Key Highlights External influencers: mixed macroeconomic signals

• Uptick in global growth and rebound in employment levels, if sustained, will have favorable implications for the sector.
• As central banks turn cautious, bond yield improvements are likely to slow in the near term, implying limited investment yield upside for insurers.

Sector trends: hurricanes to set course

• Supported by a strong bull run, global insurance stocks continued to rise as several large insurers saw improved investment and underwriting results.
• Pick-up in long-term buy recommendations for U.K. and E.U. insurers reflect improved analyst expectations.
• Natural catastrophe (NatCat) losses: Active hurricane season is expected to halt the relatively benign period of losses and limit further pricing weakness that has persisted after 2012.

See also: Insurance Technology Trends in ’17, Beyond   Tech disruption: blockchain rising

• Addressing the evolving nature of risk through innovation is a key imperative for insurers.
• Blockchain has now progressed beyond pilot stage, with early adopters looking to gain significant advantages.
• EY has taken a strong lead in helping insurers create a blockchain-based new-age information infrastructure.

Regulatory landscape: insurers prepare for impact

• Insurers need to initiate implementation plans to effectively address the changes introduced by the new accounting regulations (including IFRS17 Insurance Contracts).
• General Data Protection Regulation (May 2018): With more than half of the two-year post-adoption grace period now over, insurers will have to act fast to address the impending challenges.

You can find the full EY report here.

Insurtech and artificial intelligence (AI) have become the new buzz words and mantra in the insurance industry. Creativity and innovation are thriving in Silicon Valley with more than 1,600 technology companies in the insurtech space for underwriting and claims. If you remember, back in the 1990s, experts predicted that if your company was not an internet company, you would not be around for long. That prediction came true, but what about the current prediction that artificial intelligence for claims will change the insurance industry? I believe that it will, and now is the time to stake your claim for the future. For the last few decades, insurance companies have yielded massive amounts of data. We know that a robust AI system for insurance claims needs a lot of data to thrive and perform. Because you have the data, how can you start to leverage it and improve your business performance and outcomes? Insurance is now ready for an analytics platform such as Infinilytics' smartCTM that can help minimize claim costs, and improve customers’ claims experience. See also: Strategist’s Guide to Artificial Intelligence   One of the most contemporary AI solutions for claims is fraud and litigation prediction. Imagine the capability of predicting with a 90% accuracy rate if a claim is going to fall into litigation. Your claims team would be able to take immediate steps to mitigate the litigation, customer service would be greatly enhanced and your claims costs would be reduced. Insurance companies need to embed A.I. solutions along with their human intelligence so there's an effective feedback mechanism. Such AI-based claims SaaS solutions aim to:

• Lower loss-adjustment expenses
• Reduce the time to settle a claim
• Identify suspected fraudulent claims with AI pattern matching
• Use sentiment and emotion analysis for litigation predictions

AI solutions using machine learning require careful deployment and breathing time to achieve the return on investment. Machine learning can cause data biases and hide the context of predictions, which makes it unusable by claims organization. Claims organization need to become super users of such AI solutions and understand the context of these predictions and continue to contribute through continuous feedback. Most successful companies have combined human intelligence with AI to change labor-intensive, pattern-driven processes such as claims. See also: Insurtech: Can It Help Claims Experience?   Insurance companies have a vast amount of data. We need to leverage the data and transform the claims process into an efficient and cost-effective business model by quickly bringing artificial intelligence into the claims process.

Each year, Congress adopts a concurrent budget resolution setting net spending, revenue and debt limits on legislation emerging from each committee during the current session. This year, the path to tax reform began on Oct. 5 when the House passed its budget resolution, followed by the Senate passing its fiscal 2018 budget on Oct. 19. On Oct. 26, the House adopted the Senate’s version of the plan, allowing tax reform to increase our deficit by a maximum of $1.5 trillion over a 10-year budget period. The House passed H.R. 1, the “Tax Cuts and Jobs Act” on Nov. 16, and the Senate Finance Committee (SFC) approved its version on the same day. The Joint Committee on Taxation (JCT) estimated the bill would cost approximately $1.414 trillion over the 10-year period. The bill contains reconciliation instructions that allow it to pass the Senate with a simple majority. However, any senator may challenge proposed amendments if the instructions are not met. For example, an amendment made in reconciliation must be limited to provisions having more than an incidental effect on revenue spending and may not increase projected deficits in fiscal years beyond those covered in the measure. This is the feature that is behind the eight-year sunset in the SFC’s version. We suspect the reconciliation instructions will shape current tax proposals. Below are snapshots of specific SFC provisions affecting charitable giving, House counterparts and estimated costs. Keep in mind that a large portion of SFC’s provisions will not apply after 2025, when these changes will revert to pre-2018 law. We will conclude with the possible next steps in the legislative playbook, and some charitable planning observations. Executive Summary: The Impact of Tax Reform on Charitable Giving The most important item in each bill is the absence of language eliminating the charitable income tax deduction, limiting the deduction to a percentage (i.e., 28% effective rate as in President Obama’s proposals) or limiting the deduction to tax basis (rather than fair market value). These are huge considerations in reviewing this proposed legislation and should not be overlooked. See also: Tax Reform: Effects on Insurance Industry?   Unfortunately, the overall tax restructuring of the House and Senate bills produces an unfavorable result for the charitable world, whereby charitable giving may be significantly reduced, as set forth below. Tax Reform Snapshot Many exemptions, deductions and credits have been eliminated, curtailed or adjusted. For example:

•  The personal exemption is repealed.
•  The mortgage interest deduction is significantly curtailed and is now limited to the principal residence alone. The mortgage deduction on new acquisition indebtedness is limited to $500,000 (effective Nov. 2, 2017).
•  State and local income tax (or sales tax) deductions are eliminated, except for a deduction up to $10,000 for real property taxes.
•  The AGI limitation for charitable contributions of cash made by individuals to public charities is increased to 60% (from the current 50%).
•  The 3% limitation on itemized deductions (including the charitable deduction) – known as the “Pease limitations” – are repealed.
•  Personal casualty losses are eliminated.
•  Wagering losses are limited.
•  Tax-preparation deductions are eliminated.
•  Medical-expense deductions are eliminated.
•  Alimony deductions are eliminated.
•  Moving-expense deductions are eliminated.
•  Medical Savings Account deductions and exclusions are eliminated.
•  Employee-trade or business-expense deductions are eliminated.
•  The alternate minimum tax is repealed.

The SFC version of the bill has similar limitations, reductions or elimination of deductions, all of which must be resolved in one bill, probably during a conference committee. For example, the SFC retains the current seven tax brackets, lowering several rates, while the House’s version reduces the tax brackets to four. Both versions double the standard deduction while eliminating personal exemption deductions and most itemized deductions. The JCT estimates repealing the personal exemption deductions will increase the budget revenue by approximately $1.2 trillion, and repealing itemized deductions will add another approximately $978 billion over 10 years. In early November 2017, the Joint Committee on Taxation released a memorandum, which reflected the impact of these changes on the charitable income tax deduction: “For tax year 2018 [under current law], we estimate that 40.7 million taxpayers who itemize will deduct charitable contributions totaling $241.1 billion. Under H.R. 1, we estimate that approximately 9.4 million taxpayers who itemize will deduct charitable contributions totaling $146.3 billion in 2018.” The memo confirms a study released earlier this year by Indiana University’s Lilly School of Philanthropy: A doubling of the standard deduction alone will reduce charitable giving by $11 billion, and the increase of the standard deduction, along with other provisions in the House bill, will cause the claimed charitable deductions to fall by $95 billion, or 40%. This does not mean charitable giving will fall by $95 billion, but the amount claimed as a charitable deduction will fall by $95 billion. Both versions of tax reform permanently reduce the corporate tax rate to 20% (from the current 35%). However, the House version is effective in 2018, while the SFC version is effective in 2019. Both versions alter pass-through business income (PBI): The SFC bill allows a 17.4% rate deduction, while the House version caps the PBI tax rate at a flat 25% rate with the rebuttable presumption that 70% is wage income (taxed at regular rates) and 30% is PBI; service providers are taxed on 100% of PBI. Both versions modify rules for expensing capital investments. The SFC bill increases Section 179 expensing to $1 million (from the current $500,000), with a phaseout range beginning at $2.5 million (from the current $2 million), and expands the definition of qualified property. The House bill increases Section 179 expensing to $5 million, with a phaseout range beginning at $20 million. Both versions of the bill double the estate, GST and gift tax exclusion amounts to $10 million, adjusted for inflation. The JCT estimates this provision would decrease revenue by approximately $83 billion over 10 years. The House bill completely repeals the estate tax after 2024. The Tax Policy Center estimates completely repealing the estate tax alone would reduce giving by $4 billion in the long run, while altering behavior of lifetime giving by reducing charitable bequests by approximately 27%. SFC’s Permanent Provisions for Exempt Organizations Exempt organizations will be affected by the reduced 20% corporate income rates, changes to the various fringe benefits and tax-exempt bond reform. SFC’s bill proposes additional permanent changes to exempt organizations which include, in part:

•  A 20% excise tax will be imposed on employees who receive more than $1 million in compensation or “excess parachute payments” (producing $3.6 billion of additional revenue over 10 years; a similar provision is in the House version).
• Charitable deduction will no longer be allowed for amounts paid in exchange for college athletic event seating rights (producing $1.9 billion of additional revenue over 10 years; there is a similar provision in the House version).
• Donee organizations will no longer be allowed to provide a contemporaneous written acknowledgement (CWA) by issuing statements for all donors on a report to the government; instead, a donor making a gift exceeding $250 will need to rely on a separate or individual CWA (this has negligible revenue effects; there is a similar provision in the House version).
• A 1.4% excise tax will be imposed on net investment income earned by private colleges and universities with at least 500 students and non-exempt use assets with last year’s value of $250,000 per full-time student (producing $2.5 billion of additional revenue over 10 years; a similar provision is in the House version).
• Name and logo royalties will be treated as unrelated business taxable income (producing $2 billion of additional revenue over 10 years; there is no House corollary provision).
• Losses from one unrelated trade will not be allowed to offset income from another unrelated trade, thus unrelated business taxable income must be separately allotted for each trade or business activity (producing $3.2 billion of additional revenue over 10 years; there is no House corollary provision).

House Provisions for Charities (not part of SFC bill)

•  A flat excise tax rate of 1.4% will be imposed on investment income of private foundations (rather than the 2% or possible 1% tax rates).
• Charitable organizations sponsoring donor-advised funds (DAFs) must disclose their policies with respect to inactive DAFs and average amounts of grants.
• All charities (that is, Section 501(c)(3) organizations) will be allowed to engage in “de minimis” political activity (this is a change in the so-called “Johnson Amendment”).

Next Steps in the Legislative Playbook The Senate is expected to debate the SFC bill, potentially stripping, replacing or incorporating various House provisions during the week of Nov. 27. Following approval by the Senate, both chambers must resolve differences between the two versions of the bills before submitting one final bill to the White House, to be signed by President Trump. The proposed deadline is Dec. 12. Given the uncertainty on the horizon, donors who wish to make their charitable contributions must donate this calendar year (2017) to take advantage of current charitable giving tax benefits. Those unsure about how to allocate gifts in 2017 may nonetheless secure current benefits by using charitable giving vehicles. Donors should print out this article and consult with their advisers to explore their options. See also: Do We Face a Jobless Future?   Charitable Planning Observations

•  Many exclusions from income, exemptions, deductions and credits have been eliminated under the bill. For a vast majority of all Americans, the bill simplifies the income tax laws.
• Given the elimination or reduction of many deductions, exemptions and credits, a person with a home and a $400,000 – $500,000 mortgage may well have deductions that exceed the new standard deduction of $24,000. If this is the case, the only remaining income tax planning option is charitable giving. Charities should be alert to this opportunity and should promote outright and planned gifts during life with itemizers.
• With the anticipated reduction in charitable income tax deductions due to the increased standard deduction, charities should be aware of the danger of reduced funding and should enhance communicating their missions to donors who take the standard deduction.
• Under current law, taxpayers have tried to avoid treating business activities as “passive,” preferring them to be “active,” so that losses from one business activity can be offset against income earned as compensation. This trend may see a 180-degree turn.
• Almost all serious proposals in the past decade for repeal of estate and generation-skipping taxes have included a corollary: the imposition of carry-over basis at death, rather than a step-up in basis to fair market value at death. The Joint Committee on Taxation has scored the repeal of death taxes in conjunction with a carry-over basis regime as a revenue enhancer. This “omission” may be remedied in the final version of the bill, and, if this occurs, split-interest gifts will receive a significant boost.
• The world of mergers and acquisitions (M&A) will be changed radically. Sellers will want to sell assets because the corporate tax rate will be significantly reduced (from 35% down to 20%). Buyers will want to buy assets because the expensing of acquisition costs will be significantly increased. Charitable planners aware of this potential trend will be able to offer donors/clients the opportunity to avoid two levels of taxation, by suggesting charitable split-interest vehicles such as a gift annuity, pooled income fund and charitable remainder trust. In this manner, those selling a business may find they are only paying a 20% tax and are able to benefit the charity of their choice!

Having completed the exploration of our global trends in our previous post on services, investments and job roles, we now turn to our key themes. In our next round of posts, we will explore 11 key functional and technological areas within insurance, starting with today's installment on analytics and AI. The following statistics on big data, analytics and AI are drawn from the extensive survey we conducted as part of our Global Trend Map; a full breakdown of our respondents, and details of our methodology, are available as part of the full Trend Map, which you can download for free at any time. Insurance, relying as it does on predictions about complex future events, has always been a data-hungry, data-driven industry. The big-data explosion of the past decade is therefore something that insurers have followed with keen eyes:

According to IBM, the world generates 2.5 quintillion bytes of data every day, with 90% of the world’s data having been created in the last two years.

While insurers, and most companies for that matter, have for a fair while had more data than they’ve known what to do with, analytical and machine-learning models are now sufficiently mature and sophisticated for them to start reaping the much-heralded rewards of the big-data revolution. This is not without its challenges, though, with silos and legacy systems in particular acting as a drag on innovation. Analytics is being deployed pretty much everywhere, and by everyone, in the insurance ecosystem, so this post covers:

• Analytics and data usage across various insurance ecosystem players
• Overall data strategy
• Issues with silos and legacy systems
• Contrasting flavors of analytics in use: descriptive, diagnostic, prescriptive, predictive, behavioral and, of course, AI!

"Causing the greatest stir out of all today’s analytics tools is AI, which stands to revolutionize the whole insurance industry over the next 2-5 years, from robo-advisers and chatbots through to claims automation and mitigating fraud. While analytics teams retain the greatest degree of oversight, AI capabilities are currently being embedded across the whole insurance organization." – Helen Raff, head of content at Insurance Nexus

As we shall see, the leadership on many of these measures is provided by reinsurers. This is evidence of their driving the whole ecosystem forward, and in this they often take the lead over insurers. We also see this more generally; for example, the giants Swiss Re and Munich Re have been particularly active in accelerator-based innovation over the past two years. See also: 10 Trends on Big Data, Advanced Analytics   Is your investment in/focus on analytics increasing? 84% of all respondents are increasing their investment in analytics. This conforms to the stats we presented in our earlier post on services, investments and job roles, where analytics was second only to digital innovation for increased carrier investment. Drilling down further into the responses of different company types, we see that similar proportions of insurers (82%), brokers and agents (76%) and technology partners (85%) are increasing their investment. Of interest is the clean sweep by reinsurers, exemplifying the leadership trend we pointed out. Analytics has applications across all the major lines. Health and auto are two obvious examples given the ready availability of connected health devices and in-car sensors, which make data easier to capture and, as an extension of this, models easier to feed. This facilitates usage-based insurance (UBI), which we explore in more detail in our forthcoming post on Internet of Things, whereby actual living/driving habits inform policy prices (read ahead straight away by downloading the full Trend Map for free). Analytics also has obvious applications for predictive maintenance and security in commercial, auto and P&C/general lines, particularly where valuable assets (like property) are in play. Analytics is also growing in home insurance thanks to the increasing prevalence of connected-home devices, with Berg Insight estimating that there were approximately 18 million smart homes in Europe and North America by the end of 2015.

"There will be much more data from structured and unstructured data sources in the future – a huge challenge! 'Past developments are a good representation of future uncertainty' will not be replaced but solutions with AI-tech (big data) in combination with smart data strategies will enable insurances to make decisions based on models and evidence." –Andreas Staub, managing partner at FehrAdvice

Is your analytics strategy coordinated across your organization? The uses and advantages of analytics have been obvious for a long time, and we have seen analytics initiatives sprouting up in nearly every corner of the insurance business, from underwriting through to counter-fraud. An ad-hoc approach, often inevitable in the early days of a technology, quickly becomes unwieldy, and the benefits from coordination are substantial. It is encouraging therefore to see 57% of all respondents indicating that their analytics strategy is coordinated across their organization. The trend across our different company types is similar to the one we saw in the investment/focus question above – unsurprisingly, as coordination is vital to gain maximum value from increasing investment and focus, and often represents a large investment in and of itself. We thus see 59% of insurers coordinating, 54% of brokers and agents and 55% of technology partners, with reinsurers once more taking the lead (77%). Are you utilizing external data sources? Plenty of data is available for analytics use beyond that directly captured by insurance companies themselves, both publicly available (like social media) and for-purchase (from third-party aggregators). There is no clear trend across our ecosystem players on this measure, with 77% of insurers, 67% of brokers and agents and 81% of technology partners affirming their use of external data sources (reinsurers had a small though insignificant lead). Segmenting by region, we can tentatively identify Asia-Pacific as trailing on this measure, and our broader research and industry engagement does indeed suggest that the third-party data culture is less-well-developed here than it is in North America and Europe. That said, public sources of data remain available, from unstructured social media through to data generated/collected by incipient smart-city infrastructure (like in Singapore). More details to follow in our forthcoming regional profile on Asia-Pacific, or read on straight away by downloading the full Trend Map here. Do you have a formal data-governance strategy? Insurance companies are being borne along on an exponentially growing tide of customer data, which has brought data governance to the forefront of people’s minds; yet, as of today, only 57% of insurers, 51% of brokers and agents and 58% of technology partners possess a formal data-governance strategy. We expect this figure to rise sharply in the years to come. Reinsurers once again appear to lead (with 77% affirming the existence of a data-governance strategy). Are legacy systems and silos a problem for your business? Capturing data is only the first part of the story to building out an analytics-based business. In many cases, analytics and big-data projects within insurance companies come unstuck not because of a lack of investment or strategic focus but for more prosaic reasons: silos and legacy systems. If infrastructural bottlenecks strangle rather than feed analytical models, preventing them from operating at scale across all the relevant data pools, then the output will be etiolated and limited in use. We asked respondents whether legacy systems and silos represented "somewhat" or "very much" of a problem for their businesses, and then created a "burden score" based on a weighted combination of these two figures. Insurers clocked up a burden score of 138, brokers and agents 103 and technology partners 105. (Reinsurers score 123.) There are two key takeaways from this. Firstly, that silos and legacy systems are a problem for the entire insurance ecosystem. And secondly, that carriers are generally harder-hit (comparing insurers and reinsurers to the rest of the industry), which may well reflect their position as the central node of the industry into which all the other players feed. From descriptive analytics to AI: What's your flavor? With all new technologies or methods, there is generally a gap or lag between what is theoretically possible and what finds its way into commercial practice. We asked insurers and reinsurers what forms of analytics they were deploying out of a possible six options: As we can see, every form of analytics has attained at least a modest level of penetration, and we can tentatively construe from this an adoption curve of different analytics formats running roughly from predictive, descriptive and diagnostic (high degree of current adoption) through to behavioral, prescriptive and machine learning/artificial intelligence. So, while most respondents have developed capabilities to describe and predict, only a minority have advanced beyond this toward prescriptive and AI capabilities.

"The rise of insurtech, the analytics explosion and the new face of insurance has created a birth of new roles and impact points across the industry. No longer is analytics and data relegated to just information technology and actuarial — we are now seeing it being integrated into the business culture and DNA of insurance organizations." – Margaret Milkint, managing partner at the Jacobson Group

Analytics is a very broad category with applications across almost every part of insurance, from underwriting and marketing through to fraud and claims, as well as on the investment side of the business. For the sake of clarity, we have chosen to focus in on two areas of insurance work, underwriting and claims, to capture a snapshot of analytics maturity at the start and at the end of the policy lifecycle. The donuts above indicate the share of analytics work (as a proportion of the whole) being undertaken by respondents working in the areas of underwriting and claims – this is an intuitive way to compare the prominence of different flavors both within, and between, these two areas. A larger proportion of the analytics work undertaken by underwriting respondents appears to fall at the early stage of the adoption curve (descriptive and predictive) and a smaller proportion at the later stage (moving toward machine learning and AI), when compared to respondents working in claims. This implies that claims either encourages more advanced analytics than underwriting – which may be oversimplifying things – or that, for whatever reason, it leads underwriting on analytics maturity. See also: Why to Refocus on Data and Analytics   Join us for our next post, on digital innovation, where we talk about the rise of mobile and the many flavors of digital strategy. Or, if you'd like to access all 11 key themes straight away, simply download the full Trend Map free of charge. For any inquiries relating to the Insurance Nexus Global Trend Map, this content series or next year's edition, please contact: Alexander Cherry, head of research and content at Insurance Nexus (alexander.cherry@insurancenexus.com)

Drones are flying higher than ever. A recent Goldman Sachs forecast values the overall market opportunity for drones at $100 billion between now and 2020. Gartner predicts that global sales of drones for personal and commercial use will hit $6 billion this year and grow to $11.2 billion by 2020. But, as drone sales and applications grow, drone accidents are keeping pace. It’s a big opportunity for insurers. By the end of 2020, the drone insurance market could be worth more than $500 million in the U.S. and $1 billion globally, according to Allianz. See also: 5 Ways Drones Are Changing Insurance   Drones have already been involved in many incidents of property damage and other mishaps. Notable examples include collisions with a nuclear power station in Cape Town, the Empire State Building in New York, the grounds of the White House in Washington, DC, a Norwegian ski hill in the middle of a World Cup slalom race and the fingers of the singer Enrique Iglesias during a concert in Mexico. The most recent statistics from the Federal Aviation Authority (FAA) reveal that drone safety violations in the U.S. are up 46% year-over-year. It is therefore no surprise that insurers have started to offer coverage for private and commercial drone users. Some notable examples:

• In July, a subsidiary of the German insurer Allianz announced a partnership with Flock, an insurtech startup that analyzes real-time flight-risk information to provide pay-as-you-fly insurance. Allianz and Flock plan to launch the “Flock Cover” app in the U.K. sometime in 2017, which will offer recreational and commercial coverage for up to £10 million (US$13.6 million). The app is still in development as of this writing, but enthusiasts can sign up to beta test it on Flock’s website.
• Munich Re began offering commercial drone insurance in the U.S. in June in the wake of new regulations from the FAA. The product targets small and medium-sized businesses, including farms, that use drones. It covers injury and property damage coverage for drones that weigh as much as 55 pounds (25 kg).
• Founded in 2015, the insurtech startup Verifly offers on-demand drone insurance for recreational and commercial flights in the U.S. Earlier this year, Verifly partnered with Loveland Innovations to offer insurance for insurance adjustors and contractors using drones. The insurance is provided through Loveland’s IMGING drone property inspection platform.
• In May, a subsidiary of Liberty Mutual Insurance Group announced DroneInsurance.com in conjunction with the insurtech Acend. The site aims to offer a convenient place for American drone users to buy flexible insurance from brokers. As of this writing, DroneInsurance.com was still in development, though interested parties could sign up to be notified when it launches.
• Ageas, a multinational insurer based in Brussels, includes drone coverage in the U.K. under its “Back Me Up” flexible insurance platform. Customers are covered for accidental damage, theft and loss of their drone, anywhere in the world. Coverage starts at £8 (US$11) a month.

See also: Drones + Gig Economy = Win for Insurance As drones become more popular and profitable, insurers will create offerings similar to these examples and innovative new ways of mitigating risk. Some of these new ideas will tap into the broader potential of the Internet of Things, which I recently wrote about in another blog series, and which is becoming increasingly visible in the industrial sector.

Do you know anyone in the professional world who does not talk about risk? I don’t. As a matter of fact, human beings rose to the top of the food chain because of the ability to perceive and manage risk. Other animals also manage risks, but in a more instinctive way generally based on "fight or flight." So, why do we seem to make a big deal out of risk management if everyone does it and we humans have been at it for millions of years? Last I checked on Amazon, there are more than 22,000 books on risk management. But who's counting? It seems new jargon is added to the cauldron of concepts and ideas and frameworks of risk management on a daily basis. By the time companies get halfway through the implementation of a framework, it has already become obsolete. There are so many competing solutions being offered in the risk management marketplace – every consulting company has its own branded product. So many solutions looking for a problem. But what is the problem? See also: 4 Steps to Integrate Risk Management   At a very fundamental level, risk management is about identifying, quantifying and managing risks. And managing risks has four components – avoid, mitigate, transfer or accept risk or some permutation and combination thereof. As Sherlock Holmes would say – "It is elementary, my dear Watson." In reality, though, it is anything but. The challenge is not the what and why of risk management but the how. Let me clarify. Businesses exist and prosper because they create value. That means a decision involving land, labor and capital needs to be made in the present expecting an outcome in the future. Given that no one has perfect insight into the future, there is uncertainty about the outcome and hence there is risk involved in every business decision. So, it makes perfect sense to be able to manage that risk within an acceptable limit. The question is how. There is a lot of fumbling around there. First, before we can manage something, we need to understand it. Risk is manifested in many different ways and often not in a homogeneous manner. For example, a risk to the balance sheet is not easily comparable with risk to earnings or to cash flow. Risks in the short term are not easily comparable with risks in the long term. Risk from the individual leader’s perspective is not easily comparable with the risks from the organizational perspective. Risk professionals often say risk and opportunities are two sides of the same coin. I say not quite. For example, what may look like an opportunity from an earnings perspective may involve a huge risk on the balance sheet. So, how does one compare and manage even when one identifies the risk properly? Second, we need to think of managing risks while the decision is being made, not after or even before. Risk is not the same as uncertainty. I find it fascinating that some GRC (governance, risk and compliance) frameworks talk about creating and maintaining a risk universe as if risks exist independent of organizational decisions. I am not sure if this is because the creators of these frameworks do not know any better or because they are just out to make a fast buck off the naïveté of their clients. See also: Why Risk Management Certifications Matter  Third, I find risk professionals (at least some of the savvier ones) put a lot of emphasis on modeling and quantification of risks. Don’t get me wrong. I absolutely think measuring risks is necessary. But it is a necessary evil, as it is fraught with assumptions, and often the decision maker is oblivious to them. Quantification is only a means toward the end, not the end by itself. The goal needs to be to understand the level of risks so that appropriate resource allocation can be made. Remember, we do not have unlimited resources, and, therefore, we need to prioritize risks. At the heart of it, the purpose should be to understand the level of risk the business needs to accept. No amount of Monte Carlo simulation can help if this goal is not met. I rest my case here. Looking forward to receiving comments and feedback.

Is this claimant supposed to be off work? Did I get enough discount on the services? Were those services even necessary? I would argue that the question everyone should be asking instead is: Who is your doctor? After all, the physician is the person who sets all of the other wheels in motion — wheels that influence things from quality of care to how long an employee is out of work and the ultimate cost of an injury. Throughout the past 15 years that I’ve spent managing networks and working with top companies developing custom solutions, one thing has consistently held true: Physician quality matters … A LOT. In fact, provider quality is shown to make the single greatest impact on a claim. It’s something that shouldn’t be overlooked, and yet all too often it is. Numerous studies have shown that good doctors make a difference. There is a huge discrepancy in claims associated with doctors who score well on an outcomes basis versus those who don’t. The average costs associated with a problematic D- or E-rated physician, compared with a rock star A- or B-rated doctor, are astounding when you really dig into the data. It is even more profound when you factor in case mixing and adjust results based on severity or type of claim. See also: The State of Workers’ Compensation   A wealth of information now exists on physician quality, and many different models, from simple to complex, can provide useful insights into which doctors can be associated with better outcomes. Carriers and employers should apply this data to think more aggressively about their networks. The PPO Dilemma Before we get there, however, let’s look at what’s going on with preferred provider organizations (PPOs). PPOs are the most common strategy used to control costs in our market. A PPO’s value comes from providing a negotiated discount on a medical encounter. Once you have entered into business with the PPO, its primary revenue source comes from matching your bills to a pre-negotiated discount — and the PPO gets more matches by contracting with more doctors. Therefore, if the PPO only contracts with the doctors who show the best outcomes, the PPO loses significant amounts of revenue any time bills come in from uncontracted doctors who don’t perform as well in outcomes. As such, all discount networks must contract with as many doctors as possible to ensure they don’t lose revenue by missing a hit on a bill. A perfect PPO would include 100% of doctors who pass the base qualifications of credentialing. But, as shown in the prior illustration, there is a huge difference in outcomes between the top half and bottom half of the PPO’s doctors. I don’t fault PPOs for this — PPOs do offer a clear value in reducing purchase costs per episode. They also must contend with multiple factors from jurisdiction to jurisdiction that will always limit how choosy they can be. There is a role to be played by discount networks, but that role is not the full picture of how to bend the curve on claim costs. “Savings” Don’t Always Reduce Costs Here is a simple concept: If a cheap pair of shoes costs 30% less than a high-quality pair, I might save money on the initial purchase, but, if the cheaper pair of shoes needs to be replaced twice as often, my savings on each transaction doesn’t lower my total shoe cost. Applying this logic to medical care, let’s look at two patients — one going to a discounted doctor and one going to a full-fee-schedule doctor. Let’s assume a typical ratio of 7:5 visits between high-scoring and mid-tier doctors (the real difference is typically higher). Patient A goes to an in-network doctor selected at random from an approved list of providers that generates a 5% savings off each bill. Patient B is sent to a doctor with a high outcome score but no discount. The average bill from each doctor is $100. After the first visit, Patient A has cost $95, and Patient B has cost $100. The payer for Patient A saved $5, and the payer for Patient B has saved $0. By the time Patient A has been to his/her seventh and final visit, medical care cost $665, with PPO savings of $35. Patient B’s care wrapped up after five visits, costing $500, with $0 in PPO savings. This showcases the problem of using percent of savings as a metric – longer duration and $165 more in total medical costs reflects $35 in savings over Patient B. The metric is flawed because the more you spend the more you save. The point to consider is that network savings are the shiny object that distracts from the difference in total costs. I am not arguing against leveraging savings where available; rather, I want to underscore that quality at a higher price point can significantly outpace discounts when you look at the total cost of a patient in any market. All health markets suffer from the cost of care that requires too many visits or the additional costs of a second necessary procedure to repair a bad surgery. In workers’ comp, this is exponentially compounded when you factor in the costs of temporary disability as a result of poor recovery and permanent disability stemming out of failed procedures. The Path Forward The best way to start down the path forward is to separate the decision about which doctors to work with from how to work with them. The who should be determined by some level of quality metric while the how is figuring out which PPO or contractual relationships get you the best access to doctors who will get you the best results. This means you should first find the doctors who perform well on your chosen metrics, and then look at the PPO or combination of PPOs that get you contractual access. It works in the opposite flow as well; you can look at the total population of doctors available through your network vendors, then pick who you want to work with from that list. See also: Even More Tips For Building A Workers Compensation Medical Provider "A" Team   In part two of this series, I will go into the concept of right-sizing networks and the relationship between PPOs and exclusive provider organizations (EPOs). Pick the doctor, and then figure out which network or combination of networks provides access. It may require a little more work and data science on the front end, but the outcome is well worth it. As first published in Claims Journal.

Telemetry, IoT, wearables, AI, chatbots and drones are tools that help group insurers better engage with customers and improve business processes. There is one thing that all of these technologies have in common: data. Personal data to be precise. Exactly how insurers will mine, manage and utilize the massive amounts of data now available from various internal and external sources may mean the difference between data mastery and data mystery for many carriers. In this blog, I’ll outline a few things carriers can start to think about as they incorporate big data into their corporate strategies. See also: 10 Trends on Big Data, Advanced Analytics   Start Out Simple and Stay Focused Data science is composed of several disciplines and skill sets. AJ Goldstein does an excellent job of deconstructing this complex craft into what he calls, “The Data Science Process,” consisting of six parts:

1. Frame the problem
2. Collect raw data
3. Process the data
4. Explore the data
5. Perform in-depth analysis
6. Communicate the results

Goldstein says that, although data science is a large, complex paradigm, only about 20% of the skills needed will contribute to 80% of the outcomes. So focusing on that core 20% necessary to achieve the results you’re looking for will help simplify the process and keep IT departments focused on the goals you originally set out to achieve with the data. Applications for big data in insurance currently center on providing solutions to tasks like premium setting, fraud reduction and target marketing. How this looks will differ across projects, but regardless of the application, data experts will collect data from various sources, analyze it and use it to draw conclusions about how the company can improve the bottom line and provide value to customers. They don’t call it big data for nothing! The amount is gigantic. New variables and trends that arise can easily lead you astray from the original question you set out to answer. Stay on track and focus on what you set out to determine. You can always circle around to address new insights later. Mitigating Risks Consumers know that sharing data comes with risks. Even the most hardened networks can be vulnerable to cyber-attacks and data breaches, leaving consumers understandably wary of how and with whom they share their personal information. Carriers that take the proper cybersecurity measures will be better prepared to ward off or respond to breaches. Obtaining accreditations such as ISO 27001 may help identify any gaps before hackers do. Privacy is another important factor when obtaining and storing customer data. Consumers want to know what their data is being used for and be assured that it will not be used for anything else. If carriers can guarantee this, studies show that customers are willing to provide personal data in exchange for lower fees and improved services. See also: Next Step: Merging Big Data and AI   When the proper measures to manage big data are in place, an opportunity to form digital trust with customers is possible. If this is established, the possibilities are endless for the kind of engagement and relationships that can be developed and sustained. With information everywhere, people still value relationships they can trust. That’s never going to change. Insurers have gone from seeing the value in data, to being able to analyze it, to capitalizing on automation that is now having an immediate impact on operations. The ability to automate business front-ends and back offices has in many cases catapulted insurers into the digital age, and most are landing on their feet. This is due in no small part to strong leadership from CIOs, a shared understanding of what customers now expect and a mandate to provide it. Insurers that master big data will likely leap to the front of the pack. Those that see it as a mystery may quickly find themselves out of the race.

Given that the customer of the future wants solutions rather than products, the employee benefits provider of the future will offer a wider range of products, all designed to work together. Knowing that careers are becoming to be more fragmented (shorter tenure, parallel income streams, the gig economy), the employee benefits provider will also reduce its dependence on employers. See also: The Insurer of the Future – Part 10   The employee benefits provider of the future will offer a broad platform with multiple products (not least life, retirement, health, auto and home) open to employees of multiple companies. That doesn't mean every employer's scheme will be the same, as there will still be specifics tuned to the desires of individual companies. But all of the core covers will be the same, allowing the benefits provider to leverage massive buying power, securing excellent deals for employers and employees alike. Because the core benefits are the same across companies, they're also portable - very helpful in a world where employees hop regularly from job to job. When an employee leaves company A, he or she can port the entire benefits package to company B. If there’s any premium shortfall, the employee can pay that personally. And the employee benefits provider retains the employee as a customer for longer. But the new proposition goes even further - because the model recognizes the gig economy and is therefore open to one-person businesses such as Uber drivers as well as employees of larger companies. This, of course, further broadens the customer base served by the provider. See also: 4 Hot Spots for Innovation in Insurance   In time, workers' loyalty will perhaps become more focused on their employee benefits provider than on their individual employers - re-positioning this segment of the industry and opening up further opportunities for innovation. Earlier articles in this series can be found here.

In the last few years, insurers have raced to capture the massive opportunities created by new technologies and have learned to turn the threat of insurtech startups into smart collaborations. The result has been the avoidance - so far - of any significant loss in revenue and profitability. Nevertheless, not only does technology continue to progress rapidly, but the American and Chinese tech giants, with their global ambitions, pose new challenges to the industry. Policyholders have come to expect the same level of convenience and engagement from their insurers, and some observers even start to fear that, in their ruthless march to global domination, those giants may encroach into insurers’ territory. Insurers have a window of opportunity to leverage their consolidated customer base, deep industry knowhow and solid balance sheets to strengthen their competitive position. Look at what happened to Google Compare, an auto insurance aggregator launched in U.S. and U.K. that has been far from successful. To pursue long-term profitable growth, insurers should start focusing on opportunities for non-disruptive market creation, as well. Most of us, including insurers and insurtech startups, have come to equate technology with disruption, where a market is created by a new solution that displaces an existing one. Look at the KYC technologies that are replacing the need for face-to-face interactions. In reality, as pointed out by Professors Kim and Mauborgne in "Blue Ocean Shift," the sequel to their global best seller "Blue Ocean Strategy," a focus on disruption is limiting and leaves half the opportunities to create growth and markets off the table. The key is to realize that we do not necessarily need to destroy an existing market to create a new one. While disruption sets out to better solve an existing problem faced by current customers, non-disruptive innovation creates “blue oceans” by targeting noncustomers of the industry or solving “brand new” problems. See also: On-Demand Insurance: Ultimately a Bust?   Take BIMA, which is creating a blue ocean by offering affordable microinsurance products to the “bottom of the pyramid.” BIMA, which was established in 2010 in Ghana, has rapidly gained scale and is now bringing microinsurance to 24 million customers across Asia, Latin America and Africa. More than 90% of its customers live on less than $10 per day, and three-quarters are accessing insurance for the first time. Developing countries have economies that are generally based on farming and agriculture and require a wide range of insurance products from health and life, accidental death and disability, agricultural and property insurance, to catastrophe cover. In those countries, microinsurance already covers around 135 million people,  but that represents only around 5% of the entire market potential. Growth is expected to be between 8% and 10% a year for the next years. Similarly, microinsurance can be marketed in developed countries to reach the underserved segments of the population who struggle to afford more comprehensive products. However, microinsurance is not just a reduced-cost coverage for low-income customer segments in both emerging and developed economies; it is an entirely new way of selling insurance and creating demand. In fact, consumers who can afford traditional covers may not perceive the need for insurance until an event occurs or an intermediary stimulates such awareness. They are often unaware of the need or just the possibility to insure against a specific risk; insurers submit complex and cryptic contracts requiring a lengthy and cumbersome purchasing process that individuals are not able or willing to follow. Not surprisingly, recent studies report that millennials are the most underinsured generation and are the least likely to have any health, rental, life and disability insurance. Millennials are just one of the segments of the so-called "connected generation," an immense blue ocean opportunity also including Generation Y and the Silent Generation, Baby Boomers, and Generation X, who are shifting to mobile purchase habits. Empowered by technology, all these individuals look for authentic services that they can access across multiple platforms and screens, whenever and wherever they need. Their protection gap is estimated at more than $3.5 trillion. The key to selling insurance to the “connected generation” is to reach them with the right proposal through engaging touchpoints on a device they swipe, tap and pinch thousands of times a day: their smartphone. Helping insurers to unlock this blue ocean opportunity with a customer-centric mobile insurance proposition is the mission of Neosurance, the start-up that we cofounded and that created the first virtual AI-based insurance engine. Neosurance stimulates the protection need “pushing” the right cover at the right time on customers’ smartphone, thus triggering an emotional and impulse purchase for a small-ticket item. Insurance purchase becomes a “rational impulse,” and the transaction is completed at the “point of need” rather than at the traditional “point of sale.” This is possible because the customer experience is entirely paperless and takes less than 20 seconds. See also: The Insurance Renaissance Rolls On   Neosurance relies on a partner-friendly “plug and play” SDK easily embeddable in any app, allowing carriers not only to target their captive audience but also to tailor their insurance proposition to the front-ends and customer journeys of their community partners. In doing so, insurers (and reinsurers) can maximize customer engagement by protecting people’s common interests and passions and build a holistic ecosystem of digital communities to create a blue ocean of uncontested demand. In the future, as insurers learn to leverage the massive amount of data they collect and to analyze it through context, psychographic and behavioral profiling, more blue ocean opportunities will be generated. In particular, carriers will be able to upgrade their role throughout the end-to end customer journey from that of a simple "payer" to that of an active "player," multiplying customer touchpoints, boosting satisfaction and ultimately creating opportunities to cross-sell insurance and non-insurance products and services. This article was originally published on InsurTechNews.com. It was written by Andrea Silvello and Luciano Pezzotta.