Tag Archives: insurance

Looking to Future of Insurance, Insurtech

Carriers, regulators and solution providers in the insurance industry continue to seek innovative solutions to some of the most pressing issues facing the industry. Following are five priorities for insurance leaders to consider, in tandem with insurtech-driven solutions:

Priority 1 – Global risk: Even before the pandemic, volatility in global risks stemming from climate change, economic uncertainties, cyber attacks and more were at the forefront of long-term concerns for insurance providers. The pandemic has accelerated those debates, bringing both urgency and added complexity to forecasters and those evaluating the macro and micro impact of those risks.

Insurtech Response: Better understanding of weather volatility from storms, drought/wildfire, etc., has long been a focus in the insurtech sector, with firms such as Hazard Hub, Opterrix and Athenium Analytics, to name a few, giving insurers greater insights for underwriting and claims. Insurtech cyber solutions are really just starting to take root and grow. A great example of new cyber solutions is Global Insurance Accelerator (GIA) alum Cowbell Cyber, which is providing AI-driven, customized cyber coverages.

Priority 2 – Diversity, equity and inclusion: The increased focus and awareness around DE&I offers many challenges and opportunities for our industry. From very specific challenges such as the “red-lining” issue facing the mortgage industry and risks of biases in AI applications to macro questions about whom and how we serve, DE&I concerns are just beginning to be explored.

See also: How Insurtech Thrived in the Pandemic

Insurtech Response: While much has been discussed at the industry level about the long-term implications of AI bias, insurtech solutions are just starting to bring new tools to market to assist with not only gaining insights from our data but also helping understand and be on the lookout for unintended consequences based on how we are using that data.

In the nearer term, it is very exciting to see new insurtech solutions providing access and new products to support those who have been underserved by traditional means. For example, Caregiven, an Oregon-based startup and GIA alum, enables providers to offer real-time, curated guidance to individuals and families managing end-of-life care for an aging or ailing loved one.

Priority 3 – Managing expenses: The automation revolution coupled with digital-native upstarts is challenging every carrier to rethink their back-end platforms and processes, staffing and other overhead. Choosing from a long list of solutions to help reduce or mitigate expenses with lofty ROI promises is easier said than done for most carriers.

Insurtech Response: While some carriers are waiting for Internet of Things (IoT), robotic process automation (RPA) and AI to fully prove out their ROI, there is still a lot of activity that promises great returns. The top three approaches are:

  1. The acceptance of chatbots for customer service and sales;
  2. Automation solutions to eliminate some of the repetitive tasks associated with old processes; and
  3. Cost-avoidance solutions to mitigate claims, fraud and legal/medical expenses.

Priority 4 – Growth: The industry as a whole is looking beyond traditional insurance products to add value and attract new customers. Some of these solutions are geared around a better understanding of what policy holders need and strive to make a more engaging customer experience. Others are looking to add coverage for risks historically not covered by traditional policies.

Insurtech Response: There has been a wave of new insurance-backed or insurance-related products that are helping consumers and businesses get the protection they require, often at the exact point in time they need it most. Carriers are starting to provide the paper to managing general agencies (MGAs) or developing their own products for risks such as instant-on travel or experience insurance, insurance-backed warranties for consumer products, home warranties and even specialty coverage for solar panels and other environmentally-friendly products.

See also: Why Open Insurance Is the Future

Priority 5 – Transformation: Just a few years ago, insurtech wasn’t taken seriously by most carriers. Today, more and more are realizing they could be facing “adapt or die” scenarios. The bottom line is carriers that are not becoming more agile and digital-first could soon be extinct.

Insurtech Response: One of the main reasons carriers are hesitant to engage with start-ups is the risk associated with the unknown. Statistically, only one in 10 makes it, and 70% fail within the first five years. Fortunately, more and more carriers are responding by not only investing and nurturing these companies to help them be successful, but also redesigning their own systems to be digital-native and enabled by application programming interfaces (APIs), allowing greater agility to plug-and-play new or different solutions as the market changes.

Not coincidentally, all five of these priorities will be explored during the 2021 Global Insurance Symposium, to be held on June 28-30, with the first two – global risk and DE&I – as key themes for keynotes and panel discussions. For a full line-up of speakers and conference information, visit www.globalinsurancesyposium.com.

From Risk Transfer to Risk Prevention

Recent developments in technology and the corresponding availability of data can improve risk prevention. A key driver is the Internet of Things (IoT), the growing network of connected devices ranging from consumer wearables to industrial control systems.

According to a recent report by Kaspersky, 61% of enterprises already use IoT applications. So, nearly two-thirds of insurers’ corporate customers can potentially integrate IoT data into insurance services. And a recent study by Aviva revealed that the number of internet-enabled devices in the average U.K. home has increased by 26% in the last three years to over 10.

Insurers can use the newly available data from IoT applications to reduce risks for customers, whether directly – through real-time risk mitigation solutions – or indirectly, by promoting safe behaviors over a longer period.

Prevention services are not new in the insurance industry; for years, insurers have provided consumers with loss prevention advice, and risk-engineering teams advise businesses in commercial lines. Ways to prevent risk, however, are changing. IoT allows risks to be better managed. This can be seen as the very essence of the evolution from pure risk transfer to a “prescribe and prevent” scenario.

See also: The Human Risks in Insurer/Broker M&A

Real-time risk mitigation

Real-time risk mitigation results from the direct use of IoT technology and can either consist of:

  • Automated actions by IoT actuators that affect the risky situation without any human intervention, like autonomous driving systems in cars, or
  • A warning to trigger some kind of human intervention, such as a water leakage alert that activates an emergency repair service.

These risk mitigation actions can be triggered by the detection of three different situations:

  1. Missed safety tasks, such as scheduled inspection or equipment that needs preventive maintenance, or a diabetic patient who has left insulin at home or missed a check of blood sugar level.
  2. A risky situation, such as a frozen pipe; a cold storage door that has been left open; spilled liquids on a supermarket floor; workers without adequate equipment in the workplace; unsafe lifting by an employee; a distracted driver.
  3. The consequences of an event that has already happened, such as a water leak; an unsafe worksite; an injury; or the failure of a patient to adhere to a treatment. A mitigation action is then initiated by the IoT system.

Real-time risk prevention is most mature in commercial lines, driven by the loss control culture present in commercial insurance. Field inspections by engineering teams are well-established, and enhancing this work with new technologies seems like a natural step.

A few personal auto insurers around the world have integrated real-time warnings in their telematics programs. This live feedback – from line departure warnings to alerts about coming risky intersections – influences driving behavior and allows insurers to reduce expected losses.

Water leakage sensors are one of the most cited preventive services in home insurance. However, as of today, insurers have struggled to introduce approaches that generate substantial demand and a sustainable business case. Finding a sustainable business case in the smart home insurance market is challenging, but innovations should make homeowners the ultimate winners.

Figure 1: Leveraging IoT data for multiple use cases

Source: IoT Insurance Observatory & The Geneva Association

Bundling risk prevention with other customer services, such as security, has been the most successful approach to date. The sustainable business case is built on a bundle of different services – some sold after the purchase – and on the reduced churn rates built through customer engagement.

Life and health is the least mature field for real-time risk mitigation services. There have been many insurance pilots over the past few years around early detection, care optimization and medication adherence, but only a few examples have scaled to market level. Reasons for the slow adoption include: 

  • Health costs in most countries are not fully covered by insurers but by a public health system. 
  • Entering into the medical device space would mean entering into the medical regulatory field. 
  • Medical advice comes with significant responsibility and requires deep and specialist knowledge.
  • Execution at scale needs insurers to deal with many different medical service providers. 

Real-time risk prevention services and approaches to them are very heterogeneous. The only common denominator is that all successful services are based on a multi-year journey. 

Promoting less risky behavior

The second way to prevent risk is to encourage less risky behavior. Insurers have a role to play in creating a positive safety culture and raising awareness in society.

We can distill a three-pillar concept from the successful examples: 

  • Pillar one: Create awareness of the current risk level
  • Pillar two: Suggest a change in behavior
  • Pillar three: Offer incentives for changes in behavior

The sustainable adoption of safer habits for the benefit of all stakeholders can only happen when all three pillars are successfully implemented. 

The first two pillars are closely linked and depend on feedback to customers. Awareness of the current level of risk leads to the question: What change will make the activity safer? Before changing our behavior, we need to be aware of our current behavior.

Raising awareness of risky behavior and identifying ways to change it are not enough. There is a need to encourage people to instigate real and sustainable changes in their behavior through rewards.

The customer’s perception of the value of the rewards, their cultural context and frequency and the intersection with behavioral economics are all integral. Changes in human behavior are also instinctive; A combination of behavioral economics and gamification to engage individuals is therefore needed to help to drive behavioral change.

The most mature business line is life and health. Fully individualized suggestions and challenges are provided to customers based on the number of steps registered by their mobile phone or physical activity data from wearables.

In personal auto telematics, customers often receive a detailed analysis of their driving style via a dashboard in a mobile app. Many insurers also automatically display tips for improving the driving score, or introduce contests on specific issues – so-called leaderboards. 

See also: Despite COVID, Tech Investment Continues

In commercial lines, IoT data is being used to enhance the activities of the loss control teams and to provide periodic safety insights to risk managers and supervisors of the insured companies.

The real-life case studies on promoting safer behavior afforded the following key findings:

  • The reward system needs to be set up to reinforce positive behavior. The reachability of the reward is key. 
  • There are cultural aspects to incentives. It is important to find compelling benefits and rewards that engage target customers. What works in one country does not necessarily work in another. The rewards must be explicit and tangible. For example, monthly cashback on fuel costs is effective, but a free weekly coffee also materially influences behavior.  
  • Frequency is key. A yearly premium discount is not enough. Positive engagement must be nurtured on a short-term basis. This mechanism gives people a reason to come back to the platform. 

Enablers of prevention services

The integration of technology into prevention services greatly increases complexity. As a result, the enablers for success are the effective business transformation, cultural change and understanding of the corresponding financial management rather than the technology itself.

Figure 2: Complexity of the financial management of IoT-driven prevention services

Source: The Geneva Association

We identified the following as the main success factors:

  • C-level commitment
  • Development of vision and strategy
  • Development of culture and capabilities
  • An effective value-sharing scheme with the customer
  • Management of new and complex financials

Several new elements need to be considered in the financial management of this new paradigm, such as service fees, partner contributions, self-selection effects and net IoT costs, which are harder to integrate into the economics of traditional insurance products.

The full report from which this article is derived is available here.

Pandemic’s Lessons for Auto Insurers

It’s impossible to deny the profound impact that the pandemic has had on every person and every business, and the U.S. automotive insurance industry was no exception. Previously mundane errands such as a trip to the grocery store became a battle for survival — and toilet paper — and once-gridlocked highways were replaced with barren stretches of asphalt. 

On a more serious level, the global health crisis sent shockwaves of financial uncertainty across the nation that was also addressing considerable emergencies on both the civil and environmental fronts. 2020 quickly cemented itself as a year for the record books, and not for good reasons. 

However, as tough as COVID-19 has been, there are hidden lessons in connecting and analyzing what would otherwise have been viewed as dissociated events. And when compared with years past, the auto insurance industry can turn these pandemic-led transformations into actionable insights for the industry to evolve and adapt to meet future disruptive events. Our recent Auto Insurance Trends Report focused on analyzing the “new normal” of consumer behavior, which has a direct correlation to critical carrier-related factors such as underwriting, claims and those actively participating in telematics exchanges or usage-based insurance programs. 

Here’s what we found:

Empty Roadways Bring Out Lead-Footed Drivers 

When looking at the initial timeline of the pandemic, the sweeping stay-at-home mandates and shutdown orders across the country created a drop in miles driven of over 40% from March to April 2020. Even normalized mileage hovered between 83% and 88% of 2019 levels for the second half of the 2020 calendar year.

The empty roadways, particularly at the beginning of the pandemic, enticed many lead foot drivers, who took the opportunity to turn highways into personal drag strips. The first spike in elevated speeding rates occurred around mid-March of 2020 and remained 110% of 2019 data recordings for much of the remainder of the year. With those open roadways, drivers favored their accelerators over their brakes, resulting in a drop in hard braking instances during that same observed period. 

See also: How Insurtech Thrived in the Pandemic

Gen-Z Drives DUI Trends 

As part of our generational data insights, where we examined driving behavior across multiple age brackets, we discovered a particularly troublesome trend among our nation’s youngest drivers, Gen Z. 

Classified as ages 22 and under, Gen Z drivers ranked the highest when observing violation data across DUI infractions, overtaking those in the Traditionalist age group (ages 76-plus) who were the highest offender the year prior. While restaurant and bar closures led to a potential overall reduction in total figures, the months of April and May 2020 indicated an approximate 50% increase of DUIs among Gen Z drivers. 

Collisions and Claims Down, Severity on the Rise

When looking at the onset of the pandemic in 2020, much like the reduction in total miles driven, the volume of collisions and subsequent claims experienced considerable drops. With a 19% drop compared with 2019, one of the emerging trends throughout the entire year and particularly heightened in October was the inverse relationship between lowered claims figures and increased bodily injury. Comparing the months year-over-year, 2019 saw 7.1% growth in severity, while, in 2020, that figure rose to 12.7% despite the fewer miles driven. 

Naturally, the onset and continuation of a global pandemic will profoundly influence consumer behavior, including driving patterns, creating new challenges for the businesses that are so closely related to such behavior. However, from those challenges, pandemic-led transformations such as enhancing virtual claims capabilities have shown how the industry can adapt and improve. 

Adapting to Tackle Future Disruption 

While telematics has been around for years, 2020 and the pandemic brought a new sense of urgency to better understanding driving behavior. For insurance carriers, leveraging telematics and deploying usage-based insurance programs provides an incredible solution to accommodate changing driving behaviors. By way of stronger analytics and timely data reporting capabilities, telematics programs assist in taking the guesswork out of how to accurately calculate the risk propensity of an individual. 

As drivers continue to show interest in pay-per-mile programs as part of fluctuating driving patterns, telematics and usage-based insurance (UBI) programs give consenting consumers the opportunity to be priced more accurately than a traditional risk pool would dictate. This can increase customer satisfaction and allow an insurance carrier to be a competitive differentiator with a way to stay ahead of the curve of future disruptive events. 

The same can be said for almost all data or analysis during this strange but insight-full period of history. Understanding the motivations and connections between such events and human behavior will highlight both vulnerabilities and opportunities to grow. The pandemic will continue to affect virtually every market imaginable, not just now but for months and potentially years to come. The important aspect to consider is how to best adapt and evolve for the road ahead.

Better Models for Next Pandemic

Natural catastrophe risk models have revolutionized the property/casualty re/insurance business over the past 30 years. They have allowed more efficient deployment of capital by providing a rigorous way of estimating potential losses, better quantifying the tail and increasing trust in the probabilities assigned to natural disasters and the damage and losses they produce.

All of these models have been developed from common assumptions: An event happens and produces impacts on a known (although somewhat uncertain) exposure (property or other fixed asset), which has a known (although, again, somewhat uncertain) vulnerability to the consequences (hazard) of the originating event. Using an intricate mix of physics (through natural science and engineering lenses) and statistics, such models produce insurance loss estimates that are, generally, robust and defensible.

As new systemic and non-natural risks have emerged, establishing the potential future loss range of perils, such as terrorism and cyber, has required the introduction of social science disciplines (and greater levels of uncertainty) but did not greatly disrupt the established logic of the cat model; the components and controls remained familiar.

Not so infectious disease models. First introduced to the insurance sector to capture excess mortality from global pandemics in the life insurance business, they began as a combination of stochastic elements of natural catastrophe models with a well-established form of epidemiological model, the Susceptible – Infectious – Recovered compartmental model (and its many and varied siblings).

Unknowns

From a traditional cat modeling perspective, there remained a lot of unknowns. For example, the two components of “hazard” – location and intensity – were both poorly understood, thanks to a very sparse and poorly documented experiential history and only a rudimentary understanding of the zoonotic viruses that are the dominant cause of epidemics and pandemics.

And the model architecture required was more Gaudí than Brutalism. There is no fixed exposure or vulnerability; both are dynamic and feed directly back into the model in its next time step. And exposure and vulnerability are not controlled by engineering equations, they are assumed impacts of political decisions and human behavior, of travel webs and social networks.

The Sars-CoV-2 virus has brought epidemiological modeling to our living rooms (many doubling as home offices). Previously obscure epidemiological modelers have become household names, and the concepts of reproduction numbers, non-pharmaceutical control measures and even herd immunity have become all too familiar. Covid-19 is by far the best-documented pandemic ever, but even after many months of live information being available (although to widely varying degrees and with a broad quality range) to calibrate forward-looking models of case counts and mortality, inconsistencies and uncertainties abound.

See also: Transformation of the Risk Landscape

Epidemic forecasting, by nature, is a tall order. In some cases, these model inconsistencies are due to different assumptions that necessarily change as new information becomes available. Another reason model outputs may not reflect future outcomes is because there is a feedback loop dynamic – models affect reality. If a model predicts a dire outcome, it may in fact prompt decision makers and even the general public to change their behaviors, thereby changing the final outcome.

Further challenges are found in the conversion of pandemic model outputs to the short-term economic impacts of interest to P&C re/insurers. The literature on the economic impacts of pandemics is extremely sparse (although this will change) and dominated by economic simulations that sit on top of epidemic simulations, rather than empirical data. The consequences of government policy responses (like lockdowns) and sociological dynamics (fear, social distancing) are generally not economic outputs from models but input assumptions driving the direction of the reproduction number and, ultimately, the outcome of the epidemiological event.

As one moves from modeling a single event to the full probabilistic modeling familiar to the re/insurance industry, additional challenges must be addressed.

We think near misses are frequent in real life and must be captured via counterfactuals in the modeling domain; two coronaviruses with very similar characteristics emerging in very similar locations can lead to very different global outcomes, at the whim of individual actions – by patient zero, a head of state or many people in between – impossible to fully capture stochastically. Big challenges remain in quantifying public policy and behavioral elements that shape the nature of risk; these, too, need to be mapped out as they evolve over time and then linked to biological and epidemiological modeling frameworks.

Lessons to learn

Progress is being made, however, and Covid-19 learnings will help, although the temptation to model to the last big event has to be closely managed. The next pandemic will most certainly be different in character.

There have been significant advances in our understanding of the nature and spatial distribution of zoonotic viruses that pose the greatest risk of spilling into human populations and igniting pandemics. Improvements in biosurveillance have also shed new light on the rate of spillover, which is critical to characterizing high-frequency events, as well as the tail.

There are also continuing advances in modeling methodology, ranging from the incorporation of socio-political factors to capturing population movements. And there is still work to be done. The assumptions required to construct a probabilistic pandemic model are hugely influential on outcomes but are now based on expert judgments that are art as much as science and vary (often in ways that are not readily quantifiable) from modeler to modeler. The use of structured expert judgment to quantify and constrain uncertainties in such assumptions – and thus in model outcomes – is an area of development that carries promise from successful deployment in other contexts and, alongside other innovations, will help to build a level of trust in pandemic models that approaches that found in nat cat models.

See also: Benchmarks, Analytics Post-COVID

Despite present and future scientific and modeling advances, the full benefits will not be realized if there is a failure among decision makers to effectively use data and analytical tools as part of their decision-making process, whether it be to inform preparedness or guide response activities.

In the context of the global re/insurance market, it must be recognized that while modeling infectious disease risk is challenging and will take time and resources to build the level of trust found in nat cat models, there are already pathways to gain an understanding of the risk. This present understanding is sufficient to support tangible innovation – policy experiments, insurance structures, refinements to preparedness and mitigation strategies – within both public and private sectors. Ultimately, further innovation will be necessary (and is entirely within our grasp) if we hope to better manage the financial and social consequences of future epidemics and pandemics.

Budweiser’s Intriguing Stunt

Budweiser Canada garnered attention in recent days by teasing the possibility that it was entering the insurance market. How would that even work for the brewing company? What kind of insurance? How would Budweiser underwrite it? How would it process claims?

A few days later, Budweiser said it was really just setting up a raffle, with “barbeque insurance” as the prize. If something happens to cancel your barbeque — even if you messed up and ran out of propane — you can file a “claim” with Budweiser. The company will then randomly select winners, who will receive as much as $2,500 in “insurance.”

But, even once it was unmasked as a publicity stunt, the Budweiser announcement raises intriguing possibilities for “embedded insurance” and for the use of application programming interfaces (APIs) to build whole ecosystems.

I suppose the first takeaway is simply that the insurance industry should be flattered. Lots of companies engage customers with raffles of iPhones or other sleek electronics; here, a major consumer brand is enticing customers with … insurance. Who says insurance can’t be sexy?

The broader point is one I’ve been making for years now, that the best form of cross-selling known to man is, “Do you want fries with that?” In this case, Budweiser’s offer boils down to, “Do you want some insurance with your beer?” Travel insurance already embeds itself into the purchase of airplane tickets, hotel reservations, etc. — with the website scolding you for putting yourself in danger if you don’t pony up that 10% or so on top of the basic price. Warranty offers have long been embedded in product sales, and some other cross-selling possibilities are also obvious — buy a car, and be offered car insurance; buy a home and be offered homeowners insurance; etc.

And, if beer and insurance go together, then the possibilities for embedding insurance into other products are really just limited by our collective creativity. If Budweiser can offer a sort of insurance against cancellations of barbeques, why couldn’t Hallmark or some other company do something similar for family gatherings? Why couldn’t a sports team offer “insurance” for season ticket holders by raffling off some free season passes for the following year if the team has a losing record? And so on. (I’m from Pittsburgh, and I considered bragging that I wouldn’t need losing-season insurance, because my Steelers have only had one since the leagues merged in 1970, but my Pirates once had 20 straight losing seasons, so….)

APIs make this sort of cross-selling far easier, because they allow for exchanges of data in a clearly defined way between different businesses — the travel insurer and the airline, the car dealer and the car insurer, etc.

APIs also allow for opportunities well beyond simple cross-selling. Look at Budweiser. To apply for its “barbeque insurance,” you have to fill out a form on the company’s website and provide some information about yourself, including a way to contact you. (At least that’s the theory; after several minutes at the URL provided in the Budweiser announcement, I couldn’t even find a reference to insurance, let alone a way to sign up for it. As I write this, the site is just straightforward marketing.) Once Budweiser has a way to contact you, it can continue to try to sell you beer from time to time. But it can also connect into an ecosystem that might sell you even more. You need some chips and dip to go with that beer, of course. How about some hot dogs and hamburger meat, too? Wine? Don’t forget the ice. Maybe you don’t want to have to run around and get all the supplies at the last minute, so how about if we connect you to a delivery service? Maybe even a caterer?

Once you establish a software interface with another company or set of companies, you can develop any number of relationships. Homeowners insurance could become part of a whole ecosystem involving maintenance, security, warranties on appliances and more. The same with car insurance, with small-business coverage and with most other types of insurance — none live in a vacuum, even though we in the insurance industry often approach them as though they do.

Figuring out your role in an ecosystem can be tricky. The tendency is to think of yourself as the main player, controlling the relationship with the customer, a la Amazon. But there are actually lots of types of ecosystems and plenty of potential ways to participate, including by plugging into an ecosystem that some other company has already taken the time and energy to organize.

That’s a long discussion that I’ll save for another day. In the meantime, I hope I’ve given you a little food for thought as you drink your beer at that barbeque this weekend.

Cheers,

Paul

P.S. My Budweiser story:

When I was with the Wall Street Journal in Brussels in the mid-1980s, we ran a front-page story about a fight over trademarks between the U.S. Budweiser and a Czech beer with the same name. The Czech brewery certainly won on precedent: It was founded in 1265. The American version had even plagiarized the Czech slogan: Czech Budweiser was established by a king of Bohemia and had for centuries called itself the “beer of kings,” while American Budweiser labeled itself the “king of beers.” But the American company was claiming that it, not the Czech brewery, somehow had rights to the name.

I vowed that some day I would have a Budweiser in Prague.

I never made it while living in Brussels, but the idea kept rattling around in my head. So, following a talk I gave in Zurich three years ago, I drove the 425 miles to Prague to have a beer.

It actually took me a bit to find a Budweiser. To my surprise, pubs in Prague only serve one kind of beer, so I couldn’t go into any old bar: I had to find one dedicated to Budweiser. But after waiting more than three decades for that beer, I wasn’t going to give up easily, and soon enough I found a patio festooned with Budweiser umbrellas. The beer was glorious.

It came in a beautiful mug with “Budweiser” etched into the glass, and I just had to have it. I wasn’t going to pocket it, but I had to have it. I had zero idea how to proceed, but I have a friend who manages to get chefs and maitre d’s to give him the most remarkable souvenirs, so I texted him and asked for advice. “Ask the waiter if you can buy a mug,” he responded. “90% of the time, he’ll just give it to you.” Sure enough. I left the waiter a $5 tip for a $3 beer, and I have a lovely Budweiser mug on my book case. I’m looking at it as I type this.