Back in 2016, Microsoft reported that more than one billion people used its Office suite of applications, which includes the Microsoft Excel spreadsheet. Roughly one in every six people on Earth uses it, Microsoft said. There is no doubt that, when Microsoft first developed this application, the company was on to something important, and Excel still has its place within the Office suite for many users. Of course, Microsoft is not the only company that has produced and promoted spreadsheet programs; there are literally hundreds of similar programs, typically sold as single-seat, stand-alone applications not tied to any common database. For some companies, spreadsheet programs are chosen for their lower cost, ease-of-use and routine reporting. In fact, over the years, employees have become so familiar with spreadsheet applications that they simply don’t want to work any other way. Some call this phenomenon “spreadsheet paralysis,” which refers to the insurer’s common argument that it’s always been done this way, so why change? Reliance on spreadsheets seems especially common with smaller insurers, self-insured groups, public risk pools and their agents, all of which use spreadsheets to calculate and process data. The reason these insurers hesitate to phase out spreadsheets as a primary way to process data is because this dated technology works for what it was designed to do. When technology budgets are formulated, the typical question becomes: Why should we change if it works? It’s easier to simply continue; no extra training is required, and no changes are required in how workflow is managed. See also: How Agencies Can Use Data Far Better   Or are they? What happens when a spreadsheet error is found that affects an entire book of business and puts routine financial and regulatory reporting in a state of instant jeopardy? How long does it take to backtrack, examine the various versions of the spreadsheet’s values, calculations, source data and file history, and correct the error? Or, consider what happens when the policyholder demands something that spreadsheets can’t produce. These demands can arise within customer service, claims or billing, and we know these demands are increasing, leaving many smaller insurers in a scramble to shorten steps, access more information and respond in real time to customer requests. Let’s look further at billing. The complexity of this functional business unit requires access to data points on all things related to customers, their demographic information, policy history, premium, invoice amount and payment. When working in a common spreadsheet application, only one person at a time can access and edit this data, making real-time collaboration with other members of the billing department—much less the other pertinent business units that also touch the customer—impossible. If the application resides within the insurer's intranet, the data being manipulated by the last individual to touch the file most often represents data that was generated by multiple other sources and users, making accurate reporting difficult and delayed. For public entities and other self-insured groups, this problem could play forward to an audit disaster. Even if reporting is 100% accurate, the spreadsheet approach does not take into account the critical importance of secure storage or backups of the data, which, as part of the organization’s core business applications, are typically kept onsite, putting the entire system in harm’s way should a natural disaster occur. Like larger insurers, smaller carriers, self-insured and public entity groups have an opportunity to process data collaboratively, respond faster to customer and auditor demands and operate in a secure environment where data is controlled, available and safe from compromise. Because this method takes into account the importance of software as a service over a hosted cloud platform, data encryption (in transit and at rest) is employed to ensure comprehensive compliance and quality assurance. This method also gives users greater control over calculations and provides audit trails to document every aspect of data access manipulation. The result is an immediate reduction in formula error, improved ability to perform all the routine tasks that a spreadsheet once did and the ability to perform sophisticated risk analysis based on accurate, real-time information. And because it’s a hosted application, output of the right information to the right audience is guaranteed when and where they need it. See also: Understanding New Generations of Data   Yes, there will be some hesitancy among users who are comfortable with the spreadsheet status quo. But the reality is that spreadsheets were never intended to be used to run the business, only support it. New technology enables insurers to do exactly what they had been doing with spreadsheets… and more. From the insurers’ point of view, having modern technologies is a great enticement to the hiring of new technology workers who are looking for the latest solutions to competently do their jobs. At a time when smaller insurers, self-insured and public risk pools are feeling the pressure to become “digitally transformed,” the move away from stand-alone spreadsheets to a collaborative, cloud-based enterprise solution is a great first step.

The insurtech movement spawned what some have called a cottage industry, others a revival, of insurance-focused conferences. While there is now an abundance of choice, not all events are created equal: Some may be strong in networking opportunities but light on valuable content, and others may have great topics but constrain the time, or load panels with so many speakers it is impossible to explore topics in depth. It can be a balancing act to get the right mix of networking and education that delivers the best experience for participants, and not all events succeed. One event that gets it right, which is why I'm happy to serve as emcee, is InsurTech North. When insurtech conferences began to spread, about five years ago, participant numbers were fairly low, and speakers often shared information that left audiences feeling like they were hearing about tech possibilities in some distant future, far beyond the reach of most insurance industry companies. The cultural divide between early-stage companies making big claims about the promise of technology and incumbent insurance carriers hearing these forecasts was so great that many underlying messages benefiting both entrepreneurs and insurers often got lost in translation. For example, many insurers downplayed the potential impact of entrepreneurs, who obviously needed more insurance experience. Likewise, the numerous warning signs to that first generation of insurtechs could not have been brighter or clearer with respect to the need to build new capabilities for an industry that generally was change-resistant, risk-averse or both. The explosion, and subsequent washout, among the first generation of telematics start-ups, for example, yielded multiple lessons for entrepreneurial teams who were tuned in to how to work with the insurance market. As the number of insurtech firms grew exponentially, so did the number of conferences one could choose from to learn about this sector. At this point, if you wanted to attend a conference a week, and sometimes two, you could easily be occupied for at least two years without repetition. See also: Insurtech’s Lowest Common Denominator As with any growth market, the more that firms raced to provide solutions, the less differentiated they became from each other. Such is also the nature of the insurance conferences related to innovation and technologies, with their increase in volume watering down meaningful insights and impact. So, what makes a conference like InsurTech North stand out among the crowd and rise above the noise and familiar formulas? Let’s start with the speakers. It’s not the standard “pay-to-play” model. Sponsorships are not the primary source from which panelists and moderators are chosen. The best events select speakers based on their subject matter expertise. Where sponsors are tapped to moderate a panel, they are carefully paired with the expertise and experiences needed for a session. More valuable events also limit the number of panelists per session to allow more time for meaningful information and dialogue with the audience. A common feature at many insurtech events is the “pitch-fest” competition. Conference organizers who wish to energize and inform their participants often organize some form of showcase or competition for early-stage ventures to make their case to the insurance market. These are typically exciting and informative agenda sessions. The organizers of InsurTech North have taken this concept and made a meaningful enhancement by reaching out to a select group of respected accelerators and venture capitalists to nominate one early-stage company from within their portfolios. This approach provides an opportunity for participants to hear from entrepreneurs with solid concepts and a high propensity for success. At the inaugural InsurTech North event, in 2018, attendees literally witnessed history in the making when data and analytics start-up QuantumSense, nominated by Holt Fintech Accelerator, made its first pitch during ITN’s “Wolf Pack Competition.” Here was a pitch from a live start-up built on a working quantum computer platform. This was a genuine leading-edge capability, with a team who then remained at the conference to field questions and seek support. It’s worth noting that the competition was so fierce that another startup, Maple, representing the MaRS Investment Accelerator Fund, won the judges’ vote and JAUNTIN', from the Global Insurance Accelerator, won the audience vote, showing the depth and quality of companies on the stage. See also: Predicting the Future of Insurtech   The most compelling events offer an informative and actionable agenda, designed for the benefit of the audience, speakers and sponsors. The dialogue in the sessions is candid while balancing strategy and theory with sound tactics. The events provide time for networking and deal making. Last but not least, the event is warm, welcoming, genuine and just plain fun. InsurTech North embodies these qualities and is worth attending. I hope to see you there. InsurTech North’s 2019 conference takes place May 23-24 in Toronto. Sign up using code “ITLten” to receive 10% off your registration fee. To learn more and register, visit https://insurtechnorth.com/

The shock of the global financial crisis put life insurance, as an industry, on the back foot. A period of de-risking and retrenchment inevitably followed as companies looked to consolidate their positions and weather the storm. A decade on, and much of this necessary work has been done. The life insurance sector is on a much firmer financial footing, and growth is back on the agenda. Indeed, for many providers, growth is now vital. An aging population means mounting payouts, thanks to policies of old that were designed when data painted a different picture of risk than the one we see today. Securing new sources of revenue is crucial. There is a huge problem, however: The “pie” of potential customers for traditional life insurance offerings simply isn’t what it used to be, and grows smaller every year. Whereas in the mid 20^th century it was something that most households purchased as a matter of course, younger generations tend to view it as optional, at best, if they even consider it at all. The number of U.S. households that hold life insurance of some sort is now at a 50-year low, and according to trade association LIMRA some 38 million households don’t have any form of life insurance at all. According to a recent study by the same organization, less than 20% of millennials indicated an interest in purchasing life insurance at any point. Two Options So life insurers looking for new revenue have two options. They can try and increase market share of the existing, dwindling pie by wrenching already-engaged customers from rivals in an increasingly competitive environment. Or, they can look for new ways to engage new customers—to grow the pie, so to speak. While the first option is the easy route, any truly ambitious provider needs to think about ways to achieve the second, and the future of the sector in general will depend on re-engaging new generations of customers. The challenge is engagement. The need for life insurance, and the protection it provides, hasn’t changed. What has changed, dramatically so, is how people consume information and purchase products and services. Much of this change has been underpinned by advances in online and digital technologies. Unfortunately, the decade of retrenchment following the crisis also meant life insurers stopped innovating. The sector has always had a reputation for lagging slightly behind the times. More than anything else, growing the life insurance pie will mean embracing the new technologies. The process of gaining and keeping new customers can be broken down into marketing (funneling people to the point of potential sale), policy creation and sale, and then customer retention and upselling. Digital technology holds the potential to transform each stage and how they relate. On the marketing front, part of the problem with the current set-up is overreliance on brokers and agents, who have incentives to target wealthier households of the sort that still routinely look to buy the product (the dwindling pie). But newer forms of media, such as social networks—as well as the sheer ease of sending slick and professional communications in the digital era—can enable a more direct-to-market approach, allowing insurers to supplement their relationships with brokers by directly targeting the lower-income and younger households that the existing model systematically overlooks. Research suggests that younger generations commonly perceive life insurance to be far more expensive than it is in reality—what better way to bust this myth? See also: Digital Distribution in Life Insurance   On the policy design and sales front, younger, online consumers expect flexibility, speed and convenience above all, and are far warier of large or long-term financial commitments than their forebearers. Modern advanced, automated underwriting capabilities can allow for the creation of a far wider range of niche, temporary policies, as opposed to the one-size-fits-all blanket coverage the industry is used to trafficking in. For example, individuals who engage in extreme sports at the weekend may not currently be interested in buying a whole life insurance policy—but may well see the value in taking out time-limited policies for the periods when they know they will be engaging in these riskier activities. By getting new customers into the ecosystem in this piecemeal fashion, the task of then getting those individuals to add coverage, or convert over to full-blown policies, becomes that much easier. The Role of Data and Automation Data and automation can play a key role in efficiently connecting these two steps together. Part of the reason the industry has avoided lots of smaller, niche policies is that most will not apply to any given customer at any given time. But targeted, direct, data-driven marketing on social networks and other channels can get around this limitation, to ensure that potential customers are offered what they specifically need, when they need it. To take the previous example, a Facebook group for extreme sports enthusiasts could be an appropriate channel. Or the appeal could be taken further still, down to an individual level. Part of the problem the industry currently has with engagement is that it still relies heavily on the old-fashioned approach of an annual sit-down with a financial agent or some other intermediary. The ability to identify customers in specific situations and engage them in a timely fashion—someone who has just gotten married, or is buying a house, for example – could prove a huge advantage, and the tools needed to achieve this now exist. The sales process itself also needs to be brought in line with modern consumer habits. In a world where people are used to being able to buy most things at the touch of a few buttons on a desktop or mobile device, this approach needs to apply to life insurance, too. Rather than use reams of paper forms filled with incomprehensible and irrelevant information, the process should be simple, user-friendly, and quick. Advanced, automated, real-time underwriting technology will again have a key role to play here, and this is one area where life insurers can learn from other insurance sectors that are further along this road. With modern technology, the entire process of policy creation and sign-up, from start to finish, needn’t take more than seven minutes. It is the third stage, however—customer retention and upselling—where digital technologies could arguably play the most transformative role in redefining the relationship between life insurers and their customers. There is much excitement about the potential of apps and wearables. A regular stream of real-time data from an individual, relating to, for instance, heart rate or exercise levels, could be very useful for designing precisely the sort of tailored, niche policies mentioned above, as well as for the purpose of targeted marketing. But why should the information only flow in one direction? The life insurance industry has always centered on the collection and analysis of data, and embracing a new digital approach will increase the volume—and individualized nature—of that data by orders of magnitude. The life insurer of the future may have more precise information regarding the health and habits of customers than the individual themselves, their doctor or anyone else. ‘Reciprocal Intelligence’ So why not give back? This is where what we call reciprocal intelligence will come in. As well as providing the core insurance service, insurers could provide regular updates to customers regarding their own data and information – for instance, a message could inform a customer that she's reduced her average heart rate by X over Y period, or that her exercise levels have dipped by Z amount. This could be tied to incentives, function as a health warning or even connect to policy design through, for example, targets to reduce premiums. It would allow insurers to engage with their customers on a regular and meaningful basis, in a non-sales-oriented fashion, as opposed to the far more remote, irregular and formal relationship that has become traditional. This in turn would create far more opportunities for firms to educate and inform customers of the benefits of more comprehensive policies, as well as for more targeted upselling. All of this potential for change carries big implications for insurance firms themselves, in terms of structure and culture. Utilizing these new approaches will mean accumulating and processing huge amounts of data, and then knowing how to use it to maximum effect. It means that life insurance companies will need to become tech-savvy to the core, on an institutional basis. As has often been said in the era of Google and Amazon – “We’re all tech companies now.” See also: New Phase for Innovation in Insurance   Firms looking to engage new customers will need to give serious consideration to the question of how to recruit, or collaborate with, those with the right skills and talent. This will require a top-down element: Firms wishing to lead the way will need to consider creating digital departments, including new board level positions encompassing responsibility for delivering digital strategies (head of reciprocal intelligence included). Firms will also need to work to find ways of integrating the right technology solutions into their businesses. There are already numerous insurtechs that address some of the most pressing problems along the insurance value chain, so collaboration is key. There is no real reason why life insurance shouldn’t be as much of a standard part of life as it was for individuals in the 20^th century. This isn’t a case of VHS being surpassed by the internet, or steam by electric. It is a matter of technological and cultural adaptation to new habits and ways of living. Those that fail to adapt will find themselves scrabbling over an ever-dwindling pie of revenue. Those that do adapt, that grow the pie, could find themselves at the forefront of a new golden age for life insurance. You can find the article originally published here.

Now that we're a good couple of years into the fascination with the potential of blockchain, some breakthrough uses should be popping up, right? Instead, we're starting to see articles like this one from McKinsey that suggests scaling back ambitions, at least in the short run. What gives?

Some of the disappointment may be inevitable. We've all seen the hype curve and know that new technologies, especially ones with as much potential for fundamental change as blockchain, often produce massive expectations, only to then descend into the Slough of Despond for months or years, before reaching their destiny. 

Some of the pushback comes because blockchain's limitations are becoming clear—it's computationally intensive enough that it's not well-suited to massive storage of information, for instance—and because alternative technologies can solve many of the problems that were initially assumed to be the province of blockchain.

But if you'll permit me a geeky analogy, harking back to my days covering the world of technology for the Wall Street Journal in the 1980s and 1990s, those driving adoption of blockchain are behaving too much like Unix and not enough like Linux.

Unix is a well-regarded operating system that became positively adored by almost every major computer company not named Microsoft. Windows had achieved a monopoly on all but the modest number of personal computers then sold by Apple. Companies needed a way to compete with Microsoft, so they rallied around Unix, a key version of which was in the public domain. The problem—and the lesson for the insurance industry—is that just about every company tweaked that version of Unix. 

Something isn't really a standard, is it, if I have my version, you have yours and Sally down the street has another?

Eventually, people in the industry realized they were ceding the key advantage to Microsoft—any program written for Windows could run on any PC-compatible, while programs written for one version of Unix had to be revised before they could run fully on another version. So, the industry formed a consortium to produce a single "kernel" for Unix—and everyone tweaked that. 

I can't even tell you how many presentations I sat through from IBM, HP, Sun, etc. about how their version of Unix was the best, or how little response I got when I argued that the fragmentation of the Unix effort was going to kill everybody's Unix and keep the market clear for Microsoft.

While insurance isn't showing the knuckleheadedness that I saw in the computer world in the '90s, there still is a lot of fragmentation in the efforts to develop blockchain technology. It's tempting to try to set the standard, because a company that sets the rules usually wins the game. There's a reason Bill Gates is still second on the Forbes list of richest people in the world even though he keeps giving his money away through his and his wife's foundation.

The industry would be much better off with a focused, joint effort to develop the core blockchain technology, at which point the competition could be how to build the best uses on top of that technology. This is what happened with Linux. When Linus Torvalds wrote the kernel and put it into the public domain in 1991, development became an open-source project for the entire coder community, not a series of one-off efforts by companies. Competition became about, for instance, developing the best tools for writing apps on top of that operating system, and there was plenty of profit there: Red Hat, for instance, recently agreed to be purchased by IBM for $34 billion. Not Bill Gates money, but I'd take it.

Look at how the telecommunications world collaborates on the standards for each new generation of Wi-Fi and how much business those new standards create. We'll all end up buying new phones once 5G rolls out; video and gaming companies will find new content and services to sell us; etc.

Blockchain will still have growing pains, but we as an industry can do better.

Cheers,

Paul Carroll
Editor-in-Chief

Has the catastrophe (CAT) bond market become passé? Lost its luster? If you were talking about insurance-linked securities (ILS) around the water cooler as recently as 2017, that’s the impression you may have walked away with. CAT bonds were so ‘90s; collateralized reinsurance was where ILS was. And since the credit crisis of 2008-09 the numbers have borne that out. But since the fourth quarter of 2018, CAT bonds have come back into the fore and are proving that they have some inherent advantages over collateralized reinsurance when included in ILS portfolios. I break the ILS sector into three segments: CAT bonds, collateralized reinsurance and reinsurance sidecars and similarly styled vehicles. Collateralized reinsurance includes both primary (to insurers) and retrocessional (to reinsurers) reinsurance contracts, as well as indexed contracts like industry loss warranties (ILWs), because ILWs are typically just an excess of loss reinsurance contract with an additional payment trigger. This article focuses on CAT bonds, and more specifically Rule 144A CAT bonds that typically trade on the secondary broker/dealer market. While CAT bonds are the oldest form of ILS currently being used, their growth following the credit crisis has been outpaced by that of the collateralized reinsurance market. According to Aon Securities, in 2007 CAT bonds constituted approximately $15 billion (68%) of the $22 billion in ILS market capacity, with collateralized reinsurance making up about $3 billion (14%) of that total. By July 2018, CAT bonds were approximately $30 billion (31%) of the $98 billion of total ILS market capacity, while collateralized reinsurance made up about $55 billion (56%) of that total. Why such a change? There are many reasons, but two main ones are: 1) the heightened awareness by cedants of their reinsurer credit risk post-crisis (especially since Hurricane Ike made landfall in Texas the same weekend that Lehman Brothers filed for bankruptcy!), and 2) a desire on the part of investors to access a wider range of independent insurance event risks across the yield spectrum than what was available in the CAT bond market. See also: The Challenges With Catastrophe Bonds   Fast forward to 2019. After two consecutive years of multiple catastrophe losses of moderate size (and in 2017 the most total insured catastrophe losses ever, surpassing 2005), a rarely observed phenomenon hit collateralized reinsurers: a liquidity crunch. While deal specifics vary, in its simplest form a collateralized reinsurer posts 100% of the policy limit (less premium in many cases) as collateral for a given transaction. If a loss occurs, it takes time for the reinsured to adjust the loss, and that amount of time may extend past the next renewal of the reinsurance contract. If the size of the loss is unknown at renewal, the collateralized reinsurer may have to post additional collateral to renew the contract. If it does not have sufficient cash or liquid securities, or cannot quickly raise additional capital, it will not be able to participate in the renewal. Multiply this situation across the many reinsurance contracts in a collateralized reinsurer’s portfolio, and the result can be reduced portfolio returns because the reinsurer has to maintain collateral balances that will only earn money market yields. The need to have sufficient liquid securities available to facilitate reinsurance contract collateral requirements after one or more insured catastrophes was missed by some collateralized reinsurers. Prior to the credit crisis, most investment managers in the ILS sector had significant traditional reinsurance experience and were familiar with the loss adjustment process of significant catastrophes. After the crisis, a number of ILS funds were formed by managers who did not possess this experience and appreciation for the nuances of catastrophe claims adjustment (particularly the time associated with the claims adjustment process). Following the recent back-to-back years of notable natural catastrophe losses, the discussion of “loss creep” began in the trade press, which is not really a new phenomenon and is to be expected within the first year or so of adjusting complex catastrophe claims. Given the need for liquid collateral after a catastrophe, what’s an ILS manager to do? Unless the manager is a multi-strategy or multi-asset fund, the investment mandate is typically limited to ILS and cash. Maintaining too large a cash position creates a drag on portfolio returns and makes the manager less competitive. That leaves the manager with one choice for liquid securities: CAT bonds. While CAT bonds are not highly liquid exchange-traded securities, there is an active over-the-counter broker/dealer secondary market for CAT bond trading, and they are often recognized as Level II assets under Fair Value Measurements standards. CAT bonds have traded continuously at non-distressed prices through major financial market dislocations, including the dot-com bust and the credit crisis. Prior to the credit crisis, however, there was limited visibility into CAT bond secondary market trading volume and pricing. Then, in 2012, the U.S. regulatory agency FINRA launched the Trade Reporting and Compliance Engine (TRACE), which tracked CAT bond (and other fixed income securities) secondary market trades by FINRA-registered broker-dealers and provided a window into this opaque world. CAT bonds proved themselves again as a liquid asset in 2018, particularly in the fourth quarter, despite the catastrophic activity occurring in real-time from events like Hurricanes Florence, Michael and the California wildfire outbreak. ILS managers who were savvy enough to include CAT bonds in their portfolios sold them as needed to raise additional capital for their collateralized reinsurance businesses. According to Swiss Re Capital Markets, TRACE secondary trading volume in 2018 totaled over $2.1 billion, with the second half of 2018 exceeding $1.1 billion and $700 million of that occurring during the fourth quarter. Second half 2018 trading volume exceeded that of the same period in 2017 by 35%. With $30 billion of CAT bonds in circulation at year-end, the 2018 secondary trading volume was approximately 7% of the outstanding market. See also: Dying… or in a Golden Age?   Leaving aside the fact that CAT bond portfolio returns often outperformed those of ILS portfolios weighted toward collateralized reinsurance and sidecars in 2017 and 2018, the data suggests that CAT bonds can also perform a valuable liquidity function in ILS portfolios of all types. CAT bonds clearly give managers of ILS funds a multi-dimensional portfolio management tool that benefits both portfolio return and liquidity. Whither the CAT bond market? I suggest that thou speakest too soon!

In the name of simplicity, I’ll be brief. 1. Decades ago in the Louisiana legislature, a contentious debate was raging over a law requiring motorcycle drivers to wear helmets. Representative V. J. Bella stepped to the podium in the Louisiana House with a stool, a sledgehammer and a watermelon. He placed the watermelon on the stool and slammed it with the sledgehammer. He walked to the microphone, stating, “Enough said!” The legislation was approved. 2. Tomorrow, I’m speaking to a business group. I have 90 seconds to send a message. My left hand will hold a dollar bill. My right hand will also hold a dollar — in the form of 100 pennies. My narrative will be brief. I’ll drop the dollar bill and let it float to the ground. I’ll roll it up and throw it as far as I can. I’ll reinforce the obvious with, “This is a dollar bill.” I’ll hold up the 100 pennies and toss them in the air. I’ll clarify the not so obvious with the following: “This is also a dollar. It has the same value as the dollar bill. The difference between the two is the bill was a cohesive unit when I tossed it — the dollar in pennies were individual units — not cohesive." See also: With Innovation, Keep It Simple, Stupid   Occasionally, an enthusiastic manager or leader decides it’s time for a bold step into the future. It’s time to shake things up. It’s time to turn the organization on its head. That may be the right thing to do, but it does not come without risk. To make such a bold move — YOU MUST BE CERTAIN YOUR TEAM IS A COHESIVE UNIT focused on shared values and goals and not just a bunch of individuals driven by their perception of what’s good for us – without knowing what “us” thinks and feels about someone else’s great idea! 3. Presenting to a client organization, I placed the six individual dolls that were part of a nesting doll on the podium. I held up the first doll and explained, this is you. I then positioned “you” into the second doll - stating, this is you in your family. I followed with – this is you and your family inside of your job/profession/business. We advanced to this is you, your family, your job or profession or business inside of your community. The fifth step was you, your family, your job and your community inside of the marketplace. I closed with this is you, your family, your job, your community and the marketplace inside of the global economy. I then held up the single assembled mass and said – this represents your COMFORT ZONE. You know where you fit and what surrounds you. Even if you are miserable – you are comfortable. That’s the good news. The bad news is – each and every entity (doll) stacked in here will change drastically in the next few years, and if you can’t or won’t be flexible and adapt you will be crushed by those changes. No one stood and applauded – but I could tell they heard the message and were squirming with my presentation and the threat it represented to their COMFORT ZONE. From "The Portable Do It!" by John-Roger and Peter McWilliams we learn: “The bad news about the comfort zone: The comfort zone is never static. It is either expanding or contracting. If you’re not consciously expanding the comfort zone, it contracts. In the heating and air conditioning trade, the point on the thermostat in which neither heating nor cooling must operate -- around 72 degrees – is called “The Comfort Zone.” It is also known as “The Dead Zone.” See also: A Contrarian Looks ‘Back to the Future’   In leadership – KEEP IT SIMPLE – speak in a common language and make certain that what is heard and what is said align. When possible, be brief – USE A PICTURE or a METAPHOR that all can understand. DON’T LIVE IN THE DEAD ZONE - DON’T BE STUPID! KEEP IT SIMPLE. KEEP IT COHERENT!

We like our insurance carriers to be risk-averse. So it should come as no surprise they are often last to innovate. Insurers need to feel very comfortable with their risk predictions before making a change. Well, machine learning is writing a new chapter in the old insurance book. There are three key reasons why this is happening now:

1. New insurtech players are grabbing market share and setting new standards. Traditional carriers have no choice but to follow suit.
2. Customers are expecting Netflix/Spotify-like personalization, and have no problem changing providers — this trend is expected to grow as we see more millennials maturing out of their parents’ policies.
3. Getting started with machine learning is becoming VERY easy because of open source frameworks, accelerated hardware, pre-trained models available via APIs, validated algorithms and an explosion of online training.

As with any innovation, it only takes two things for widespread adoption:

1. Potential to improve business goals.
2. Ease of establishing pilots.

With time, we see that successful pilots become products. Teams are hired/trained, resources are allocated, business goals gain more "appetite" and models are tweaked. For P&C carriers. we see the opportunity for improving business goals and easily pilot machine learning in the following areas: Risk Modeling Given the complex and behavioral nature of risk factors, machine learning is ideal for predicting risk. The challenge lies in regulatory oversight and the fact that most historic data is still unstructured. This is why we often see machine learning applied to new products such as those using data from IoT sensors in cars (telematics) and home (connected home). But innovative carriers are not limited. They use pre-trained machine learning models to structure their piles of unstructured data: APIs to transcribe coupled with natural language understanding (NLU) extract features from recorded call center calls, handwriting and NLP/NLU tools for written records, leading toward identifying new risk factors using unsupervised learning models. See also: 4 Ways Machine Learning Can Help   Underwriting Carriers can get an actuarial lift even without designing and filing new actuarial models. Using machine learning to better predict risk factors in existing (filed) models. For example, a carrier may have already filed a mileage-based rate-plan for auto insurance but rely on user-reported or less accurate estimates to determine mileage. Machine learning can help predict mileage driven, in a less biased and more accurate way. Similarly, APIs to pre-trained chatbots using lifelike speech and translators can turn website underwriting forms into more engaging and personalized chats that have a good chance to reduce soft fraud. Claims Handling Claims handling is a time-intensive task often involving manual labor by claims adjusters onsite. Innovative carriers already have policy holders take pictures and videos of their damaged assets (home, car…) and compare with baseline or similar assets. Carriers could easily leverage existing APIs for image processing, coupled with bot APIs to build a high-precision model, even at the expense of low recall. Compared with having 100% of the book handled manually, a triage bot that automates even a mere 20% of the claims (with high precision) can enable carriers to start with a low-risk service that’s on par with new insurtech players and improve ratios over time. Such a tool can even be leveraged by adjusters, reducing their time and cost. Coverages While personalized pricing may be regulator-challenged, personalizing the insurance product offering is expected in this Netflix/Spotify age. As basic coverage is commoditizing, carriers differentiate their products based on riders and value-added services, not to mention full product offerings based on life events. Carriers can (with consent, of course) leverage social media data to tailor and personalize the offering. Similarly, marketing departments can use readily available recommendation algorithms to match and promote content about the benefits of certain riders/value-adds to relevant customers at the relevant time. Distribution The world of insurance distribution is growing in complexity. Carriers are struggling with the growing power of intermediaries, and agents are having hard time optimizing their efforts due to lack of predictability of loss commissions. Point-of-sale and affiliation programs are growing, and with them the need for new distribution incentive models. Both traditional and new distribution channels could benefit from machine learning. Brokers, point-of-sale partners and carriers can leverage readily available machine learning models and algorithms designed for retail, to forecast channel premiums. Carriers can grow direct channels without growing headcount, using pre-trained chatbots, NLU and lifelike speech APIs. See also: Machine Learning: A New Force   Machine learning is part of our everyday lives. Innovative insurers are now jumping on the ML wagon with an ever-growing ease; which carriers will be left behind?

As the foothold of innovation across industries grows stronger by the day, insurers are witnessing the advent of tech-based economies, and with them a fundamental shift in the very definition of risk. Every advancement stands to revolutionize how property, businesses and employees will be insured. Consider automated cars and workplace automation tools, such as Amazon warehouse robots, or the emergence of shared ownership business models, like Lyft and AirBnB. Traditional risk calculation models need to evolve to keep up with rapid change. How shall insurers prepare for this shift? According to Valen Analytics’ 2019 Outlook Report, a key part of the answer lies in the need to weave data and predictive analytics into the fabric of their business strategies. The report, which employs third-party and proprietary data to identify key trends, revealed: Insurers Are Heavily Relying on Advanced Use of Data and Analytics to Fuel Growth Valen’s Underwriting Analytics study found that 77% of insurers are incorporating predictive analytics into their underwriting strategy. This marked an increase compared with the steady 60% of insurers during the past three years, demonstrating a clear emphasis by the industry on data-driven decisions. While many factors have fueled the demand for sophisticated data and analytics solutions, one stands out. Insurers have a growing desire to reap a share of the underserved small commercial market, which represents over $100 billion of direct written premiums. Data analytics tools enable insurers to reduce the number of application questions, verify necessary information and ascertain risk much more quickly and accurately. This is particularly important in creating effective business models that align with the needs of small business owners. The rise in insurers looking to employ advanced data analytics techniques has also resulted in the growth of data aggregation services and consortiums. With new primary customer data sources emerging, insurers have access to better insights on consumer risk and behavior. This has contributed to insurers’ appreciation of the predictive horsepower that large pools of data offer. In fact, Valen’s proprietary research found that the synthetic variables appended with consortium data are as much as 13 times more predictive than policy-only data. Synthetic variables are built from computations of more than one variable, made possible by leveraging large and diverse datasets. See also: Understanding New Generations of Data   Regulation and Innovation Must Go Hand-in-Hand With a rise in advanced predictive analytics and robotic process automation in insurance, regulators are paying close attention to the industry. To ensure this oversight doesn’t stifle innovation, it is important that insurers build and document their analytics initiatives so they can be explained and understood by regulators. Being collaborative and responsive will help ensure that regulators can discern the small percentage of use cases that need to be reviewed for consumer fairness protection. In doing so, insurers have the opportunity to take the industry to Insurance 2.0 -- the next phase in technology adoption and innovation. Talent and Infrastructure Challenges While insurers are looking to integrate data and predictive analytics into their business strategies, what will truly determine their success is their ability to hire and nurture the right talent. Unfortunately, the industry continues to suffer from a lack of the talent needed to support fast-paced innovation. Seventy-three percent of insurers surveyed indicate moderate to extreme difficulty in finding data and analytics talent, and the reasons haven’t changed over the years. While geographic location of the job is the primary reason cited by the survey respondents, more and more prospects are either looking for better compensation packages, are simply not interested in an insurance career or opt for opportunities in tech startups or data-driven companies in other fields. Another roadblock for insurers is their dated IT infrastructures, which cause massive backlogs. While most insurers suffer backlogs of two years or more, others cannot identify how long their IT backlogs are. See also: Insurance and Fourth Industrial Revolution   Both of these problems go hand in hand. Clearly, there is a need to foster an innovation mindset, and, to do so, the industry needs a mix of new thinking and engaging work culture. Insurers should follow the footsteps of leading tech companies and cultivate a culture that appeals to high-level talent. By making small changes, such as embracing diversity and a remote workforce, insurers can make themselves attractive to the talent they need. This will build a workforce capable of overcoming IT infrastructural issues. In short, to maintain a competitive advantage, insurers must not only put data and analytics at the forefront of their businesses, but also make strategic decisions on how best to employ them to enhance all aspects of their businesses, from customer service and information handling to risk calculations and claims processing.

It can be challenging for commercial insurers to gain a competitive edge in today’s insurance market while still maintaining profitability. However, small business continues to grow, with 30 million small businesses currently employing 48% of U.S. workers. This creates an opportunity for commercial insurers to increase the volume of small businesses in their book of business. So the question becomes, how can insurers best service their current small business customers to ensure strong retention while furthering the growth of that revenue stream? To gain a competitive advantage, it is crucial that insurers provide elite service, efficiency and innovation that meets the high expectations of their small business customers, especially given how technologically advanced today’s small businesses are. According to a new study by LexisNexis Risk Solutions, there are five key areas identified as opportunities to do so: Expand and implement more automation For small businesses, automation is an efficiency driver in all aspects of their daily operations, including those with their insurer. Likewise, embracing automation can increase the speed and efficiency of insurers’ workflows, reduce human error and help address changing business needs and demands. As is, the level of automation used in small commercial underwriting has not improved over the past two years. See also: Why Start-Ups Win on Small Business   The underutilization of automation remains a great pain point for small commercial insurers. 89% of those small commercial insurers who participated in the study reported the need to manually re-evaluate insurance applications. However, for insurers looking to set themselves apart, it is important to take advantage of these automation opportunities to reduce the risk of incomplete or error-ridden applications, alleviate labor-intensive busywork for underwriters and improve the overall customer experience. Without these efforts, carriers risk losing money and weakening their competitive position. Identify the best data assets and leverage them to their full potential Commercial insurers, like most small businesses today, are no strangers to using data for more informed business decisions. However, the majority of commercial insurers surveyed reported that they relied mostly on public records data, and data retrieved from internet search results. These insurers also cited consumer credit data and commercial credit data as providing the most valuable competitive advantage. Data assets are not being used consistently across the insurance workflow, but insurers that reset their workflow and spend their time analyzing the right data and utilizing credit and other data sources to their full potential will most likely see improved profitability in their small commercial book of business. Use predictive modeling consistently The majority of carriers (81%) believe predictive modeling is important for commercial underwriting, pricing and rating, and it has proven to help insurers evaluate loss propensity and make more informed decisions based on their risk appetite. Carriers that use predictive modeling also report at least moderate success. However, only one-third of respondents said they use predictive modeling consistently. Small predictive modeling can help insurers new to modeling gain a better understanding of how score-based decision-making can benefit their business, and how to build on that knowledge to adopt it as a consistent business practice. Put customer experience first The study found that the three most important factors to the customer experience were faster turnaround times, improved accuracy of customer data and playing a consultative role. However, these three areas were also reported as needing the most improvement. In the era of instant gratification, commercial carriers should focus on enhancing their online digital platforms by deploying new automation technologies – such as data prefill – to improve accuracy and make the turnaround time and overall process faster. As a result, agents will be able to spend more time being consultative with new and existing customers rather than having to spend it filling out basic information. Embrace market trends Seventy percent of the insurance professionals surveyed for the study believe that emerging market trends are important to their business strategy, but less than half are actively making strategic changes in response to them. To stay ahead of the competition, commercial insurers will need to prove that they’re cutting-edge by identifying new trends early and responding quickly. The current key market trends identified, in the study, as having the biggest opportunities for business strategy include telematics, Internet of Things and direct-to-consumer. On the flip side, data breaches, artificial intelligence (AI) and direct-to-consumer are seen as bringing the biggest threats to business. As these emerging market trends continue to become mainstream, embracing the changes will be the only way to keep from being left behind. See also: Taking Care of Small-Medium Business   Find the gap, make the opportunity While insurers are well aware of these trends and their importance to business performance, few are taking the appropriate actions needed to keep up. For every missed opportunity, the insurer risks falling further behind changing market demands and evolving customer expectations and is less likely to appeal to current and prospective small business customers. Commercial carriers who remain complacent will not only risk losing their current small business clients but could also miss out on the opportunity to optimize and grow their small commercial business. It’s still anyone’s game to become the go-to insurer for small businesses, so even those that take small steps to better target and service this market can yield big results. The small business community does business via relationships and recommendations, so those who provide best-in-class service to their current small business customers are sure to gain market validation and perhaps even recommendations that can help them organically grow their business.

Insurance is a business sector where predictive analytics software has some of the most straightforward applications, also with a high return on investment (ROI). Predictive analytics is already offering companies significant savings, and it is expected to grow exponentially in the next few years. Most likely, it will become the standard practice for insurance and risk management. The advantage is that the data lake used for predictive analytics can collect both internal and external data and correlate it to identify patterns and create almost real-time reports. In turn, this would prevent fraud and help to analyze behaviors. The results can be used in various areas of the business, which include risk assessment, pricing policies, claim processing, fraud management and trend analysis. Here are a few of the ways predictive analytics is reshaping the insurance sector: Pricing This is one of the first applications of predictive analytics in the insurance sector because it offers a high ROI. As sources become more diverse and precise, results will be more actionable. Although there are relevant security and privacy issues involved, insurance companies are collecting and analyzing data from sources that, for example, 10 years ago were not available or considered relevant, like social media. The good news is that now data is no longer an average of a cluster, but, after the general profile is created, the machine can measure how each person scores against the grid. Market Trends and Risk Assessment Identifying market trends is all about detecting the right patterns in data and anticipating their further development. Fortunately, AI is perfect for doing just that, regardless of the volume or complexity of the input data. Recently, both the U.S. government and the E.U. ruling organs have adopted an “open data” policy, making available lots of census data related to population statistics, education, safety and more. These new sets offer insurance companies new opportunities regarding macro risk assessment. See also: 3 Ways to Optimize Predictive Analytics   Correlating these sets of data within the right algorithm can help insurance companies to create clusters of customers grouped according to their profitability. For example, such analysis can provide the answers to questions like the probability of a person being involved in a car accident in a certain town, or the likelihood of default for a mortgage for a specific educational profile. The next step is to extrapolate the results and make predictions for the following periods to stay ahead of the market. Fraud Detection and Prevention Insurance is a very vulnerable sector for fraud. People are tempted to pay for an insurance policy and “make it look like an accident” to collect the value of the insurance. Although over the years insurance inspectors have become well aware of classic schemes, new tools are needed because the insured risks become more diverse and linked to digital activity. The Coalition of Insurance Fraud estimates that over $80 billion is lost due to fraud. The same studies show that one in 10 claims is fraudulent. Therefore, insurers are ready to go to any lengths necessary to prevent such actions. The advantage of predictive analytics is that it can signal potential fraud before it happens. The machine would identify specific patterns associated with fraud, usually by means of dots that don’t connect. Tailor-Made Services Most companies, from utilities to retail and especially e-commerce, strive to offer customers a very personalized experience. The insurance sector needs to be at the forefront of this practice, too, as products have few real differentiators apart from the price. In this business, predictive analytics can look at customers’ profiles and predict needs, create bundles of services and help these customers meet their personal goals. Depending on a customer's profile, such purposes can include increased safety, budget management, saved time or significant risk hedging. These systems also offer the opportunity to prioritize claims and serve customers not only in their arriving order but also by evaluating their lifetime value, to avoid losing important ones who need their cases sorted faster. Customer Retention Learning from the world of retail and even HR, the insurance business can benefit significantly from identifying those customers who are about to cancel their policy. Usually, by giving these some extra attention, they can be kept onboard for another year or more. In this case, data insights and customer behavior analysis can help insurance companies identify those who are already looking for solutions from competitors. Focus on the Extraordinary Not all odd claims are frauds, but unexpected and expensive claims can hurt an insurance company’s profit margins. In this case, the role of predictive analysis is to identify potential risks and warn the customer to take all necessary preventive measures. Although such outliers are harder to detect due to the lack of previous relevant data for training, the advantage of using machine learning is that it can put together several distinct pieces of information to identify potential risk. See also: 3 Key Steps for Predictive Analytics   Privacy Concerns As in all matters related to the use of personal data, some people could have three categories of concerns, as stated by the report of the Geneva Association. To wrap up this discussion of data-centric insurance, let’s look at them:

• Privacy and data protection concerns. These are mostly related to the fear of discrimination based on profiling. The other problem in this category is intrusiveness in the right of self-determination, especially when customers can’t afford the prime for their risk class, thus restricting their lifestyle options.
• The individualism of insurance problems. The problem of exclusion should be at the forefront of insurance companies’ internal regulations. Excluding certain high-risk categories can lead to social pressure and the need to find alternative solutions such as state funding.
• Implications of big data and AI for competition. The fourth technological revolution is already causing disruption and changing markets. By implementing these tools, we can expect that some jobs will disappear or reorganize. This will also happen to companies that will not adopt the new standards.