Financial metrics improved for most U.S. states during 2022, but the outlook for state credit quality is less favorable as economic conditions soften. As noted in Conning’s recently published 2023 State of the States Report, we have assigned a “declining” 2023 outlook on state credit quality, a change from the “stable” outlook we have had since our 2021 report.

Conning 2023 State of the States Overall Ranking

The previous two years were marked by relatively benign economic and credit conditions, with state credit quality riding the wave of the economy recovering from the impact of the Covid-19 pandemic and an unprecedented amount of federal aid. Moreover, state coffers benefitted from a strong labor market and nominal growth in consumer spending. With the economy in 2022 continuing its pandemic recovery and with people returning to work, income taxes performed very well. Sales tax collections also performed well, again due to federal stimulus-supported consumer spending and a reopening economy.

However, signs are mounting that the extraordinary tax-revenue growth of the past few years will soon moderate. If economic conditions soften, inflation could squeeze budgets, push down sales tax collections, keep expenses high and force states to tap reserve funds to balance budgets. Weakening labor and/or housing markets would impact state finances as well, as would lower corporate profits and depressed financial markets.  Some states are already feeling the effect of weaker stock and labor markets on income tax collections, and states that lowered taxes or expanded services could experience additional financial pressure. Furthermore, several states are facing significant infrastructure spending and pension obligations that may challenge their fiscal stability should an economic downturn come soon.  Nonetheless, states’ balance sheets are in their best shape since before the Great Financial Crisis with reserves at all-time highs and much-improved pension funding ratios. 

Which states will successfully navigate a recession? The impact will likely depend on how a recession plays out. For example, if unemployment jumps, states heavily reliant on income-tax revenue may suffer more. Or If consumers pull back on spending, states counting on sales-tax revenue may be vulnerable. How state leaders respond and leverage available resources in an economic downturn will be a critical test. 

See also: Transformation of Jobs in the AI Era

Conning’s Analysis: A Relative View of State Credit Quality

Conning analyzes 13 economic, socioeconomic and financial metrics that are indicative of state credit health in our State of the States Report. Our ranking is relative – comparing states to each other – rather than absolute – quantifying each state’s credit quality. For example, a state that improves its financial condition can still move down in our ranking should other states make even greater strides. 

Here are highlights of other findings in our 2023 report.

• Texas, fifth overall in 2022, moved up into first place. Florida, 2022’s #1 ranked state, fell to second. Both states benefitted from a strong economy, population growth and, in Florida’s case, an improved housing market while Texas outperformed in GDP per capita.
• The top-five overall ranked states had GDP growth, employment, revenue and population growth, which skewed to the South and West regions.
• Utah, which had held first place for three years prior to last year, fell to its lowest rank since 2015, in part due to how rising home prices affected affordability.
• New Hampshire, which had benefitted from the pandemic’s work-from-home dynamic, dropped from the top five to 21 as work conditions normalized.
• California was a notable laggard, dropping 14 spots to 42nd place, losing economic ground as reflected by lower tax collections. 
• New York’s population declined most, followed by Illinois and Louisiana.
• All 50 states recorded employment growth, with parts of the South and West offering the best jobs picture.  
• Nevada, Hawaii and Texas remained in the top five for employment growth, benefiting from an influx of residents.

Conning focuses on state credit quality, as states are large issuers of municipal debt, and their relatively high credit quality is important to many investors, such as insurance companies. Municipal securities offer opportunities for diversification by region and away from securities prevalent in many insurer portfolios, such as corporate and U.S. government debt. Additionally, these securities often offer higher yields than corporate debt of similar quality and duration and, particularly with taxable deals, will typically have a longer duration and a lower history of defaults than corporates. Our State of the States Report is not a definitive position on any state but rather a valuable starting point when evaluating a state’s debt issuance.

In this month's interview, with Sivan Iram, founder and CEO of insurtech Capitola, he offered up a striking image of how agents and brokers can use AI: as a little angel sitting on their shoulders, whispering wisdom. 

At a time when we're all being bombarded with predictions about the future of generative AI and being lectured about how to start using it, I like that simple image: an angel sitting on my shoulder.

Iram backed up that image with one of the more trenchant summaries that I've seen of the arc that AI has followed and that has brought us to this breakthrough moment. 

He said a key difference between generative AI and what preceded it is that the latest form doesn't just have a brain; it also has a mouth. You can talk to it, and it'll talk back. 

He also said the AI's brain has taken a leap forward. He said the first generation of AI could extract information from documents by recognizing characters, the second by recognizing words. Now, in the third generation, Iram says, the AI can search based on meaning -- it doesn't have to see the word "premium" in a document to infer that that's what's being discussed, for instance.

The generative AI can not only extract information from formal documents but can monitor and learn from the interactions between brokers and underwriters and between brokers and clients. The AI can also now easily supplement the information from internal sources by pulling in publicly available information, such as on a firm's revenue history. 

Meanwhile, the AI is assembling the wisdom that it will start whispering in your ear -- and will keep getting smarter as it pulls in more information. Iram focused on risk appetite as an area where the new version of AI can provide valuable advice. He says carriers currently communicate their risk appetite but do it in an analog, rather ad hoc way. He says AI can track all the current signals and augment them based on email traffic, then help a broker efficiently find a market to place the risk. 

He obviously hopes you develop your AI aspirations alongside Capitola, which provides a digital marketplace where brokers work with carriers to place risks in small commercial lines. But, whatever route you take, he provides some real insight. I hope you'll give the interview a read.

Cheers,
Paul

---

AI AND THE FUTURE OF INSURANCE

With scrupulous implementation and judicious oversight, AI can supercharge the industry and provide better services to customers.

WHY BROKERS SHOULD EMBRACE AI

AI lets brokers focus on higher-value tasks, provide better service and build more business than they ever thought imaginable.

9 KEYS FOR EMBEDDED INSURANCE

Embedded insurance, partner distribution or B2B2C distribution can be highly effective. But it's not easy to get right.

LIFE INSURANCE BROKERS NEED BETTER TECH

If brokers are going to continue to provide exceptional customer service, they need the technology and resources that can back them up.

THE POWER OF LIFECYCLE MARKETING

With an integrated, data-driven approach, companies can optimize their strategies based on customers' needs at all lifecycle stages.

INDEPENDENT AGENCIES' GUIDE TO REBRANDING

70% of purchase decisions are based on emotion, so branding that consumers like and relate to can be a revenue generator.

ITL:

You wrote a nice piece for ITL recently on why agents and brokers should embrace AI. I wanted to follow up, given all the recent developments with generative AI, and talk about where you see the benefits of AI, both now and in the future.

Sivan Iram:

Absolutely. The mission is to bring technology to build better tools for insurance professionals. AI is just a means to an end. But it has immense potential to bring in capabilities that we’ve never seen before.

The place where GPT models, large language models, excel is with textual information. These models can take in large amounts of data, truly understand it and synthesize it and create new content out of it. That may mean summarizing it, enriching it with external information or composing new bodies of work.

I can be specific. At the core of Capitola, we're trying to bring efficiency to the middle market—larger companies’ insurance needs still require tailor-made products and solutions. Our AI lets us extract information from large documents and summarize them to get brokers and account managers familiar with the risk. We can automatically generate emails and mesh with whatever communication protocol the underwriter uses, so the underwriter can read through the responses and understand what they mean.

I'll give you a quick example. The other day, an underwriter wrote back to a broker about a real estate placement and said, “We don't insure anything built prior to 1946.” The AI read that response, understood that it was a declination and understood the reason. The AI will remember what happened and, the next time a broker is trying a placement on a building built before 1946, will tell the broker up front, This market doesn't accept anything prior to 1946.

We refer to the AI as a co-pilot because it sits set to the broker in the cockpit, helping every step of the way.

ITL:

I’m hearing the term “co-pilot” more and more. I've used different terms over the 35 years I’ve been following AI, but I think I like that one.

You said generative AI excels with text. Would you provide more context to help people understand why it’s so different than what’s come before?

Iram:

The most basic technology that can extract data from documents is OCR: optical character recognition. It's based on computer vision, which is a machine learning algorithm, but it’s very basic form of AI. It doesn't do a great job with scanned documents, and you can't really do handwriting. That was generation one.

The second generation extracted data based on syntax, not just characters. If you wanted to extract, let's say, the dollar value of the premium on a certain policy, the AI looked for the word “premium.”

The big leap with the third generation, with GPT technology, is that there’s a semantic understanding of the text. You can extract information via inference. The word “premium” doesn’t have to be there for the AI to understand that that’s what’s meant.

But that's only the first thing. Another thing we can do is enrich the information from publicly available data sources. If you want the last five or 10 years of revenue numbers for a company, you just ask the AI to go out and fetch the information.

The last thing we see is risk appetite matching. AI can help brokers know what markets to go to for every risk.

GPT has both a brain and a mouth, and people really get excited by the fact that it has a mouth, so you can talk to it, and it can respond. But even the brain in itself is a large language model that marks a huge improvement. The AI’s brain can look at huge datasets, understand what’s there, synthesize it and create insights and recommendations. Then there is the added benefit of the mouth, so you can also talk to it as if it were you.

ITL:

Drawing on the capabilities of the AI, you’ve also adopted a very modern design perspective, right?

Iram:

We start with deep, deep empathy with the broker: understanding their pain points, understanding their needs and putting them at the center of the process. Traditionally, most of the software that's been built for the industry required people to perform repetitive, manual tasks, such as entering data. We bring a new methodology, which is a very popular trend called “consumerization” of the enterprise. You think about the user first – the broker, in our case – and design everything for their benefit. You try to put them in a state of flow.

The new generation of brokers are folks who enjoy consumer tech, they watch Netflix and they use Instagram and other tools, then they go to work and they work with a system that feels like it was designed in the ‘90s.

ITL:

Helped me visualize this. If I'm a broker, and I'm using the Capitola system, what does my day look like versus what it would have looked like?

Iram:

Most likely, the brokerage that employs you has an agency management system with screens upon screens and all these different modules. You keep switching windows, working out of your Outlook, which is a tool designed for emails, as you work on 12, 13, 14 different placements. There's a constant stream of emails coming in, and you have to quarterback everything in your mind. You have to understand which email relates to which placement. You might be using Notes on the side, using Excel, sometimes using Word and PowerPoint to create customer proposals.

Capitola moves you to a project-based system. Every single placement is its own tiny project. If it's a basic, simple renewal, it takes you a couple of days to complete. Some are really complex. You might need to go really broad with a marketing exercise, or you might have a shared or layered program and have to really think about the structure. But, with Capitola, you see your entire book of business, organized by accounts, organized by renewals and placements, with all the documents there, and can deal with each project independently rather than all at once.

ITL:

And you have a sort of little AI on your shoulder whispering advice to you, such as not to try to place a building built before 1946 with a particular market?

Iram:

Today, carriers try to communicate risk appetite but in a very analog way. They do it at conferences, they do it at events, sometimes on the golf course, maybe through marketing emails. But really, at the moment of submission, every broker has their own algorithm running in their head.

To improve on that process, we're taking very explicit indications from the carriers themselves, which are giving us their underwriting guidelines. The promise we make is simple: Tell us what your risk appetite is, and you’ll get a highly curated deal flow from Capitola.

Number two is we're also working with the leadership of every brokerage we work with to understand what their preferred markets look like, all else being equal in terms of coverage and terms and price. Every brokerage has their natural partners.

And the third source is transactional data. We're reading the emails for indications of risk appetite and watching to see which placements are accepted and which are declined.

Combine all that, and we’re able to come up with a market recommendation engine that sits on the broker’s shoulder and whispers wisdom into the broker’s ears.

ITL:

That’s great. But what have you done for me lately? What's next?

Iram:

The company started in 2021, and for the first year and a half we were kind of under the radar in terms of public activity. But the two sides of the market, brokers on one side and carriers on the other, are getting more and more engaged, so we’ve started spreading the word. We finalized our Series A raise in April, and now we're on the mission to bring on board as many brokers as we can and as many carrier partners as we can. We plan to keep building capital and make an impact in the ecosystem.

ITL:

It sounds like you’ll do great. Best of luck.

About Sivan Iram

Sivan is the co-founder and CEO of Capitola, the world's smartest digital marketplace for commercial risk. He has raised $20 million to date from some of the world's best venture capital firms, including Lightspeed Ventures. Currently leading a team of 20, he is responsible for setting the company's vision and mission, making strategic decisions, and ensuring that the company's sales targets are being met. Sivan holds an MBA from Harvard Business School and has a background in Software Engineering.

 

KEY TAKEAWAY:

--Although storm surge is incredibly damaging, it’s omitted from most traditional home insurance policies. Policyholders need help understanding if their current policies cover damage it causes.

----------

June marked the start of the six-month-long hurricane season, a time of year that brings forth powerful storms capable of causing extensive damage and loss of life. The Eastern Seaboard, in particular, often finds itself in the crosshairs of severe winds, heavy rainfall and storm surge, which can lead to destruction for countless homeowners and communities.

What Hurricane Ian Taught Us About Financial Damage 

One need only look back at the aftermath of Hurricane Ian last year to see the lasting impact these storms can have. The devastation resulted in an estimated $112.9 billion in physical and economic damage, ranking it among the five costliest hurricanes in U.S. history. In Florida, Hurricane Ian is anticipated to set records as the largest hurricane-related loss event.

Only about 18% of homes in counties that were under evacuation orders for Hurricane Ian actually had a flood insurance policy. Ian left thousands of homeowners holding the bag to cover the costs caused by storm surge, and many workforces were completely disrupted.

Although storm surge is incredibly damaging, it’s omitted from most traditional home insurance policies. Policyholders need help understanding if their current policies cover damage it causes. If there’s a gap in coverage, then it’s valuable for them to get supplemental disaster insurance that specifically covers storm surge or flooding caused by hurricanes.

See also: Hurricane Season: More Trouble Ahead?

What Does This Hurricane Season Have in Store?

Although preseason predictions suggested a less active hurricane season, Mother Nature can be fickle. According to experts at the Colorado State University Tropical Meteorology Project, 13 named storms are expected for the 2023 season, including six hurricanes, with two of them potentially becoming major hurricanes (Category 3 or higher on the Saffir-Simpson scale). While this may seem like a relatively calm year, even in "normal" seasons like 2022, hurricanes such as Ian and Nicole each resulted in billions of dollars in damages in the U.S.

These lessons serve as a reminder that even if the forecasts indicate fewer hurricanes, the effects can still be incredibly costly. With the memories of Hurricane Ian fresh, policyholders along the eastern U.S. coastline must prepare themselves and their finances for the unpredictable nature of hurricanes. 

Here are six steps consumers can take to safeguard against the dangers and financial impacts of hurricanes:

1. Stay informed: Keep a close eye on news and weather forecasts to stay updated on any approaching hurricanes. Create a disaster plan and be prepared to evacuate if necessary.
2. Secure your home: Strengthen your home against hurricane forces by reinforcing the roof, windows and doors with hurricane shutters or other protective measures.
3. Elevate your home: While this is obviously a major step, FEMA says it's important to ensure that the lowest floor of your home is above the base flood elevation for your area.
4. Clear your yard: Remove any debris from your yard that could become hazardous when caught in high winds.
5. Stock up on supplies: Prepare an emergency supply kit with essentials such as water (at least one gallon per person per day for a minimum of three days), non-perishable food, first aid supplies, flashlights and a battery-powered radio to stay informed during power outages.
6. Invest in disaster insurance: Traditional home and renter insurance policies often lack comprehensive coverage, leading to significant out-of-pocket expenses for families. Waiting for claim processing can deplete emergency funds. Products like Recoop Disaster Insurance offer a solution by providing insured homeowners with quick access to flexible funds within days of filing a claim after a declared disaster.

See also: The Unprecedented Hurricane Season

While it’s impossible to predict the exact path and severity of hurricanes in any given year, you can encourage your clients to take these measures. They will significantly improve their preparedness in the face of a potential disaster and will appreciate having an insurer that cares about their overall financial well-being

KEY TAKEAWAYS:

--An obstacle to writing more small commercial insurance business is the limited data offered by small firms. The effort required to assess the risks does not always match the relatively small policy premiums.

--Enter technology, particularly the advancements in cognitive technologies and modern AI-powered data platforms. These innovations are reshaping small commercial risk assessment by enabling faster, more efficient and accurate underwriting processes.

---------- 

Small businesses account for 99.9% of all businesses in the U.S., according to Forbes. Not only is there a large pool of clients, but many are growing. Case in point: Ben & Jerry’s started as a single ice cream shop in Burlington, Vermont. 

Today, it seems no two small businesses are the same. In fact, many—including barber shops, grocery stores and restaurants—are more specialized than ever, as entrepreneurs fill specific market needs. There are restaurants that welcome dogs to dine with their owners, members-only hair salons and bars where patrons can practice ax throwing. Emerging microsegments can be lucrative opportunities for insurers. But targeting them requires fast and accurate ways to assess the risks, as well as a streamlined approach to quoting and binding policies. 

Despite their small size, many small business owners often expect the same attention and expertise large businesses receive. Small business owners want to know the insurer understands their unique risks, and they want to feel confident they have the right protection. 

But the small-business market can be a solid opportunity for insurers because specialization in niche segments can lead to becoming a provider of choice. And the more insurance companies work with businesses in specific sectors, the better they can understand in-appetite risks and nurture profitable business more quickly.

Using technology to address the micro-market

There’s significant opportunity for insurers to write more small commercial insurance business, and technology can play a key role. But one substantial obstacle is the limited data offered by small businesses, making it time-consuming to assess risks effectively. The predicament faced by many insurers and MGAs is that the effort required does not always match the relatively small policy premiums.

Enter technology, particularly the advancements in cognitive technologies and modern AI-powered data platforms. These innovations are reshaping small commercial risk assessment by enabling faster, more efficient and accurate underwriting processes. 

To transform commercial underwriting, insurers and MGAs should evaluate their existing risk assessment methods and explore how using technology for data access can augment their processes. For example, a company may already have an agile system in place to connect with and leverage API-enabled data platforms but needs to adapt workflows. Alternatively, the insurance organization might have a robust underwriting workbench solution in place but require modern technology to gain access to more data for better risk assessment. 

When selecting a data solution to enhance visibility into exposures and risk quality, speed and accuracy are both important. Speed holds particular significance in small commercial insurance, where efficient application response times can outweigh price considerations for agents/brokers and policyholders. By delivering quotes promptly, insurers and their distribution partners can deter business owners from seeking alternatives. 

See also: Emerging Tech in Commercial Lines

While speed is crucial, inaccurate information renders it useless. Emerging AI technologies are gaining popularity for risk evaluation. But these solutions may sometimes finesse answers if reliable sources cannot be located. When considering a generative AI solution, it is imperative to ensure the vendor has established processes to verify data accuracy, such as providing citations for the sources used to develop conclusions.

Additionally, insurers should seek out technology providers willing to collaborate and ensure seamless implementation. It’s best to collaborate with partners who become integral to the team, listen to feedback and adapt the solution to meet specific requirements. Establishing a strong relationship with the provider is another means to ensure accuracy in risk-quality data. Some technology companies even go the extra mile by working closely with underwriting teams, running numerous queries to assure information validity.

The small commercial insurance sector holds immense opportunities for insurers and MGAs. Not only does it offer a sizable client base, but specializing in niche markets can foster expertise, deepen market understanding and align the insurer's risk appetite accordingly. By capitalizing on cognitive technologies, insurance organizations can overcome the challenges of small commercial underwriting, swiftly generate quotes, bind policies and expand business.

KEY TAKEAWAYS:

--Among the other risk trends examined in the latest Allianz report are geopolitical, economic and market volatility and the inflationary environment (ranked #3).

--At the lower end of the risk rankings scale, but not to be underestimated, is the use of new technologies such as AI tools by professional services firms.

----------

Architects and engineers face greater scrutiny over building and fire safety defects. Financial services professionals may be accused of mismanaging investment funds hurt by inflation. A lawyer’s untrained use of artificial intelligence (AI) tools when preparing client cases could result in an error-ridden brief. The emerging risk landscape for professional services firms is multi-faceted.

A new report from professional indemnity insurer Allianz Global Corporate & Specialty (AGCS) finds that affected professions include management consultants, auditors, accountants, architects, engineers, solicitors and lawyers and media executives, all of whom may be held responsible for losses that arise from a perceived breach of their duties.

Although exposures vary, all these professions face a wide range of civil liability exposures that need to be addressed and mitigated. These could range from accusations of negligence or omissions resulting in harm or damage to the client, to misrepresentation, to failure to identify fraudulent activity, to the unintentional breach of contract, intellectual property rights or confidentiality and regulatory investigations and actions. 

Building safety laws and digital dangers top the heat map

Of the 11 emerging trends in the report, evolving legislation related to building safety and cyber crime, social engineering and data loss are both ranked #1 (very high – a critical impact to operations or loss severity could be expected). 

Although building safety has predominantly been a U.K. issue following the Grenfell Tower fire tragedy in 2017, some impact will be felt globally. In the U.K., extended liability periods for building and fire safety defects could bring new legal claims against manufacturers and suppliers, with a potential domino effect on all specialists in a construction project, such as architects, engineers and design and build contractors, for example.

Cyber-attacks have increased in recent years – and professional services firms are highly exposed due to the proprietary customer data and intellectual property they process or operate with.  For example, cyber mercenaries are increasingly targeting law firms to illegally obtain confidential or protected data that could tip the balance in courtrooms. These "hackers-for-hire" provide technical capabilities and deniability of involvement in the cyber-attack, should it be discovered. 

Claims drivers, which apply across all professions, include phishing and spoofing frauds, third-party supply chain risks, ransomware or malware, a lack of adequate systems or controls or data loss. Not only does a cyber breach present immediate first-party costs and disruption, it can also result in significant regulatory exposures, including action from data protection authorities and considerable fines. Litigation from affected data subjects may follow, including large group claims. Breaches may also lead to client and third-party liability claims, with claimants alleging losses due to business interruption or leaked information. A breach also carries the risk of reputational damage, resulting in stock drops and securities claims. Smaller firms can be more vulnerable as they typically have less sophisticated cyber-security.

See also: Best of Both: Bundling Parametric, Indemnity

Prepare for volatility and unexpected impacts from inflation and new tech

Among the other risk trends examined in the report are geopolitical, economic and market volatility (ranked #3 – moderate impact to operations or loss severity could be expected). The report notes that regulatory exposures can arise for professionals acting for clients who may potentially be caught by a rapidly evolving sanctions regime, while, for construction and design professionals, disruptions to supply chains could bring claims relating to project delays. 

The inflationary environment also ranks as a #3. If inflationary pressures lead to recessionary conditions, there could be a myriad of potential exposures for professionals, including insolvency-related exposures for auditors and insolvency practitioners, lenders’ claims for solicitors and valuers and claims arising from due diligence against lawyers and accountants. Outside of recessionary conditions, financial services professionals may face mismanagement and suitability allegations relating to funds hurt by high inflation.

At the lower end of the risk rankings scale, but not to be underestimated, is the use of new technologies such as AI tools by professional services firms (ranked #4 – minor impact).

While AI has the potential to operate as a risk reducer, as technological solutions evolve rapidly so do the potential claims drivers. These include data privacy or copyright issues, the need to preserve confidentiality when using service providers, risks of errors being repeated in volume work and the level of supervision involved in machine learning tasks.

Professional services firms must continue to properly train and supervise their staff as technology evolves and to ensure the authenticity of work products considering the emergence of tools such as ChatGPT. Ultimately, a lack of awareness of how generative AI works, as well as untrained use, could lead to legal sanctions and civil claims against all types of professionals.  

The full report is available here: Professional Indemnity Insurance Claims 2023.

“Parametric” is a buzzword in insurance today. 

Imagine that you have signed up for travel insurance. Your flight is delayed; instead of your reporting a claim and waiting weeks to be reimbursed, your insurance is connected to an open-data source exposing real-time flight schedules, so it automatically proceeds with the payment with no further formality. 

Same applies for corporate risks when an industrial group signs up for a hail coverage, which payment is based on the size of the hail stones. 

Is the growing buzz about parametric insurance due to its enhanced user experience (UX)? Certainly, but the appeal shouldn’t be restricted to this. 

First things first, let's set the scene with some raw figures:

(source: InsTech)

Buzz is high, but the market is still emerging. It is focused on corporate lines and natural events. 

See also: Best of Both: Bundling Parametric, Indemnity

Closing the protection gap 

Figures show that the main merit of parametric insurance is to provide answers when traditional, indemnity-based insurance demonstrates some limits. 

Parametric insurance is often proposed as a complement to an indemnity-based contract, for instance when terms and conditions exclude certain perils or assign limits or exclusions. Parametric insurance can fill those coverage gaps. 

More substantially, parametric insurance stands for a solution to protect uncovered populations or situations 

There is a continuous flow of announcements about parametric insurance about natural events in developing countries. This involves several sources of innovation:  

--It often consists of micro-insurance for farmers or small business owners who would not have access to insurance otherwise. 

--Specific distribution schemes are at work, through humanitarian/international organizations acting as sponsors, distributors or even financiers to reach fragmented markets. 

--New sources of data fuel the range of parametric products, bringing answers where indemnity-based insurance is unable to do so. 

--Satellites provide weather data used for natural events, but also for performance yield of renewable energy facilities, soil composition to determine drought conditions, 3D imagery to assess water level changes (flood), etc. 

--IoT or sensors help to monitor cargo shipping, navigability of waterways, etc. 

--Credit card transactions can determine business interruption without damage (linked to social unrests, pandemics, weather events). 

--Network transactions make downtime insurable or help with the detection of cyber attacks. 

Not to mention custom indexes built for specific parametric programs.

The list is still open and growing.

On a more general basis, parametric insurance can -- to some extent -- mitigate a lack of insurance capacity.

As a single-peril coverage, parametric insurance provides coverage that is more focused and easier to limit, compared with a comprehensive policy. (Re)insurers may have more appetite under these conditions. 

More importantly, when traditional (re)insurance capacity is shrinking, financial markets may have more appetite for insurance-linked securities (ILS) as a way to diversify their portfolio (in terms of geography or financial markets cycles). In some way, parametric insurance stands for a continued evolution initiated by Cat bonds in the '90s. 

Risk management or corporate finance? 

Parametric insurance offers another advantage that opens up wider prospects: speed of payment. 

In traditional insurance, a catastrophic event requires long filing procedures, the involvement of adjusters to determine conditions, assess losses, etc. It can take months (if not years in case of litigation), whereas parametric insurance claims triggering payment in a matter of weeks. This means much more than UX! 

Most small and medium-sized businesses don’t have cash reserves or business continuity plans to withstand catastrophic events impairing their activity. A swift payment can represent a matter of survival. Enough to justify the cost of parametric insurance. 

The same rationale applies to larger organizations, at a wider scale. A catastrophic event that is quickly compensated doesn't draw on cash reserves or require negotiating a credit facility. Parametric insurance turns out to be a tool to protect equity and reduce performance volatility. A key point for large listed companies! 

Add to this that when parametric insurance is transferred through ILS, it tends to move the insurance cursor from risk management to corporate finance, from risk manager to CFO! 

Parametric insurance is no silver bullet

Parametric is simple to explain, but designing a hail coverage, as per our above example, is not simple: 

It requires high-quality data (available, accurate, real-time), possibly certified by a third party… 

…and some work to define the actuarial link between the trigger and the loss incurred, through risk modeling and analysis of same events history. 

Basically, a poorly calibrated parametric product can lead to: 

--the trigger being reached and payout released, but no significant damage for the insured; 

--the trigger not being reached, but the insured suffered a loss. 

Parametric insurance is meant to avoid complex claims processes, adjudication costs, etc. If, in the end, adjusters or lawyers have to be involved to mitigate such cases, parametric insurance would lose part of its upside. 

In that sense, beyond its apparent simplicity, parametric insurance still requires significant education. 

What about price: Is parametric insurance cheaper? It is difficult to compare a single-peril parametric coverage cost against a comprehensive policy. Because parametric is often used to fill gaps from traditional insurance, it stands for an additional cost for sure… yet with upside. 

Finally, the parametric alternative doesn’t change a basic insurance rule: If there is no risk appetite from carriers, there is no capacity; if there is no capacity, there is no insurance, parametric or not. As simple as that!  

The primary way to mitigate this, especially for Cat events, is to ensure a proper mutualization of the portfolio. This is why pooling is increasingly used, sponsored by supranational organizations to provide more legitimacy. 

See also: Parametric Insurance: Is It the Future?

The second mitigation is about finding alternative sources of capacity to cover primary carriers. Parametric insurance can be boosted by the appetite from financial markets for high risk/high yield in a low-interest-rate environment. With increasing interest rates, will financial markets demonstrate the same appetite for ILS-shaped parametric products?

So, what is ahead of us? My views on the drivers for parametric insurance to gain traction: 

It is a data game. The development of new data sources allows parametric insurance to expand, especially beyond natural events. Parametric could become mainstream when it thrives for mainstream perils. The question is, how attractive can parametric be against indemnity insurance? The UX advantage is granted, price advantage remains to be demonstrated. The unbundling of coverage against comprehensive policy will be the next challenge. 

Along with it, it is an actuarial/data science game. When current models are not applicable, new ones have to be invented. Risk modeling innovation aims both at keeping indemnity-based insurance relevant, and at using parametric as a sustainable way to transfer Cat risks. 

It is a capacity game. As long as parametric insurance deals with high-severity perils, reinsurance will drive the market: It dictates conditions to the primary carriers and defines the ultimate protection of insureds through retrocession or ILS. The main question is about frequency: If frequency adds to severity (re climate change), there will definitely be a capacity issue. 

It is a regulatory game. Regulation is there to protect the insureds, but when regulation impairs insurers trying to adjust their terms to market conditions, it produces a shortage in capacity. Another question is the bias introduced by the different regulatory frameworks between reinsurance and ILS on one hand and primary insurance on the other. It is not certain that this asymmetry is beneficial to the protection of insureds.

KEY TAKEAWAY:

--If an existing brand does not represent the true value an agency has to offer, a rebrand could be in order. But it should never be a spur-of-the-moment decision, should be allowed enough time to take shape, should draw on ideas from the whole team and should be continually monitored based on key customer metrics.

----------

When you first thought about opening an insurance agency, you likely visualized a name and maybe a logo as part of the business's identity. From the beginning, you considered how your business would be perceived by consumers, how they would interact with it and how it would contribute to your reputation. Whether consciously or not, you were thinking about your agency brand. 

Over the years, an agency’s brand evolves with its service offerings and the world around it. Today, 70% of purchase decisions are based on emotion, according to a study by Gallup, so branding that consumers like and relate to emotionally can be a revenue generator. There will come a time when the original brand no longer serves the agency or evokes consumer emotions, and decision makers will consider rebranding to bring new life to the business and realign themselves to fit their client’s needs. 

Should you rebrand? 

An effective rebrand at the right time can encourage loyalty with existing clients and growth with new ones. Conversely, a rushed, incohesive rebrand can damage the agency’s current book of business and opportunity with new markets or, at best, result in a waste of time. 

Take Brinks Home Security’s rebrand to Broadview Security after they were acquired by Broadview in 2009. After failing to consider the long-term brand standing of the Brink’s name, the company lost a large portion of their base clientele. In response, they reinstated the Brink’s name two years later.

Taking a calculated risk that still considers existing clientele is important. 

While it is impossible to put a time stamp on when to rebrand, agents should be paying attention to the value their agency brings to the industry. If their existing brand does not represent the true value an agency has to offer, a rebrand could be in order.

Our organization, SIAA, for example, is no longer the organization we were a few years ago because independent agents require a different suite of support than they did when we began 25 years ago. As such, we have chosen to undergo a rebrand that better highlights our offerings for the modern independent insurance agent.   

See also: Is Your Agency Ready for Automation?

Take your time

A rebrand should never be a spur-of-the-moment decision. Once the risks have been considered, independent agencies should allot a minimum of six to 12 months to complete the process. If it were to be done in six months, it would require full-time attention. The key is not to rush. Rebranding will require many pivots, and allowing time for these small, detail-oriented tweaks is essential.

Allowing enough time to go through the rebrand process will also let agents better analyze what is working and what is not in the business. A rebrand is not the time to throw everything away and start over. 

Agency owners should ensure the business leads with what they do well and consistently. They should consider the agency’s core values and mission statement as well as their agency data, including lead conversions, sales numbers or client retention rates. This data can illuminate what is working with the brand and what needs to be changed. Existing clients stay with their agency for a reaso,n and management would do well to identify those facets of the business and find better ways to spotlight them in the rebrand. 

The team knows best 

Your team will be your strongest asset in a rebrand. From making preliminary decisions to communicating the final product, agency staff will often have the best insight on perceptions of brand among clients, on areas that can be improved and on legacy aspects to maintain. Agency owners should ensure their team feels welcome to offer ideas and feedback. This way, management can alleviate some of their workload, and the staff will be familiar with the vision when it comes time to reintroduce the agency. 

Plan for the follow-through

When the rebrand has been built out on paper and an agency is ready to implement their changes, it will require a commitment from staff to ensure consistency. Agents should be prepared to enforce their new brand guidelines and practices by listening to their staff. Management should monitor how they are interacting with clients or prospects and stay present in email chains and other communications to confirm their approach aligns with the new brand vision. Any identified branding gaps should be addressed with staff to ensure they understand the goal of evolving the brand. 

It is crucial that independent agencies know how they are going to measure the success of their rebrand. Agencies should consider data such as lead conversions or client retention rates. Management should track these numbers throughout the rebranding process to have a tangible idea of how the new brand is performing. When the new brand is first launched, agencies might see a small decrease in these numbers before they begin to grow. This is normal, but agencies should remain diligent in monitoring progress.

See also: Incumbents Can Score an Alley-Oop! 

Tap into your resources 

While a time-consuming process, rebrands need not be excessively expensive. Organizations like SIAA can be a great resource for guidance throughout a rebrand. While SIAA does not offer formal resources for rebrands, we can help connect our members with resources such as Logotournament.com or freelance professionals to assist with various parts of the rebrand process such as content creation and graphic design. Agencies also should consider consulting their network for additional resources or rebrand insights they may have from their own experience. 

Rebranding can be a daunting task for any business. Independent agencies might find it difficult to imagine making the time to undergo a rebrand that will bring more value to the industry and their clientele. However, rebranding is a natural and integral part of owning any business, especially one with longevity.

Consider taking a moment to imagine how your agency might benefit from a reintroduction to the world and the new opportunities a rebrand could present.

"The best loss is the one that never happens."

That, to me, is the most compelling line from the interview I conducted recently with Pete Miller, CEO of The Institutes. The Institutes (with which ITL is affiliated) has for some months now been arguing that insurers should take all the data that they're gathering on potential hazards in commercial buildings, homes, cars, etc. and spin it forward. Rather than view data analysis as a historical exercise for pricing risks, then indemnify customers for losses, Pete says — and I wholeheartedly agree — that there is a major opportunity for insurers to use sensor data to spot wiring problems before they lead to fires, to detect a leak before it can do any damage, to warn workers that they're risking injury before they get hurt.

Insurers will pay fewer claims. They will also avoid all the costs of processing the claims that don't happen. They will have the opportunity to sell all sorts of new products and services that minimize risk. Meanwhile, insurers will not only spare customers from paying a deductible but will let them skip all the hassle that comes with recovering from a fire, leak, etc.

Talk about fulfilling the insurance industry's noble purpose. Talk about creating loyal customers.

If you want to dig deeply into "predict and prevent," I encourage you to check out the podcasts Pete has been conducting with insurtechs such as Whisker Labs and Betterview, with executives from insurers such as Chubb and brokers such as Marsh McLennan, with insurance commissioners and with customers. (If, like me, you're more a reader than a listener, you can find the transcripts for each of the six podcasts on the same page.)

In the meantime, here is the interview I did with Pete, which, in my not so humble opinion, is a great summary of the opportunity that predict and prevent presents:

ITL:

The predict and prevent theme really resonates with me. How did the idea take shape, and how did it become a major theme for The Institutes?

Pete Miller:

The idea came from an awareness of emerging technologies that can really help to do more effective and, in fact, more-real-time risk mitigation.

If you think about it, the best loss is the one that never happens. And there's a huge amount of data, including real-time data from IoT sensors, as well as a vast increase in the tools that are available to process and make sense of the data. If you start to combine all that, then there's an opportunity for real-time, improved data analytics for resiliency.

As a result, we can make people's lives better and safer and easier. We can use analytics to predict what's going to happen and stop problems before they start.

ITL:

What are some of the best examples of predict and prevent that you've come across since you started focusing on this theme?

Pete Miller:

Whisker Labs has a device called a Ting that is really, really cool. You just plug it into a wall socket, and it monitors for electrical hazards both in your wiring and the electrical devices you're using and alerts you before a fire can start. State Farm is deploying Ting to its customers [at no cost to them].

There's also a device that people in a warehouse can wear. It learns your body for the first few minutes. If you’re taller, you do some things differently than if you’re shorter, for instance. After that initial phase, if you're doing something like leaning over too far and are risking injury, the device goes, "No, don't do that."

How cool is that? You prevent injuries that could have someone walking around in all kinds of pain for a lifetime.

Others are looking at ways to head off damage from wildfire.

It's all been really interesting.

ITL:

You have lots of senior insurance executives on The Institutes board and talk to others all the time. What has their  reaction been to predict and prevent?

Pete Miller:

I can't talk about actual deliberations within our board, but I can say that there's a very broad recognition that this is the right thing to do. The theme really resonates. It's just too logical, and if the insurance industry doesn’t do this then somebody else will. There's so much benefit to be had.

Insurance companies understand that a predict and prevent approach cuts their losses. They don't just have fewer claims but also avoid all the expenses associated with loss adjustment. The loss never happened.

Policyholders are happy because you're telling them you can detect problems and prevent losses.

Regulators see this as a great opportunity, too.

ITL:

I imagine that there are also ways to take the data being gathered and feed it back into building codes, insurance contracts or other instruments that will change behavior.

Pete Miller:

There are a variety of ways, some of which is dual-purpose. Chubb, for instance, is working with a real estate company that has a bunch of office buildings, and they put water sensors in them. The sensors provide real-time alerts if there is a water leak or flooding, and a valve can shut off the water automatically. The sensors also generate data that can be provided to builders or property managers or those who write the building codes and that will help head off similar problems down the road.

ITL:

To me, the tipping point for predict and prevent has always depended on being able to make a strong economic argument. If you retrofit a house to install sensors and a valve that can automatically shut off the water when there’s a leak, that costs you maybe $700 or $800, and you have to install a lot of those valves to prevent a single loss. How close do you think we are to a tipping point?

Pete Miller:

In round numbers, the cost to set up and monitor those sensors that Chubb installed is less than $5,000. A single water loss? $100,000, on average. So, for that client, the math is easy.

Whisker Labs says its Ting device can prevent 75% to 80% of house fires, and the cost of a device is minuscule.

Those economics are coming into line. It's starting to make sense. And the costs are always coming down.

Companies are also viewing predict and prevent as a value-add in terms of customer satisfaction. Customers think, "I have an older home, and my insurance company is giving me a device that protects me and my family." That's worth something.

ITL:

I saw in your podcast interview with the founder of Whisker Labs that he also talked about how devices like his Ting get smarter as more get deployed. You can see, in his case, that a certain fluctuation in the flow of electricity in a building that didn’t initially seem significant actually shows a risk of fire.

How do regulators deal with this new approach? Do insurers just graft it onto the existing insurance model? Or does there have to be a more fundamental change?

Pete Miller:

That's a very good question.

Insurers understand that this is a cultural shift, and, frankly, there's some resistance. Some people say, "Well, that's not insurance." But the people I've talked to say, "Well, now we're kind of thinking we're risk managers." Insurers will certainly always indemnify clients for losses, but if we think in terms of risk managers then there's a whole series of broader products and services we can offer.

There are other risks that can be mitigated, too, beyond fire and water. Cyber, for one. Insurers can help clients reduce those risks and eliminate a lot of losses. On the commercial side, there are lots of additional property risks that insurers could help reduce. More can be done with telematics in cars.

And I keep going back to the selling point: You, the insurer, proactively stopped something bad from happening.

You didn't just save me my deductible. I don’t want to have to do a bunch of stuff to recover after a loss. If I own a business, I don’t want to have down time.

It's the same reason I go to the dentist, right? Because I don't want all my teeth pulled.

You asked about regulators. Every regulator I talk to says this is awesome. They look at predict and prevent from a consumer point of view, and they say, "You just saved the consumer a lot of heartache."

ITL:

Words to live by.

And it sounds like the opportunities will only grow from here.

Thanks, Pete.

 

Cheers,

Paul

 

For this month’s ITL Focus, on operational efficiency, ITL Editor-in-Chief talked with an old friend and colleague, Jamie Yoder. Jaime is the president and general manager for Sapiens North America, which provides software tools that help insurers transform their businesses.

---

ITL:

My mantra for a long time has been, “Let’s burn all the fax machines.” But I know you take a broader view when you think about how insurance companies can operate more efficiently.

Jamie Yoder:

I go back to the Digital Darwinism paper I published with ITL on the principles of a bionic organization. Too often, companies do little pilot projects to try to operate better rather than systematically understanding how to improve the speed and efficacy of decisions that happen across an organization.

The nice thing about ChatGPT is that it’s put in everybody's minds, “Oh, wow, we could use AI everywhere.” Not that we weren't attacking inefficiency across the board already, but now the ability to take a systematic approach has become obvious.

ITL:

The issue went from difficult to obvious, almost overnight.

Yoder:

I guess that's always the case. Then people get disillusioned, and we revert a bit, but then we find all the real opportunities.

If you step back a bit and look at the full potential of digital and all the advances it’s enabled, those innovations are in the capture, consumption, interpretation and use of data in new and exciting ways. And those new sources and new techniques allow for transformation in three key ways. You can change the way you engage, with customers and others. You can change the way the work is done. And you can change the way your organization changes.

In a bionic organization, the issue isn’t whether the machine or the people do the work. It’s about how they work together to improve the speed and efficacy of every decision and about how you keep improving over time.

When I look at generative AI, I like the notion of it as a copilot. With every task, it can almost be like having somebody there helping you. This is low-hanging fruit. The AI allows you to do the tasks you do every day, but much better and much more readily.

We have a product called Decision that allows you to visualize and manage complex business logic. Building those models can take a lot of manual effort. A simple use case for generative AI – one we’ve already done—is to describe for it what you want as your model and what the logic is, and to have it grab the eligibility rules for a mortgage or a claim or something else. You’ll have to test the model the AI gives you to make sure the logic is sound, but you can deploy it almost instantly.

You can produce an answer engine for agents dealing with customers.

You can use the AI to gather all kinds of different information and at least provide some guidance to augment what you're thinking, in any role from underwriting and marketing to legal, finance and management.

ITL:

I love the idea of AI as a copilot. I’ve described generative AI as providing rough drafts for us humans to finish. How do you make sure a generative AI doesn’t have the “hallucinations” that have sometimes embarrassed users very publicly?

Yoder:

You’re not using the AI to fully automate. You're just using it to sift through an awful lot of things to provide you the basis for a decision.

We used to look at underwriting in the commercial space and say, Wouldn’t it be great if every underwriter was actually a team that included a data scientist? Now, you can have an AI poring through all the information that’s available and winnowing it down. You aren’t just using the AI for efficiency. You’re uncovering information that you wouldn’t have gotten otherwise and making better decisions.

Think of the junior underwriter who gets binders full of information and has to sort through it all, and mostly is just trying to figure out what has been done with similar situations in the past. Now, the AI can do all that sifting and comparing and make sure that junior underwriter doesn’t miss something.

Marketing is another good example. The AI can create a nice outline based on your inputs, which is enough to trigger good ideas and to help ensure you don’t miss any angles. You’re not going to create something and post it automatically, but the AI can get you started, and we humans are good at sculpting once there’s something to work on.

Or think about contracts. Most contract clauses are repeated, so if you have a large language model that’s built off all the contracts you’ve ever done, you can have it do an awful lot of the work on new contracts you’re writing.

ITL:

A great thing is that it sounds like people can do things in the short term, not just in the long term, that can generate efficiencies while building long-term momentum.

Yoder:

In that Digital Darwinism piece, we say you shouldn’t just use AI to look at the stars. You should also use it to pick up the trash. I'll be quite excited to see how generative AI picks up the trash, because there can be a lot of quick wins.

Just train your AI on all the proposals you’ve ever done. 70% of the questions you’re asked are always the same, so the AI can provide the answer, leaving it to a human to provide unique spin as appropriate.

Have the AI look at your submissions and ask it, How have we responded to all the similar submissions we’ve received in the past?

ITL:

A quick digression: People talk a lot about large language models. It seems to me there's also potential for what I think of as small large language models: You train the model on all the data that’s out there on the internet but then bring it inside your company and only give it access to your data, your procedures, etc. Do you see the same thing?

Yoder:

Absolutely. You go wide initially and then give the model deep domain expertise.

ITL:

Glad I’m not hallucinating. What comes after the easy wins, after the picking up of the trash?

Yoder:

Companies need to operationalize the information from the AI within their process flow. Decision modeling is a huge part of what we do, so we look not just at how to ingest all that information but how to augment the intelligence in that human-machine pairing.

Then, of course, we’re going beyond how to do the work and looking at all the below-the-waterline stuff, about how you change the way you change. There’s a huge advantage in development, in testing, in documentation and so on if you can create an environment that lets you continually improve.

As we help insurance companies transform, I keep looking at those three levels: how you engage, how you do the work and how you improve. That last one is key. You can’t just do projects. You have to create an environment that lets you get better and better all the time. You need an operating model that is built to continuously evolve.

You don’t want machine learning. You want a learning machine.

ITL:

A great idea to end on. Thanks, Jamie.