Haves in Agency Management Systems

Overall, software platforms offer a range of benefits that align with the evolving needs of insurance businesses in today's digital landscape. Some software platforms allow insurance companies to automate certain processes, from underwriting and policy management to claims processing and customer service. This automation reduces manual effort, eliminates the need to re-key information, speeds up processes and minimizes errors. All of this leads to improved operational efficiency and a better customer experience.

For example, Atlantic Global Risk, a forward-thinking specialty insurance broker that specializes in crafting complex insurance solutions for legal, tax and credit risks, recently invested in a software platform that replaced three separate technology tools. Before making this technology upgrade, Atlantic Global Risk was juggling multiple applications for different operational needs, including a CRM for sales leads, an enterprise cloud platform for managing associates, policies and documentation and an agency management application for invoices and customer records. The challenge was that these applications were not specifically designed for the insurance industry, leading to inefficiencies and data fragmentation across departments. The company was able to streamline workflows, eliminate data silos caused by having so many different applications that could not talk to each other and empower its staff with access to a single source of information across the company.

With industry-specific software platforms, insurance companies can offer digital self-service options to their customers, such as online policy purchasing, claims submission and account management through a customer portal. This improves the overall customer experience by providing convenience and accessibility.

Insurance organizations are turning to software platforms to stay competitive in a rapidly evolving industry, improve operational efficiency, empower their employees, enhance customer experience and drive innovation.

When two earthquakes made front page news in the past week, the one near New York City seemed to get more attention because, well, New York City.... But the earthquake that hit Taiwan is far more important, not just because of the billions of dollars of damage it caused but because of what it didn't cause. 

The 7.4 earthquake (on the Richter scale) in Taiwan unleashed some 8,000 times as much energy as the 4.8 earthquake that shook the New York City area, yet the death toll was only 13 people, and only one of those was killed in a building collapse. The rest were killed by landslides and boulders. 

How was Taiwan so well prepared that a massive quake killed only 13 people, 25 years after a similar quake there killed almost 2,500? Therein lies a tale – and an endorsement for the Predict & Prevent model for insurers and their clients.

The short answer about why Taiwan withstood the earthquake so well includes luck. The earthquake occurred just off the coast of a sparsely populated, mountainous area that absorbed much of the force before it reached Hualien County and its population of about 320,000. 

But Taiwan has also been bracing itself, literally, for a quake like this for 50 years.  Taiwan has been incorporating earthquake resistance into building codes since 1974 and has been continually strengthening requirements based on lessons learned from quakes around the world. 

Taiwan has enforced the codes, too. After the Chi-Chi quake in 1999 killed almost 2,500 people, inspections found that many developers and builders had taken shortcuts or used cheaper materials to save on costs. Many were thrown in prison, as well as fined heavily. 

Thousands of buildings were reinforced, including more than 10,000 schools. When a strong quake hit Hualien in 2018, building regulations were tightened further. 

The New York Times reported: "The government had also helped reinforce private apartment buildings over the past six years by adding new steel braces and increasing column and beam sizes.... Not far from the buildings that partially collapsed in Hualien, some of the older buildings that had been retrofitted in this way survived."

The Guardian wrote that, "with more than 4 million homes in buildings that are at least 30 years old [in Taiwan], according to official statistics, the government has also introduced subsidies for local authorities to inspect and upgrade any buildings that do not meet modern safety standards." 

The BBC described the effects of Taiwan's preparations as stunning: 

"Just over a year ago, we saw earthquakes of about the same magnitudes striking Turkey and Syria, causing the deaths of more than 50,000 people. These countries, of course, had far fewer resources. But when a much smaller 6.7 magnitude quake hit the city of Christchurch in New Zealand in 2011, almost the entire city center was flattened."

By contrast, in Taiwan, "A hundred meters beyond the police cordon, the streets of Hualien look entirely normal. Shops and cafés are open, traffic is flowing. Drive through the city, and if you didn't know a big quake had struck days ago, you wouldn't guess it." (The police cordon the author mentions is around the Uranus Building, a 10-story office building that was knocked to a precarious angle and whose image became the dominant one from the quake.)

The Times offered a similar description: 

"It was possible to walk for city blocks without seeing clear signs of the powerful earthquake. Many buildings remained intact, some of them old and weather-worn; others modern, multistory concrete-and-glass structures. Shops were open, selling coffee, ice cream and betel nuts. Next to the Uranus Building, a popular night market with food stalls offering fried seafood, dumplings and sweets was up and running by Thursday evening," the day after the quake.

Most importantly from an economic standpoint, operations at TSMC, the world's most advanced semiconductor manufacturer, only needed to suspend operations briefly and reported minimal damage. The precision of its work is mind-boggling. The lines being etched into the surface of the chips and the lines of metal being deposited on them are as thin as 13.5 nanometers—a nanometer being a billionth of a meter, or about 100,000th the thickness of a sheet of paper. So to not have much of anything knocked out of whack suggests extraordinary precautions—and great benefit to the world economy, given the roles that TSMC chips play in just about everything . (I wouldn't be surprised to hear of the loss of some work in progress, because the making of a chip requires hundreds of steps and perhaps 11 layers of metal, but TSMC has yet to report any lost production.)

Taiwan's extraordinary preparations won't be easy to duplicate elsewhere. Taiwan sits on a series of faults, and everyone knows it, so there isn't any need to convince people of the peril, in the way that there can be with potential natural disasters in other parts of the world. Even if you know you're facing an active hurricane season in the Southeastern U.S. – and forecasts keep getting worse -- there is an awful lot of territory that could be threatened, so you may not need feel the need to make extraordinary preparations. Besides, threats such as wildfires and convective storms may migrate from year to year, rather than staying constant, like the earthquake threat in Taiwan.

In addition, Taiwanese are conditioned to be alert to threats, not just because of earthquakes but because of the possibility of military action, even invasion, by China that has hung over the island nation for decades and has been intensifying in recent years. So Taiwanese respond when they get a phone alert about an earthquake, based on the nation's sophisticated early warning system, in a way that Americans getting an evacuation order in advance of a hurricane may not. Those earthquake warnings may be only seconds ahead of the shaking, but they can still give lots of people a chance to find at least a bit of shelter. 

Still, the Taiwanese earthquake shows the power of the Predict & Prevent model for insurers – spotting risks ahead of time, warning people about them and working with people to prevent losses, rather than taking the traditional approach of primarily focusing on paying claims after the loss occurs. 

And there are steps we can take, even if it'll be hard to match Taiwan's performance any time soon. 

"The Fortified program that's done in conjunction with Alabama and the University of Alabama's Risk Center is an ideal example of what communities can do." Sean Kevelighan, CEO of the Triple-I, said in a conversation we had late last year. "Fortified is a certified home building process developed by the Insurance Institute for Business and Home Safety, and it helps us withstand the likes of hurricane-strength winds or other natural catastrophes. And Alabama has embraced this. I think they are a unique example in the Gulf Coast community. Neighboring states arguably have many more issues because they haven’t focused as much on resilience.

"The program has been featured in your alma mater, the Wall Street Journal, as something that is working. We're hoping [for] more of these types of community-based projects."

If we don't figure out some way to prevent some of these massive losses that natural catastrophes are causing, the prospects for profitability are, at best, clouded and, at worst, bleak.

Pete Miller, CEO of The Institutes (ITL's parent), wrote recently:

"Last year, U.S. P/C insurers incurred a $21.2 billion net underwriting loss, only slightly improved from a $24.9 billion underwriting loss recorded in 2022. Roughly $65 billion in natural catastrophe losses hit P/C insurers last year. The August 2023 Maui wildfires alone contributed an estimated $4-$6 billion in damages.

"As the severity and frequency of losses continues to increase exponentially, so does the cost to repair damages after a loss occurs. This is making insurance’s traditional approach of detecting and then repairing after a loss no longer economically viable."

With that sort of outlook, I'm hoping we can learn to be more like Taiwan -- with lots of the recent luck and as few as possible of the natural disasters.

Cheers,

Paul

P.S. I encourage you to consider submitting an application to be considered for a Global Innovation Award, which ITL is handing out together with the International Insurance Society again this year. We are recognizing the Insurer of the Year in Property and Casualty, the Insurer of the Year in Life, Health and Retirement and the Insurtech of the Year in Predict & Prevent. The awards will be handed out at the IIS's Global Insurance Forum, being held in Miami this year in November. 

For more information on the awards and on last year's winners (AIA, PAK Insurance Programs and Swiss Re), click here. You can apply for this year's awards here. Note that the deadline is April 17, so it's coming up fast. 

We had a great representation of innovators last year, the first year we gave out the awards, and are hoping to hear from even more of you this year, as we try to encourage and recognize the impressive innovation happening within insurance and risk management. 

There is no great mystery about what determines insurance premiums – it all comes down to market conditions and risk profiles. However, many policy applicants have a huge misconception that can inadvertently increase their rates.

The role of an insurance agent is akin to that of a matchmaker – the goal is to connect businesses with suitable policies that address their specific needs. To make a successful match, insurance agents and underwriters require information. Generally speaking, the more information, the better. 

Many business owners, however, think it is somehow beneficial for them to “hide the ball” from insurers and obfuscate details of their business. 

This myth of secrecy is not only incorrect but incredibly damaging. When there is a dearth of information about a potential client, underwriters are forced to fill in the blanks as best they can. Underwriters are not an optimistic bunch. When confronted with limited information, they will not assume the best. Instead, they will make conservative assumptions that will likely result in higher premiums than would have been the case if a more transparent approach was taken.

See also: The Need for Transparency in Underwriting

While insurance agents are typically the face that clients see the most, underwriters are working behind the scenes to:

• Build Risk Profiles: Underwriters scrutinize various aspects of a business, including its industry, operations, financial stability and claims history. Then they assess the likelihood of future losses based on these factors.
• Understand the Client's Risk Management Practices: Underwriters appreciate companies with robust risk management protocols, as they are less likely to incur significant losses. These practices include safety measures, loss control strategies and disaster preparedness.
• Customize Coverage: Underwriters initially work with generalized risk profiles, but the real magic happens when they gain specific insights into a business. The more details they have, the better they can tailor coverage. Imagine a custom-made suit versus an off-the-rack option—the former fits perfectly.
• Tell a Story: Underwriters and insurance companies want to know the story of their clients. When potential clients volunteer information and give generous insights into their goals, strategies and relevant data, they make the job of an underwriter much easier. 

How to Bridge the Knowledge Gap

All this is to say that when insurance companies know more about applicants and have a high degree of confidence about the levels of risk at play, they will offer more competitive rates, terms and coverage. Businesses applying for coverage can do themselves a huge favor by abandoning the myth of secrecy and helping their agent or broker to bridge the knowledge gap and provide information for underwriters to use in their calculations.

The more specific information about a business and its risk management practices that the underwriter knows, the more accurate the risk profile becomes. A good risk profile makes an applicant more desirable in the insurance marketplace. Businesses with solid risk management and loss control practices will be offered much better policies than those with generalized average risk profiles or incomplete information. This means that when an underwriter has a question about some aspect of your business and that unknown isn’t addressed, they will be likely to assume that a hidden risk is present, and policy offerings will be priced accordingly.

Examples of key steps a business can take to create a solid risk management strategy that will appeal to insurers and their underwriters include: 

1. Build a company-wide culture based on safety
2. Manage claims efficiently to keep costs down
3. Pinpoint exposures and cost drivers
4. Identify the best loss control solutions to address risks specific to the business
5. Create a business continuity plan to account for disasters and other unpredictable risks

These five items help you control your claims history, which plays a huge role in determining insurance costs. Businesses that take risk management seriously will stand out in the insurance market and become better, safer and more effective.

See also: What Makes Insurance Invoicing Different

Ditch the Secrecy, Get a Better Policy

If information is hidden and a claim occurs, that information will likely come out, whether the insured party likes it or not. This undesirable scenario could be grounds for rejecting the claim or for non-renewal when the policy term ends. The business could need to pay the cost to address the claim out-of-pocket and will likely face higher rates when shopping around for a new policy.

Today, applicants for insurance face the most unfavorable market conditions in decades. Everyone is experiencing increases, even those with a good loss history and no claims. The market reacts to industry trends, and increased claim expenses due to inflation and a recent uptick in third-party litigation are currently driving significant price increases. 

Despite this, insurance costs can still be somewhat controlled by having a strategic risk management plan and a transparent relationship with your insurance agent or broker. Their role can also extend beyond merely selling policies – in fact, they can act as a valuable resource for risk management advice and best practices.

Insurance premiums aren’t arbitrary numbers pulled from thin air. They reflect a careful analysis of risk. Applicants who take an active role in bridging the knowledge gap will almost always get a better outcome than those who buy into the myth of secrecy. Those who want a well-fitted insurance policy that protects their business effectively should embrace transparency, share relevant details and let underwriters work their magic. 

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As the timing of interest rate drops and a “soft landing” for the economy continue to be debated, new capital stack solutions, which offer organizations multiple ways to meet financial requirements, are needed. Enter surety.

Surety bonds guarantee compliance, payment or performance of a specified act, providing tremendous appeal as a letter of credit alternative to meet finance assurance requirements. However, this is not the only advantage. Unlike letters of credit, these bonds are considered contingent liability versus debt and allow businesses to avoid using cash or return cash deposited with a third party. 

While surety has traditionally been used in the construction industry, since the passage of the Inflation Reduction Act (IRA) it has seen rapid growth. According to a new AM Best report, in 2022, the surety market grew 16%, generating $8.6 billion in premiums, which resulted in $2.3 billion in underwriting profit. The first three quarters of 2023 continued the trend, with premium revenue increasing by 11% as insurance companies expanded into this business line. 

With more capacity available, the real estate, renewable energy and healthcare sectors are areas where increased surety usage is anticipated in 2024.

Making Real Estate Projects a Reality

Following years of favorable conditions, arguably no industry has been more affected by high interest rates than real estate. Fortunately, surety has several applications to help mitigate these impacts. 

One area for surety’s use is lease bonds, which ensure payment under a rental agreement. This is especially important as the office sector and retail markets continue to show vulnerability. Other non-traditional uses include municipalities requiring investor guarantees for the completion of rehabilitation projects and commercial real estate owners using the financial tool as a performance and payment assurance in their efforts to have buildings meet carbon emissions standards.

While new construction still leverages surety in the most traditional sense, it’s the expansion of new sectors that is propelling growth here, too. For example, with the private development of data centers, surety’s use is spiking.

See also: Insurance Is Not a Commodity

Powering the Energy Sector and Solving the Connection Queue

With the development of new renewable energy projects fueled by the IRA and corresponding increased financial deposit requirements, the energy surety market has grown significantly.  

A pain point in IRA project development and the main area where surety has been used is to solve issues tied to the backlog of connection cubes. To understand this connection issue, consider a new wind farm project being launched with funding from the IRA. That project will undergo the complicated process of being connected to the energy grid. New connections must be built to handle the additional needed capacity, and the magnitude of this connection issue is huge. The delays in connecting these new projects, coupled with rising costs, have prevented IRA projects from doubling the U.S.’s electricity capacity.

In response to this issue, utility companies are increasing financial deposit requirements for new energy projects and moving to first-ready, first-serve models. Third-party companies can use surety to ensure these obligations are met.

Another federally funded program that has sparked new projects for the surety market to capitalize on is the Broadband Equity Access & Deployment Program (BEAD). BEAD provides $42.45 billion in funding to expand high-speed internet access across rural communities in the U,S. In the latter half of 2023, the BEAD program amended its letters of credit requirements, allowing surety bonds to be used. 

The positives of using surety, in this case, are clear – these bonds boast lower credit fees and offer added flexibility. However, in 2024, each state must decide if it will accept surety, so the extent to which the market will grow is still to be determined.

Meeting the Financial Demands of the Healthcare Industry

Another area of growth for surety use is healthcare. Early this year, the financial guarantees needed to participate in federal programs (Medicare and Medicaid) increased from 3% to 4%. This opened the door for healthcare providers and health systems to use surety bonds to negotiate better terms within their contracts. For context, the bond rates are fixed at around 1% to 2%, while the average letter of credit rates are variable, with an average of about 4% annually.

As the cost of healthcare increases alongside participation in Medicare Advantage or state-run Medicaid programs, health plans are moving toward value-based care models, which require participants to post financial guarantees, including surety, to meet contractual requirements.

See also: Blockchain's Future in Surety Industry

What Brokers Should Consider 

Brokers need to be able to navigate the upswing coming into the market. Working with customers, brokers must stay abreast of material changes to underwriting guidelines, new entrance requirements and the acceptance of surety from new industries or counterparties that have not historically accepted it. As the market continues to expand, brokers have an opportunity to guide their customers into a new era backed by surety. 

To the uninitiated, no-code and low-code software solutions sound similar. Some even think the two terms are interchangeable. But the reality is far more complicated. Choose one type of solution, and you are likely to get the benefits of faster digital product development at less cost. Choose the other, and you could suffer a serious case of buyers’ remorse.

The evolution of coding in insurtech

All systems, going back to the days of mainframes, came with certain configuration capabilities. Traditionally, if you wanted to change a system’s core behaviors, you needed to know how to write code in a programming language. These languages matured from the early days of Basic, COBOL and Pascal to modern-day languages like Python and JavaScript. Making significant changes with coding often took weeks or longer. In fact, where coding language is concerned, little has changed in terms of speed of process.

No-code solutions trace their origins back to the 1980s and the advent of Microsoft Excel, which many consider the first program that enabled developers to manipulate data without code. But enterprise-grade no-code platforms didn’t emerge until the early 2000s.

Today, no-code platforms exist across all industries. By definition, no-code in insurtech is exactly what it sounds like: It allows you to build workflows, screens, documents, products and rates without knowing any programming language. This means you don’t have to translate your knowledge to developers and other IT professionals. Instead, you are empowered to make changes yourself. And because you don’t need to know coding, you can make substantial updates in mere minutes.

True no-code platforms are designed to be no-code from the start. You can’t take a legacy, coding-heavy platform and make it low-code without rebuilding the entire system. Unfortunately, this is where the waters have become murky.

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Why confusion reigns

Over the past few years, in response to the no-code movement, we have seen the term "low-code" rise in prominence. It sounds enticing. After all, low brings the connotation of less, so you would assume low-code platforms require only a small amount of coding.

That assumption can be, and often is, wrong. By definition, any platform calling itself low-code still requires coding at some level. This means the do-it-yourself service that comes with no-code simply isn’t the same in a low-code environment because some level of coding — and the technical training required to both understand and implement coding language — is required. 

Low-code solutions exist on a spectrum. Some low-code software may have pre-prepared forms that don’t require much coding. Others are old-fashioned, code-rich systems where even the smallest change needs code; hence, they unashamedly wear a marketing disguise.

Some longtime incumbents use the term "low- code" to reintroduce dated technology that can require heavy coding over months or years — along with millions of dollars — to implement. These vendors may believe using a term like "low code" makes their software feel modern. But when legacy providers use this terminology, it perpetuates the unfortunate overpromise-and-underdeliver reputation of older core systems that have already left a bitter taste in the mouths of insurance leaders.

Why knowing the difference matters

When you choose a no-code platform rather than a low-code product, you stand to gain benefits in these three essential areas.

1. Staffing. All systems that require coding — even if they’re called low-code — need support from IT professionals. This is both a concern and an expense for many insurers. Experienced coders are hard to find, and recruiting and retaining your own team of developers year over year is expensive. If you choose low-code and don’t have your own developers, you will need to rely on your vendor, which can add to your costs.

With no-code, you don’t need an IT team or even vendor support. You can empower your best, detail-oriented strategic thinkers, including spreadsheet gurus and product managers, to make changes and upgrades. These users are already embedded within your business, understand your processes and eliminate the need for additional headcount. 

2. Training and implementation. Low-code platforms require extensive training before your IT team can start to use the system, which lengthens implementation timelines. In contrast, no-code platforms offer a much lower barrier to entry. You can complete training in days rather than months. 

3. Cost structure. This benefit may not seem obvious at first, because no-code and low-code solutions may carry similar upfront costs. However, the cost savings differ significantly over time. With low code, every change to a form and each adjustment in coverage may cost you thousands as well as numerous days or weeks to adapt. With no-code, your professionals can make these changes themselves, in real time, with no extra expense other than a few hours spent making the adjustments.

Can no-code really solve complex problems?

Some insurers believe no-code isn’t suited to build the type of game-changing software they need. Yet that is not entirely true.

The capabilities of no-code platforms vary by vendor. Some are workflow management tools designed to solve only certain steps in the insurance value chain. Hence, they are not built to scale and handle complex processes like managing regulatory changes across 50 states or building complex rules to cover out-of-sequence endorsements.

However, other no-code platforms are complete insurance-specific platforms, designed to meet an insurer’s needs from end to end. These systems are purpose-built to manage the intricacies of carriers, brokers and MGAs. Best of all, because you don’t have to translate your knowledge to IT teams, you can start building products and writing premium with end-to-end no-code solutions immediately, creating a thoughtful speed-to-market process that is increasingly required in the competitive landscape of insurance.

What can you accomplish with a no-code platform versus a low-code or fully coded solution? One of the most powerful no-code use cases is creating complex and non-standard insurance products. Traditional and low-code solutions often lock insurers into standard product lines, such as personal auto or commercial property. By contrast, no-code gives you a clean slate, allowing you to build solutions that manage non-traditional risks or package policies.

Flexibility is the secret sauce in this type of accelerated product development lifecycle. With no-code, you can capture data in the user experience all the way through to rating, documents and integration, all without altering a single line of code.

See also: Why Are Digital Payments So Clunky?

Can I leverage AI and IoT with no-code?

If you are looking to embrace the power of artificial intelligence (AI) and Internet of Things (IoT) devices, a no-code platform is an optimal choice for two reasons:

1. Ease of integration: Building integrations is one of the most time-consuming processes of traditional software implementations — and most legacy vendors have extensive implementation backlogs. No-code systems accelerate and simplify the process by using built-in application programming interfaces (APIs) and webhooks to connect with other apps. A no-code platform with a rating system, for example, may have a point-and-click API step that lets you call data from an external IoT source, such as a telematics device. As a result, you can complete this type of integration in hours or days, not weeks.

2. Faster AI implementation: The large language models (LLMs) that empower AI-enabled solutions are only as accurate as the data used to train them. No-code eliminates the need for complex data manipulation, empowering you to integrate and use AI solutions faster and more accurately.

How do I know I’m not getting fooled?

You would not drive off the lot with a new car before you looked under the hood. The same is true when evaluating no-code and low-code solutions. Five best practices to include in your decision-making process are:

• Rely on show, not tell. Ask vendors to show you exactly what it looks like to set up coverages, rates, documents, quoting flow portals and other workflows in their system. If any part of the workflow requires involvement from IT professionals on their team or your team, then their solution is low-code and not no-code.
• Perform a stress test. Create a complex use case that covers the product development lifecycle from start to finish. Then ask the vendor to complete it. Along the way, assess how much of the journey — from the front-end user experience through to integration — your business users can manage and how much support you need from your vendor.
• Request customer success stories. Seek tangible examples from companies within insurance. Then, ask some key questions: How long did implementation take? What was the timeframe for each successful project? How much was the customer able to do without technical resources from the vendor?
• Connect with your peers. As you narrow down your choice of solutions, reach out to peers who are already working with the same vendor. Ask how they divide up roles and responsibilities on their team. If they tell you they need IT resources to perform updates, then you will know the solution you are considering is low-code and not no-code.
• Choose your support tolerance. An end-to-end no-code platform will give you flexibility to build products on your terms. You should have a choice of doing everything yourself or using your vendor if you might prefer to do so. If a vendor says DIY isn’t possible, then you are not looking at a no-code platform.

Surprise billing, also known as balance billing, is a controversial practice in healthcare where a patient receives a bill from a healthcare provider that was not previously disclosed or expected. This usually happens when the patient receives care from an out-of-network provider, not covered or fully covered by their health insurance.

Even if a patient goes to an in-network hospital, they may be treated by an out-of-network doctor, specialist or subcontractor without their knowledge. Later, they receive an unexpected bill, which can be substantially higher than the usual cost.

This practice has garnered significant backlash because it places a heavy financial burden on patients. Some states in the U.S. have legislation to prevent or limit surprise billing, and proposed federal regulations also aim to address this issue nationally. Providing transparent pricing and improving networks could also help prevent surprise billing and protect patients from unexpected medical costs.

Surprise billing represents a significant issue in the healthcare system that necessitates urgent attention and resolution for several reasons:

1. Impact on Financial Stability: Surprise bills can threaten a patient's financial stability. They are an unexpected burden on the patient, who might already be dealing with health-related issues.
2. Insurance Coverage Discrepancies: Even when patients are diligent about using in-network providers, they can still receive out-of-network care unknowingly, particularly in emergencies or when an out-of-network specialist is called in. Despite having insurance, patients can end up bearing heavy, additional costs.
3. Lack of Transparency: The healthcare sector is often criticized for its lack of price transparency. Patients are rarely made aware of the cost of care beforehand. High healthcare costs combined with this lack of transparency can discourage patients from seeking necessary treatment in a timely manner.
4. Damage to Patient-Physician Trust: Surprise billing can lead to mistrust in the relationship between patients and healthcare providers. 
5. Risk of Medical Debt: Surprise bills are a leading cause of medical debt, which is a major issue within the U.S. healthcare system. Medical debt may lead to bankruptcy.

Regulatory and technological solutions, like data science and analytical models, can contribute significantly toward a resolution on surprise billing.

See also: Data Science Is Transforming Public Health

The Power of Data Science in Healthcare 

The ability of data science to analyze vast amounts of data has revolutionized many industries, including healthcare:

1. Prediction and Diagnosis: Data science aids in predicting diseases by analyzing patient data, medical records, and other factors such as lifestyle habits, genetic information and environmental factors. Predictive algorithms can help identify individuals at risk of developing specific diseases, allowing early intervention or preventive measures. Similarly, machine learning algorithms are being used for diagnostic purposes, such as figuring out patterns in MRIs, X-rays and other medical images.
2. Patient Care: Real-time monitoring and electronic health records (EHR) can be analyzed to provide personalized care to a patient. Predictive analytics can also help identify patients at high risk of serious complications and enable proactive care.
3. Billing Management: Data Science can significantly streamline billing management by flagging billing errors, reducing fraudulent claims, predicting claim approval probabilities and taking necessary measures to prevent surprise billing.
4. Healthcare Operations: Data science is also used to optimize healthcare processes and operations. It can help hospitals and clinics to forecast patient influx, manage resources, plan staff rotations, and reduce patient waiting time, resulting in increased efficiency of healthcare delivery.
5. Drug Discovery: Data science also plays an integral role in the pharmaceutical industry. It helps in accelerating drug discovery, personalizing treatments and predicting drug responses based on the data generated from genomics and proteomics.

By harnessing the power and potential of data science, the healthcare industry can make significant strides toward improving patient care and delivery and revolutionizing healthcare operations.

Integration of Predictive Analysis and Surprise Billing

In healthcare, predictive analytics can serve as a powerful tool to forecast scenarios that may cause surprise billing, thus enabling action to prevent undue fiscal stress on patients.

Predicting Out-of-Network Scenarios: Predictive analytics can help identify potential out-of-network scenarios. By analyzing insurance data and healthcare provider networks, machine learning algorithms can predict whether a healthcare provider falls within a patient's network. Prior information on whether a provider is in or out of network can avoid instances of surprise billing.

Cost Prediction for Procedures: Data from a patient's medical history and other demographic details can be analyzed to predict the possible cost of a healthcare procedure. Predictive models can be trained on historical data, encompassing variables such as age, past medical conditions, severity of the current condition, localit, and insurance coverage, to provide an estimate of the potential cost. This helps patients make informed choices about their treatments, mitigating the likelihood of surprise bills.

Risk Factor Analysis: Predictive analytics can also scrutinize patient data to identify patterns and correlations between various risk factors and healthcare costs. It can help highlight aspects of a patient’s medical history or lifestyle that may lead to costly medical treatment.

Real-Time Predictive Analysis: Real-time analysis can be employed to alert an individual in the event of a potential surprise billing situation. For instance, by analyzing real-time healthcare data and insurance details, the system can alert individuals about potential out-of-network providers or services that are not covered by their insurance plan.

While data science can provide a method to predict and avoid surprise billing scenarios, the key lies in using these techniques proactively and ensuring they are integrated into the current healthcare system. Accurate prediction models, timely alerts and easy access to a patient's complete healthcare information might serve as crucial steps in our journey toward combating the issue of surprise billing.

See also: Maximizing AI's Impact in Group Insurance

Cost Estimation Models and Their Importance

Advanced machine learning algorithms can develop dependable cost estimation models, enabling patients to make informed decisions regarding their health.

The process unfolds in several stages:

1. Data Gathering: This is the first step, where relevant information is collected from diversified sources like patient health records, hospital records, insurance details, demographic data and even health indicators from wearable devices.
2. Model Training: Once the data are gathered, machine learning models are trained by feeding them these data. The models learn from various inputs (age, disease type, comorbidities, demographics, etc.) and their corresponding output (cost).
3. Model Testing and Validation: The trained model is then tested against a separate dataset to assess its accuracy. It's essential to validate the model's predictions against actual costs to ensure its reliability.
4. Deployment and Prediction: Once the model passes the testing and validation phase, it's deployed in a real-world setting. When a patient's information is fed into this model, it can predict the probable cost of their healthcare procedure.

The use of machine learning algorithms in generating reliable cost predictions offers several crucial benefits to patients:

Enhanced Transparency: By predicting the estimated cost of a medical procedure or treatment beforehand, these models bring about transparency in the healthcare billing process. They empower patients to comprehend the expenses they are likely to incur, reducing instances of surprise billing.

Informed Decision-Making: By offering insights into probable procedure costs, these models enable patients to compare options and make informed decisions about their care. In cases where multiple treatment paths are available, cost prediction models can help patients and healthcare providers choose the most cost-effective one.

Trust Building: When patients are informed up front about the potential costs associated with their care, they may enter their treatment journey with more confidence and trust in the healthcare system.

Integrating data science and machine learning in healthcare can revolutionize the way patients navigate their treatment journey, fostering a system where hidden costs are minimized, and transparency is maximized. By anticipating and addressing financial concerns upfront, these sophisticated models contribute to alleviating the stress and anxiety often associated with medical billing.

Leveraging Data Science for Eligibility Verification

Data science and machine learning techniques play a pivotal role in automating eligibility verification processes in healthcare, ensuring services are covered under a patient's insurance policy before those services are used.

The process typically involves the following steps:

1. Data Integration and Sharing: Data from insurers, healthcare providers and patients are consolidated into a unified system. This includes patient insurance information, policy coverage details and healthcare provider prices and services.
2. Automated Insurance Verification: Once this data is integrated, machine learning algorithms are then leveraged to automatically verify a patient’s insurance coverage. Each time a patient schedules an appointment, the system refers to the consolidated insurance data to determine if the patient is insured and has enough coverage for the services required, and determines what the out-of-pocket cost would be. In real time, the system can provide information about the insurance validity, policy details, covered benefits, co-payment and deductibles.
3. Communication: The system can automatically generate alerts for healthcare providers when it identifies potential issues with insurance coverage for a particular service. Similarly, it can provide patients with upfront cost estimates, potential out-of-pocket expenses and flagged services that are not covered by their insurance.
4. Continuous Learning and Adaptation: Using advanced machine learning algorithms, these systems can continue to learn and adapt to changes in insurance plans, adjustments in healthcare services costs, changes in patient's insurance status and other factors. This ensures the system remains up-to-date and accurate.

This automation improves business operations for healthcare providers by reducing time spent on manual insurance verification, reducing claim denials and improving cash flow. For patients, it improves their overall healthcare experience by providing transparency on cost and coverage, avoiding unexpected bills and speeding the service scheduling process.

In essence, data science helps create an efficient, automated eligibility verification process that benefits both healthcare providers and patients. By using this technology, insurance eligibility and coverage details can be quickly confirmed, reducing the potential for surprise billing situations and ensuring a smoother healthcare journey for all involved.

See also: How Data & AI Can Shape Group Benefits

Contract Management using Data Science

Data science plays an instrumental role in evaluating contracts between healthcare providers and insurance companies. Effective use of predictive analytics can help identify inconsistencies that often lead to surprise billing situations.

Contract Analysis: Data science algorithms can analyze the terms of contracts between healthcare providers and insurance companies. This analysis can identify services that have limited coverage or are excluded altogether, thus helping to interpret the convoluted insurance contracts and clarify coverage for different services.

Predictive Modeling: Predictive analytics can be used to simulate potential patient scenarios against the terms of existing contracts. These simulations can detect instances where a patient’s necessary treatment might fall outside the purview of the insurance coverage, preemptively identifying situations where surprise billing may occur.

Risk Scoring: Machine learning techniques can assign risk scores to various healthcare services based on factors such as price, possibility of the service not being covered, etc. High-risk services could then be flagged in the system and trigger alerts to healthcare providers and patients about potential out-of-pocket costs or surprise billing.

Continual Reassessment: Given that provider contracts and insurance policies may change, data science techniques can be used to routinely evaluate new contracts and terms to ensure that the system remains up-to-date, automatically surfacing potential risks of discrepancy.

Automated Alerts: Employing real-time analytics, automated alerts can be triggered to notify healthcare providers and patients when a proposed treatment or service falls within a risk parameter for being out-of-network or not fully covered by the insurance.

By leveraging data science, healthcare stakeholders can gain an improved understanding of complicated contracts, enabling them to forecast and mitigate potential issues like surprise billing. This results in better alignment between provider and insurer, cost and coverage transparency for patients and overall prevention of unexpected medical costs.

Real-Time Alerts Through Data Analytics

Analytic and predictive systems are significant assets in the healthcare industry both for patients and for providers. They can provide real-time alerts for potential out-of-network scenarios or instances where services are not covered within a patient's insurance policy.

Real-Time Alerts for Out-of-Network Scenarios: An analytic system can use machine learning and artificial intelligence to continuously scan a patient's insurance information and a healthcare provider's network details. If a patient tries to schedule an appointment with a provider that is out-of-network, the system immediately triggers a real-time alert informing the patient about the situation before treatment is provided. This is particularly beneficial during emergency or specialist visits, where the status of the healthcare provider might not be immediately known.

Real-Time Alerts for Non-Covered Services: Machine learning algorithms can examine a patient's insurance policy to determine the coverage details for various services and procedures. Then, when a service or procedure not covered under the insurance policy is considered, the system sends a real-time alert to both the provider and the patient about the potential coverage gap. This allows patients to make informed decisions before moving forward with any treatment and helps providers enhance their service by informing patients about uncovered services.

Automated Regular Checks: Regular checks can be conducted with real-time updated information about changes in insurance policies, in-network provider lists and lists of covered services. Automated alerts can be sent out if there are any changes that may affect a patient's coverage.

Customized Patient Care: Based on the patient's individual’s insurance coverage, the system can provide personalized care options to patients to avoid unnecessary out-of-pocket expenditures. It can help in planning the health journey of the patient meticulously.

Challenges in Implementing Data Science for Surprise Billing

While data science presents promising solutions to curb surprise billing, practical implementation can encounter several challenges:

1. Data Privacy and Security: Handling sensitive healthcare and insurance data poses significant privacy and security concerns. Strict regulations for data handling should be met, and substantial resources are required to ensure that patient data is secure and privacy is not compromised.
2. Data Quality and Compatibility: Effective data analysis requires quality data. Inconsistencies, errors, outdated information or missing data can hurt the performance of predictive models. Furthermore, given that data is sourced from different entities like healthcare providers and insurance companies, compatibility can also be a challenge.
3. Technical Expertise: Effective implementation of predictive analytics and machine learning requires a high level of technical expertise to build, maintain and improve models. The lack of skilled professionals can be a potential hurdle.
4. Scalability and Adaptability: Scalability can be a challenge as implementing predictive models across vast and diverse healthcare systems can be complex. The models must also be adaptable, given the dynamic nature of healthcare policies, procedures and treatments.
5. Regulatory Challenges: The healthcare sector is heavily regulated, and any changes or implementation of new systems would need to comply with a myriad of guidelines. Regulation often prolongs the implementation process, making it complex and challenging.
6. Acceptance and Trust: For the system to work optimally, it requires acceptance and trust from all stakeholders: patients, healthcare providers, insurers and regulatory bodies. Any resistance to change or skepticism about the output of the models can limit the effectiveness of these solutions.

Despite these challenges, the move toward a data-driven approach in healthcare is inevitable and much needed. With careful planning, stringent data security measures, continuous model refinement and transparent communication with all stakeholders, data science can be used to tackle surprise billing and drive innovations across the healthcare industry.

The Future of Surprise Billing 

Advancements in both regulation and technology have the potential to significantly improve the current state of healthcare, particularly in terms of addressing the issue of surprise billing. Below are some prospective developments that may bring about positive changes:

Regulatory Advancements:

1. Enhanced Transparency Laws: Regulations might be strengthened to enforce greater transparency in healthcare pricing. Providers could be mandated to disclose prices in advance for all procedures and treatments. Consequently, patients would be better equipped to anticipate healthcare expenses.
2. Fixing Liability for Surprise Bills: Policies might be put in place where healthcare providers are made responsible for any extra charges if they did not inform patients about potential out-of-network care.
3. Standardizing Healthcare Pricing: Though a complex issue, regulators might consider some form of price standardization across healthcare providers, making it easier for patients to compare prices and make informed choices.

Technological Advancements:

1. Advanced AI: As AI continues to evolve, its application in predicting potential surprise billing situations is expected to become more reliable and accurate.
2. Blockchain in Healthcare: Blockchain technology can be leveraged to create a decentralized and secure system of medical records. This could facilitate real-time updates, ensure data integrity and make eligibility verification systems more efficient.
3. Telemedicine: The growth of telemedicine is expected to continue, giving patients the ability to seek healthcare without geographical boundaries. However, its integration in insurance networks would have to be streamlined to prevent surprise billing.
4. Interoperability: The drive toward achieving interoperability, where systems and devices can exchange and interpret data, would create more integrated and efficient healthcare ecosystems, reducing administration errors that can lead to surprise billing.

With these potential advancements, the aim is to foster a healthcare environment where pricing is transparent, billing mistake instances are minimized and patients can make informed decisions regarding their health without worrying about unanticipated expenses. 

See also: How Gen AI Changes Everything in 2024

Conclusion

Surprise billing serves as an unnecessary stressor within an already complex healthcare system, placing an undue financial burden on patients and impairing trust among patients, healthcare providers and insurance companies. It is, therefore, crucial for healthcare industry leaders, regulators and various stakeholders to collaborate closely to address this pervasive issue.

Continual enhancements in policies are a prerequisite to keep up with the dynamic nature of the healthcare industry. Regulatory bodies need to devise and implement robust laws that mandate price transparency and protect patients from unforeseen medical charges. It's equally crucial that these laws adapt as healthcare practices change and challenges emerge.

Increasing transparency is another crucial aspect. Patients need clear, upfront information about potential costs associated with their care. The more transparency there is in pricing and billing, the more educated decisions patients can make, potentially reducing the prevalence of surprise billing.

Lastly, the effective management of healthcare data is of paramount importance. In an era where large swathes of data are generated every moment, we need to lean on the technologies like data science and AI to make sense of it all. Harnessing and interpreting this data effectively can lead to alerts, better decision-making and ultimately the elimination of surprise billing.

The journey of eradicating surprise billing is complex yet achievable. It requires the collective effort of all stakeholders in the healthcare industry, updated regulations, the embrace of transparency, strategic use of technology and the effective management of data. The goal is to create a patient-centric healthcare system where high-quality care is provided without the looming worry of surprise costs, leading to better health outcomes and experiences for all patients.

The U.S. has been experiencing an opioid crisis since the mid-1990s, and opioids have had a significant effect on public health and the nation’s economic and social outcomes. Beginning in the late 1990s, healthcare providers began prescribing opioids at greater rates. By 2006, opioid prescriptions were written at 72.4 per 100 people in the U.S. That is roughly six times the rate in 1992. More than 500,000 opioid-involved deaths have occurred since 2000, and the U.S. has the world’s highest number of opioid-involved deaths per capita.

Although federal funding to address the opioid crisis has increased in recent years, opioid overdose mortality has also increased. Deaths from opioid-involved overdoses were among the leading causes of death in 2020. The use and misuse of opioids can result in serious health effects: People with certain harmful behaviors that result from opioid misuse — such as an increase in the amount and frequency of opioid use or failure to fulfill primary responsibilities at work, home or school — have opioid use disorder (OUD), which can affect an individual’s participation in the labor force and their ability to care for their children. Treatment for OUD is used far less than behavioral health professionals recommend.

The crisis encompasses several key issues:

• Over-prescription: The surge in prescriptions of opioids for pain management.
• Addiction: A high rate of users developing dependencies on opioids.
• Overdoses: A significant rise in fatal and non-fatal overdoses.
• Economic burden: Substantial direct and indirect costs to economies and healthcare systems.

The opioid crisis disrupted the workers’ compensation industry landscape by introducing a complex layer of new medical, ethical, legal and socio-economic challenges. Addressing these required a nuanced, multifaceted approach that safeguarded workers’ well-being while navigating the precarious terrain of opioid management, ensuring regulatory compliance and maintaining ethical integrity.

The opioid crisis peaked in 2012 when the prescription rate reached 81.3 prescriptions per 100 people. By 2020, the prescription rate had declined by almost half. And while the worst of the crisis is behind us, opioids are still being prescribed at three times the rate they were in 1992.

To rein in the crisis, workers’ compensation industry stakeholders explored diverse avenues, including alternative pain management therapies and stringent monitoring of prescription practices. Advances in technology have opened new avenues to explore and mitigate opioid risk. What if artificial intelligence (AI) had been more widely available during the crisis? How could it have augmented these strategies to make them more effective sooner? Could AI have mitigated the impact of the opioid crisis on the industry and, more crucially, safeguarded the health and livelihoods of workers across various sectors?

See also: 'Predict & Prevent' Can Rescue Insurance

Impact of the Opioid Crisis and the Workers’ Compensation Industry

The opioid crisis has created many challenges and concerns for insurance carriers related to injury management, recovery and claims. Many workers, following injury-related pain management treatments, are prescribed opioids. There are many valid reasons for opioids or strong pain killers to be used to minimize the pain that comes from catastrophic injury or surgery. In some instances, prolonged use or misuse leads to dependency or addiction, which complicates the workers’ path to recovery and reintegration into the workplace.

Various cases reveal a pattern where workers, after being prescribed opioids to manage pain post-injury, traverse the path of dependency. Often, a short-term prescription intended to manage acute pain can evolve into chronic use due to factors like inadequate monitoring, the absence of alternative pain management strategies and the inherently addictive nature of opioids.

Financial Burden on the Industry

The industry faced escalating financial strain as the nexus between opioid prescriptions and workers’ compensation claims strengthened. The intertwined challenges of managing opioid prescriptions, subsequent addictions, elongated recovery timelines and increased claim costs created a hefty financial burden for insurance carriers and their policyholders. Further, the cost is not merely monetary but also permeates the resource allocation and management aspects, necessitating more extensive care management and monitoring protocols.

Impact on Workers’ Health and Rehabilitation

The opioid crisis injected complexity into the health and rehabilitation journey of workers. Addiction or dependence on opioids can delay recovery, hinder effective rehabilitation and diminish the overall quality of life. Furthermore, the psychosocial impact, stigmatization and mental health toll of dealing with addiction introduce additional barriers to a worker’s rehabilitation and return to productive employment.

See also: How AI Can Help Insurers on Climate

Legal and Ethical Concerns

The legal landscape intertwining opioids and workers’ compensation is fraught with disputes and complexities. Cases where workers have succumbed to overdose, engaged in illicit drug-seeking behavior or experienced deteriorated life quality due to opioid addiction have found their way into courts, demanding legal scrutiny over prescription practices, monitoring and overall management within the compensation framework. Legal disputes may involve questions around liability, appropriateness of care and due diligence in prescribing and managing opioid-based treatment plans. In many cases, carriers are held accountable for insured workers’ negative actions.

The ethical discourse revolves around dual responsibilities: ensuring effective pain management and mitigating the risk of opioid misuse or addiction. Employers and insurance carriers encounter ethical dilemmas related to:

• Prescription practices: Balancing effective pain management while avoiding contributing to potential opioid misuse.
• Responsibility: Deciphering the extent of the employer’s or insurer’s responsibility in managing the repercussions of opioid prescriptions.
• Worker support: Establishing how to adequately support workers navigating opioid dependency in a compassionate and economically viable manner.
• Prevention and education: Implementing preventive measures and educational initiatives to enlighten workers about the risks associated with opioid use.

In this context, insurance carriers needed to act in the early 2000s to mitigate the crisis’ impact on their business. Suppose workers’ compensation carriers had more widely adopted artificial intelligence during the early stages of the crisis. How might it have been used to mitigate the extent of it and, in effect, save lives?

While it’s speculative to say AI could have “solved” the opioid crisis in the workers’ compensation industry, it could have significantly mitigated the impact. Through intelligent data analysis, predictive analytics, personalized interventions and more robust support systems, AI offers a multi-pronged approach to managing opioid prescriptions more effectively, thereby reducing the risk of dependency and its associated challenges. And while the worst of the opioid crisis is behind us, opioids still play an increased role in treating injured workers. It behooves insurance carriers to deploy artificial intelligence to help mitigate its effects today.