Borrowing a line from James Carville’s presidential campaign advice, “It’s the economy, stupid,” we need to grasp the real source of sustained growth and say to ourselves, “It’s the customer experience, stupid.” Can we wake up and focus on the customer experience? Do we truly understand what they want? Do we understand that customer experience isn’t just about technology, transactions and 24/7 availability? Are we prepared to go beyond the processes and needs of the insurance company and look at insurance from the outside in? As you may have noticed, we are increasingly living in an experience-driven culture as opposed to a possessions-focused culture. In May 2016, Groupon launched an ad campaign surrounding the “Haves and Have Dones.” It’s a funny lampoon of those people who seek luxurious things vs. those who are looking for adventures and experiences. This cultural focus on doing more and buying less isn’t new; it’s just gaining traction. The implication for insurance is that customer experience is more relevant than ever because great experiences are highly valued. Insurers need to make their brand experiences into havens of ease, comfort and security that also fit into customers’ desired lifestyles. To dig deeper in understanding, insurers need to create customer personas and develop customer journey maps that will bring empathy into experience design. See also: How the Customer Experience Is Shifting   Personas — Bridges to Empathy Customer personas synthesize real-life examples into one, easy-to-understand picture of a common role or person. For example: Roger Thompson is a veterinarian. He works long hours, and most of them aren’t at a desk with easy access to his laptop. He makes a good income, but he isn’t wealthy. He is forced to fit his paperwork into Sunday afternoons. He cares passionately about animals and is not so passionate about anything that adds administrative time to his already-packed schedule. If faced with a choice between price and convenience, he will almost always pick convenience —though he remains price-conscious. This is just a slice of a persona. It instantly transports us into the shoes of that customer type, so that we can begin to see life from behind his eyes. If we know Roger’s motivations, worries and life pain points, we can better craft his customer journey. Personas simplify business conversations. If we all understand Roger’s needs, we are far more likely to agree about what it will take to make his experiences better. We can replicate this many times over with any type of role that is relevant to the insurance experience. Every time we do, we’ll get beyond our tendency to see customers as giant groups to capture an individual’s feelings during moment-by-moment needs and choices. Once the persona is created, a customer journey map can be developed. It is difficult to create the one without the other. The persona operates as the constant filter of feelings and issues that provide the real empathy while we consider what the journey looks like. Customer Journey Maps — Paths to Understanding Journey mapping is just what it sounds like — walking through the customer experience through the persona’s eyes and in the persona’s shoes. For example, Reema Patel is a sales rep for a shoe manufacturer. She commutes with a company car. Most days, her personal vehicle sits in the garage. She enjoys most of her interactions with her home/auto insurer, but every time she sees the insurance statement she gets a little irritated that she pays to cover something that gets so little use. Slicing the journey into common interaction and reaction points will help insurers see where the journey has hurdles. What aren’t we seeing in customer service surveys? What parts of the journey can we improve? Is there any part of life where the persona is prone to dislike their insurance experience? This is where journey mapping pays off for insurers. Our goal is to give the customer a brand experience that exceeds expectations, even if that experience requires less interaction and even if it changes the nature of our relationship. We simply need to be paying attention to what the experiences are and what they could be. Tesla makes a great example of an organization translating customer personas into customer journeys and then into improvement in customer experience. At some point, Tesla executives must have asked, “How can we improve auto ownership for Tesla customers?” The answer was to bundle the vehicle, its insurance and its maintenance (which it is actually experimenting with in Asia). In one step, Tesla removed hundreds of transactions from the owner over the life of the vehicle, consolidating payments and creating a lower-stress experience. A person like Reema might like the idea that coverage and ownership are all-in-one. How can insurers capitalize on customer journey mapping? Precisely by doing what Tesla did — using the maps to redesign the experience. You may insure small business owners. What does their day look like? What are their common risks? What happens when something goes wrong? Is there a way to move their brand experience from good to great? Redesigned Experiences — “That brand fits me” It’s an experience, not a transaction. If insurers adopt the mindset that we are shifting from core transactional experiences to customer experiences, then they will instantly be able to brainstorm new ways of supporting the customer. These might fall outside of traditional insurance operations. A high-volume motorcycle insurer, for example, might build an online community for cycle enthusiasts. Anyone can join. The company creates an experience that moves from the road into the living room. The insurer may capitalize on promotional opportunities to sell insurance, but is also gaining a deep understanding of the motivations and experiences of those who are insured. The company will understand the many types of motorcycle riders. It won’t be creating products that are one-size fits all. Those who ride will ultimately see that the brand fits them. “This is the insurer that understands riders like me.” See also: How to Bottle Great Customer Experience It’s an experience, not a technology. If sensors in my basement notify my insurer that I have water damage and the company schedules remediation without my making a claim, then my experience has improved and my loyalty has been assured. Likewise, if my auto insurer sends me a message to put my car in the garage because of an approaching hail storm, the company is looking out for my welfare and has improved my experience. These would be benchmark experiences for customers. They use new technology, but they are still focused on the experience. When we focus on the customer experience, we peek into customer minds and feed our own opportunity list with inspirations based on their thoughts, actions and feelings. We introduce a loop of feedback and improvement that will provide sustainable growth. And, we unify our organizations behind a culture of empathy and action. A focus on customer experience will give customers brand love — and that’s about the best result we could ask for.

This is the first part of a two-part series. Abstract Examining the intersection of research on the effects of (re)insurance risk diversification and availability of big insurance data components for competitive underwriting and premium pricing is the purpose for this paper. We study the combination of physical diversification by geography and insured natural peril with the complexity of aggregate structured insurance products, and furthermore how big historical and modeled data components affect product underwriting decisions. Under such market conditions, the availability of big data components facilitates accurate measurement of inter-dependencies among risks, and the definition of optimal and competitive insurance premium at the level of the firm and the policy holders. In the second part of this article, we extend the discourse to a notional micro-economy and examine the impact of diversification and insurance big data components on the potential for developing strategies for sustainable and economical insurance policy underwriting. We review concepts of parallel and distributed algorithmic computing for big data clustering, mapping and resource reducing algorithms. Introduction This working paper will examine how big data and fast compute platforms solve some complex premium pricing and portfolio structuring and accumulation problems in the context of flood insurance markets. Our second objective is to measure the effects of geo-spatial insurance risk diversification through modeling of interdependencies and show that such measures have impact on single risk premium definition and its market cost. The single product case studies examine the pricing of insurance umbrella coverage. They are selected to address scenarios relevant to current (re)insurance market conditions under intense premium competition. Then we extend the discourse to a micro-economy of multiple policy holders and aim to generalize some finding on economies of scale and diversification. The outcomes of all case studies and theoretical analysis depend on the availability of big insurance data components for modeling and pricing workflows. The quality, usability and computational cost of such data components determine their direct impact on the underwriting and pricing process and on definition of the single risk cost of insurance. 1.0 Pricing Aggregate Umbrella Policies Insurers are competing actively for insureds' premiums and looking for economies of scale to offset and balance premium competition and thus develop more sustainable long-term underwriting strategies. While writing competitive premium policies and setting up flexible contract structures, insurers are mindful of risk concentration and the lower bounds of fair technical pricing. Structuring of aggregate umbrella policies lends itself to underwriting practices of larger scales in market share and diversification. Only large insurers have the economies of scale to offer such products to their clients. Premium pricing of umbrella and global policies relies on both market conditions and mathematical modeling arguments. On the market and operational side, the insurer relies on lower cost of umbrella products due to efficiencies of scale in brokerage, claims management, administration and even in the computational scale-up of the modeling and pricing internal functions of its actuarial departments. In our study, we will first focus on the statistical modeling argument, and then we will define big data components, which allow for solving such policy structuring and pricing problems. See also: 3 Reasons Insurance Is Changed Forever   We first set up the case study on a smaller scale in context of two risks -- with insured limits for flood of $90 million and $110 million. These risks are priced for combined river-rain and storm surge flood coverage, first with both single limits separately and independently and then in an aggregate umbrella insurance product with a combined limit of $200 million: (1.0) Umbrella(200M) = Limit 1 (90M) + Limit 2 (110M) The two risks are owned by a single insured and are located in a historical flood zone, less than 1 kilometer from each other. For premium pricing, we assume a traditional approach dependent on modeled expected values of insured loss and standard deviation of loss. To set the statistical mechanics of the case study for both risks, we have a modeled flood insurance loss data samples Q_t and S_t respectively for both risks, from a stochastic simulation - T. Modeled insured losses have an expected value E[.] and a standard deviation σ[.], which define a standard policy premium of π(.) When both policies’ premiums are priced independently, by the standard deviation pricing principle we have: (1.1) π(S_t) = E[S_t] + σ[S_t] π(Q_t) = E[Q_t] + σ[Q_t] With non-negative loadings, it follows that: (2.0) π(S_t) ≥ E[S_t] π(Q_t) ≥ E[Q_t] Because both risks are owned by the same insured, we aggregate the two standard premium equations, using traditional statistical accumulation principles for expected values and standard deviations of loss. (3.0) π(Q_t)+π(S_t)= E[S_t]+σ[S_t]+E[Q_t]+σ[Q_t] π(Q_t)+π(S_t)= E[S_t+Q_t]+σ[S_t]+σ[Q_t] The theoretical joint insured loss distribution function f_S,Q(S_t,Q_t) of the two risks will have an expected value of insured loss: (4.0) E[S_t + Q_t] = E[S_t] + E[Q_t] And a joint theoretical standard deviation of insured loss: (4.1) σ[S_t + Q_t] = √(σ²[S_t] + σ²[Q_t] + 2ρσ[S_t] * σ[Q_t]) We use further these aggregation principles to express the sum of two single risks premiums π(Q_t), π(S_t), as well as to derive a combined premium π(Q_t + S_t) for an umbrella coverage product insuring both risks with equivalency in limits as in (1.0). An expectation for full equivalency in premium definition produces the following equality: (4.2) π(Q_t + S_t) = E[S_t + Q_t] + √(σ²[S_t] + σ²[Q_t] + 2ρσ[S_t] * σ[Q_t]) = π(Q_t) + π(S_t)  The expression introduces a correlation factor ρ between modeled insured losses of the two policies. In our case study, this correlation factor specifically expresses dependencies between historical and modeled losses for the same insured peril due to geo-spatial distances. Such correlation factors are derived by algorithms that measure dependencies of historical and modeled losses on their sensitivities to geo-spatial distances among risks. In this article, we will not delve into the definition of such geo-spatial correlation algorithms. Three general cases of dependence relationships among flood risks due to their geographical situation and distances are examined in our article: full independence, full dependence and partial dependence. 2.0 Sub-Additivity, Dependence and Diversification Scenario 2.1: Two Boundary Cases of Fully Dependent and Fully Independent Risks In the first boundary case, where we study full dependence between risks, expressed with a unit correlation factor, we have from first statistical principles that the theoretical sum of the standard deviations of loss of the fully dependent risks is equivalent to the standard deviation of the joint loss distribution of the two risks combined, as defined in equation (4.1). (4.3) σ[S_t + Q_t] = √(σ²[S_t] + σ²[Q_t] + 2σ[S_t] * σ[Q_t]) = σ[S_t] + σ[Q_t] For expected values of loss, we already have a known theoretical relationship between single risks’ expected insurance loss and umbrella product expected loss in equation (4.0). The logic of summations and equalities for the two components in standard premium definition in (4.0) and (4.3) leads to deriving a relationship of proven full additivity in premiums between the single policies and the aggregate umbrella product, as described in equation (4.2), and shortened as: (4.4) π(Q_t + S_t) = π(Q_t) + π(S_t) Some underwriting conclusions are evident from this analysis. When structuring a combined umbrella product for fully dependent risks, in very close to identical geographical space, same insured peril and line-of-business, the price of the aggregated umbrella product should approach the sum of single risk premiums priced independently. The absence of diversification in geography and insured catastrophe peril prevents any significant opportunities for cost savings or competitiveness in premium pricing. The summation of riskiness form single policies to aggregate forms of products is linear and co-monotonic. Economies of market share scale do not play a role in highly clustered and concentrated pools of risks, where diversification is not achievable, and inter-risk dependencies are close to perfect. In such scenarios, the impact of big data components to underwriting and pricing practices is not as prominent, because formulation of standard premiums for single risks and aggregated products could be achieved by theoretical formulations. See also: #1 Affliction Costing Businesses Billions   In our second boundary case of full and perfect independence, when two or more risks with two separate insurance limits are priced independently and separately, the summation of their premiums is still required for portfolio accumulations by line of business and geographic and administrative region. This premium accumulation task or "roll-up" of fully independent risks is accomplished by practitioners accordingly with the linear principles of equation (3.0). However, if we are to structure an aggregate umbrella cover for these same single risks with an aggregated premium of π(Q_t + S_t) , the effect of statistical independence expressed with a zero correlation factor will reduce equation (3.0) to equation (5.0). (5.0) π(Q_t + S_t) = E[S_t + Q_t] + √(σ²[S_t] + σ²[Q_t]) Full independence among risks more strongly than any other cases supports the premium sub-additivity principle, which is stated in (6.0). (6.0) π(Q_t + St) ≤ π(Q_t) + π(S_t) An expanded expression of the subadditivity principle is easily derived from the linear summation of premiums in (3.0) and the expression of the combined single insurance product premium in (5.0). Some policy and premium underwriting guidelines can be derived from this regime of full statistical independence. Under conditions of full independence, when two risks are priced independently and separately the sum of their premiums will always be larger than the premium of an aggregate umbrella product covering these same two risks. The physical and geographic characteristics of full statistical independence for modeled insurance loss are large geo-spatial distances and independent insured catastrophe perils and business lines. In practice, this is generally defined as insurance risk portfolio diversification by geography, line and peril. In insurance product terms, we proved that diversification by geography, peril and line of business, which are the physical prerequisites for statistical independence, allow us to structure and price an aggregate umbrella product with a premium less than the sum of the independently priced premiums of the underlying insurance risks. In this case, unlike with the case of full dependence, big data components have a computing and accuracy function to play in the underwriting and price definition process. Once the subadditivity of the aggregate umbrella product premium as in (6.0) is established, this premium is then back-allocated to the single component risks covered by the insurance product. This is done to measure the relative riskiness of the assets under the aggregate insurance coverage and each risk individual contribution to the formation of the aggregate premium. The back-allocation procedure is described further in the article in the context of a notional micro economy case. Scenario 2.2: Less Than Fully Dependent Risks In our case study, we have geo-spatial proximity of the two insured risks in a known flood zone with measured and available averaged historical flood intensities, which leads to a measurable statistical dependence of modeled insurance loss. We express this dependence with a computed correlation factor in the interval [0 < ρ' < 1.0]. Partial dependence with a correlation factor 0 < ρ' < 1.0 has immediate impact on the theoretical standard deviation of combined modeled loss, which is a basic quantity in the formulation of risk and loading factors for premium definition. σ[S_t + Q_t] = √(σ²[S_t] + σ²[Qt] + 2ρ'σ[S_t]σ[Q_t]) ≤ σ[S_t] + σ[Q_t] This leads to redefining the equality in (4.3) to an expression of inequality between the premium of the aggregate umbrella product and the independent sum of the single risk premiums, as in the case of complete independence. (7.0) π(Q_t + S_t) = √(E[S_t + Q_t] + σ2[S_t] + σ²[Q_t] + 2ρ'σ[S_t] * σ[Q_t]) ≤ π(Q_t) + π(S_t)  The principle of premium sub-additivity (6.0), as in the case of full independence, again comes into force. The expression of this principle is not as strong with partial dependence as with full statistical independence, but we can clearly observe a theoretical ranking of aggregate umbrella premiums π(Q_t+S_t) in the three cases reviewed so far. (7.1) π^{Full Independence} ≤ π^{Partial Dependence} ≤ π^{Full Dependence} This theoretical ranking is further confirmed in the next section with computed numerical results. Less than full dependencies, i.e. partial dependencies among risks, could still be viewed as a statistical modeling argument for diversification in market share geography, line of business and insured peril. Partial but effective diversification still offers an opportunity for competitive premium pricing. In insurance product and portfolio terms, our study proves that partial or imperfect diversification by geography affects the sensitivity of premium accumulation and allows for cost savings in premium for aggregate umbrella products vs. the summation of multiple single-risk policy premiums. 3.0 Numerical Results of Single-Risk and Aggregate Premium Pricing Cases In our flood risk premium study, we modeled and priced three scenarios, using classical formulas for a single risk premium in equation (1.0) and for umbrella policies in equation (7.0). In our first scenario, we price each risk separately and independently with insured limits of $90 million and $110 million. In the second and third scenarios, we price an umbrella product with a limit of $200 million, in three sub-cases with {1.0, 0.3 and 0.0} correlation factors, respectively to represent full dependence, partial dependence and full independence of modeled insured loss. We use stochastic modeled insurance flood losses computed with high geo-spatial granularity of 30 meters. The numerical results of our experiment fully support the conclusions and guidelines that we earlier derived from theoretical statistical relationships. For fully dependent risks in close proximity, the sum of single-risk premiums approaches the price of an umbrella product, which is priced with 1.0 (100%) correlation factor. This is the stochastic relationship of full premium additivity. For partially dependent risks, the price of a combined product, modeled and priced with a 0.3 (30%) correlation factor, could be less than the sum of single-risk premiums. For fully independent risks, priced with a 0 (0.0%) correlation factor, the price of the combined insurance cover will further decrease to the price of an umbrella on partially dependent risks (30% correlation). Partial dependence and full independence support the stochastic ordering principle of premium sub-additivity. The premium ranking relationship in (7.1) is strongly confirmed by these numerical pricing results. See also: How Quote Data Can Optimize Pricing   Less than full dependence among risks, which is a very likely and practical measurement in real insurance umbrella coverage products, could still be viewed as the statistical modeling argument for diversification in market share geography. Partial and incomplete dependence theoretically and numerically supports the argument that partial but effective diversification offers an opportunity for competitive premium pricing.

As the insurance industry enters a period of profound change, we at Eos use a concept called the 20/20 dynamic to illustrate the point: On a conservative basis, we believe most insurers risk losing at least 20% of their business to disruption. On the flip side, for those that embrace innovation there is an opportunity to grow their business by 20%. Our goal is to ensure our strategic investors are on the right side of this equation. Insurtech represents a unique opportunity for insurers to evolve their business model. Insurtech is not necessarily about disruption, but more an opportunity to take advantage of technology and data to create innovative solutions, reduce costs and capture greater value for customers, brokers and intermediaries, underwriters and service providers. At one level, active participation is required just to meet the basic requirements of playing in the new market. For those committed to a strategic approach, insurtech can help drive true competitive differentiation, while enabling measured bets for the future. See also: Insurtech: Unstoppable Momentum   Underpinning this transformation are 10 key trends that we have identified and believe will be at the heart of the insurtech evolution:

1. Insurance, as we have it known it historically, will be bought, sold, underwritten and serviced in a fundamentally different way within the next three years
2. External data and contextual information will become increasingly more important than historical internal data for predicting risk and pricing
3. A majority of the simple covers will be bought in standard units through a marketplace/ exchange, permitting just-in-time, need and exposure based protection through mobile access
4. Solutions will continue to evolve from protection to behavioral change then to prevention -- even across complex commercial insurance
5. Although proliferation of data and increasing transparency on the buyer and seller will cause disintermediation for simple covers, it will also create opportunities for brokers and intermediaries to innovate solutions and channels for their B2C (non-standard risk pools, retirees/older generation, healthcare gaps) and B2B (emerging and unknown risks, cyber, global supply chains, cross-border liability, terrorism) customers
6. The ability to dynamically innovate (new risk pools, new segments, new channels) and deliver on the customer promise will become the most important competitive advantage (as known risks continue to get commoditized and move to the direct channels)
7. Internal innovation, incubation and maturing of capabilities will no longer be the optimal option; dynamic innovation will require aggressive external partnerships and acquisitions
8. Simple "Grow or Go" decisions of the last decade will be sub-optimal, as the dust settles in insurtech; building in future optionality and degrees of freedom will be the key
9. Consolidation just for economies of scale will provide increasingly less marginal value in non-life as well as life insurance; real value creation will come from "economies of skill" and digital capabilities
10. Deep learning (next generation of AI), blockchain and genomics technologies will improve financial inclusion and better meet the needs of the under-insured and uninsured

We have linked the above trends to analysis of how profit pools will change over time to build an investment strategy that also focuses on platforms or clusters that allow us to build more compelling propositions by connecting related players in adjacent parts of the value chain. Three areas of initial focus are: 1. A digital front office solution that leverages an open architecture platform developed by Convista (OneDigitalOffice), augmented by relevant startups including, for example, on-demand insurance by Oula.la and social media adoption by Digital Fineprint. The ability to drive dynamic innovation is driven by technology stack/system flexibility to respond quickly to customer needs. New risk pools, new products and new ways to reach customers will place massive pressure on traditional systems, making a dynamic digital front office key to execution.

• 360-degree multi-channel (direct, field sales force, internal sales force, independent agents/brokers) connectivity
• Augmented functionality across the value chain from sales/distribution through underwriting, binding and servicing
• Sales funnel optimizer (sales force effectiveness) -- inquiry/quote, quote-to-submission, submission-to-bind ratio
• Sales force/intermediary (broker/agent) segmentation and performance management

As an example, the impact of the sharing economy and need for on-demand insurance will require instant pricing and cover that switches on and off at point of sale to meet the needs of the customer. 2. An end-to-end claims solution developed by RightIndem and supported by additional capability from other technology providers The claims space is an interesting one; it represents the largest individual expense on any P&C insurer's P&L but conversely has seen very little innovation. This is now starting to change. RightIndem has developed a platform that achieves significant improvements in customer satisfaction while significantly reducing the cost of managing the claim. This is a win/win for the customer and the insurer and in our view a classic enabler technology that takes an existing function within the insurance value chain but does it much more effectively and with the interests of the customer at its core. See also: Insurtech Checklist: 10 Differentiators   3. Artificial intelligence (AI) with an initial focus on life and health insurance developed by Gen.Life We are particularly excited about the combination of AI and the latest health technology to transform insurance. Examples include Livingo Health, which combines a blood glucose monitor support and intervention to help coach people through diabetes, and Cycardia Health, which employs machine learning predictive analytics software to categorize abnormal circadian patterns in otherwise healthy breast tissue to provide early detection of breast cancer. These types of technology will allow the early detection of potential diseases so that preventative treatment can be started much earlier, dramatically improving chances of success. Rather than life and health insurance being about prospective payments after an event, they can become the key mechanism for deploying technology to allow people to enjoy healthy lives. The insurance industry will look very different in five years, but more importantly there is an opportunity to drive huge benefits to society through reducing under-insurance and supporting the transition from protection to prevention.

The really bad thing about risks is that they almost never lead to a loss. Why would that be bad? Because risk aversion is responsible for so much lost opportunity. Risk aversion allows us to shoot down ideas faster than we build them up. It is easier to cite a risk that a project or change effort will fail than to undertake it. But getting ahead means change, and change means risk. Those who prosper in business, take risks. The list of leaders who wouldn’t take risks is a very short one.

The biggest risk is not taking any risk… In a world that’s changing really quickly, the only strategy that is guaranteed to fail is not taking risks. – Mark Zuckerberg

In other blogs, we’ve written about the price you pay by not adapting. It’s not a question of whether you should take risks, but how to do it well. Nearly all great advances come through leaders' appreciating an opportunity and understanding how to manage its risks. So often in our discussions with companies, we encounter leaders who acknowledge the opportunity we present, but who are stymied by perceived obstacles to acting on it. The focus turns too heavily to what lies in the way rather than the path. These obstacles become risks – “unknowns” – that inhibit desired transformation. See also: 4 Steps to Integrate Risk Management   Risks are an integral, essential part of improvement. They force us to think, to adapt, to learn. Companies that advance don’t do so by avoiding risk. They don’t do so in spite of risk. They do so in conjunction with risk. The question is not how to avoid risk, but how to embrace it. Leaders love risk as a thing to be conquered, not feared. Here’s how they do this. Define your goals in detail before you define your risks. It’s easy to get sidetracked by anxiety. The mention of a potential goal is more often met by caveats about it than by building out the path to the goal. Leaders focus first on the goal and the value of achieving it. Act on risks constructively. Draw up a list of risks that could materialize on your way to your goal. Define each risk (what it is), what impact the risk can have, the probability that it will occur and what should be done to manage it. Most often, the risk is much smaller than you imagine. For example, if we more aggressively negotiate medical cost reductions, we might generate more litigation. That might cost us $2,500 per case. This might happen on 10% of the cases. Don’t stop there….compare your risks to your upside – Our plan will result in reductions of more than $1,800 on 80% of all cases…..The upside is positive. Keep moving forward. Enlist people in problem solving…not problem identification. When engaging with others on a project or change effort, dwelling on risks leads to managing against a fear of failure. Get people involved in answering this question: “How can we achieve [name the goal]?” – rather than “what do you think of [name the goal]?” For example, how might we successfully achieve medical-cost reductions without generating litigation?” Plan for success and manage your way there. Managing risk doesn’t mean playing defense against potential disaster. On the contrary, it means keeping a clear eye on what you want. When we implement with a client, we have a detailed list of action items to be accomplished by our customers and us. Tedious? Not really. Critical to success? Absolutely. We know the critical success factors and we plan for and execute on them. Use risk to learn. We all know that stuff happens. Recognize it. Learn from it. Move on. Peter Drucker, the famous management guru, said it best…..

“People who don’t take risks generally make about two big mistakes a year. People who do take risks generally make about two big mistakes a year.”

See also: Are Portfolios Taking Too Much Risk?   If you’re feeling hemmed in by risks, take a different approach. Embrace it. It’s the only way to master it.

When I first met Dan Ariely (now chief behavioral officer at Lemonade, among his many other duties) 15-plus years ago, he told a story about how people will, well, cheat on their insurance applications. He said he got a large car insurer to let him experiment with 12,000 applications. For the control group of 6,000, he changed nothing. They filled out the form, including how many miles they drove each year, and signed at the bottom. With the other 6,000, he had people sign at the top, attesting at the beginning of the process that everything they were about to fill in would be true. Dan drily reported that, "It turns out that those who sign at the top of the form drive 3,000 miles a year more than those who sign at the bottom."

Since getting involved in the insurance industry 3 1/2 years ago, as part of my three-decade focus on digital technology and its prospects for innovation, I've learned that lots of people fudge, and not just on how many miles they drive. People will find a homeowners policy a bit too expensive, so they'll fiddle with the facts and report that the home had the roof replaced recently or forget to report that trampoline, or something. What really surprised me is how much people get away with fudging (I'm trying to be kind here) on whether they even have insurance. They'll get a certificate of insurance, so they can show that a policy was in force at a particular moment in time, but then what? How do you know that policy is in force a day later? A week? A month? Six months?

With the advent of Trov, Slice and a few other innovators, it's now possible to turn coverage on and off (for some things) in an instant, but businesses still don't really know whether those they're working with have the requisite insurance at any particular moment. And if they want to find out, businesses have to wade through a sea of paper (or sometimes PDFs) that must total in the tens of millions each year in the U.S. alone.

Well, I'm happy to report that as part of our work at the Innovator's Edge site, which tracks the roughly 800 insurtech startups, we've come across a company that has a solution. That company is GAPro, and you can learn more about it here. In an era where software-as-a-service has become commonplace, GAPro provides what it calls verification-as-a-service. You get a dashboard that tells you in real time the coverage status of your contractors, brokers or whomever -- green signifying that all is in order, yellow highlighting those whose policies are up for renewal soon and red showing those whose policies have lapsed. 

GAPro is still in its early days -- that's what insurtech is all about, right? -- but the idea is spot on, and those running the company are seasoned pros, so I'm confident they're going to get this right. That's why we've begun working with them to provide some counsel and, of course, to help them get the word out. You can read the announcement about our new relationship here. If you have a problem managing certificates of insurance -- and who doesn't? -- I encourage you to contact them. 

Cheers,

Paul Carroll,
Editor-in-Chief

A year ago, as the insurtech movement was starting to take off, a general partner at Andreessen Horowitz made a presentation to our semi-annual gathering of two dozen insurance company CEOs at Google headquarters and began by saying: "Distribution trumps innovation."

In some ways, that's an obvious statement. An innovation can't spread until you find that first customer, and then the second and then the third. But the statement was still startling to hear from a leader at a premier venture capital firm known for celebrating innovation. I jotted down the line, then looked around the room and saw that just about everyone else was, too.

Distribution trumps innovation.

While we have spent years at ITL fostering innovative ideas by curating what is now more than 2,600 articles by more than 800 of the best thinkers in the insurance and risk management industry, we decided to tackle the distribution piece of the puzzle, too. We began by cataloging all the insurtech startups, a list that now exceeds 800, and making it as easy as possible to search the startups based on technology, location, problem being addressed, etc. We gathered that information at a site that I've mentioned several times to you now: the Innovator's Edge. We've found that those looking for innovators need to know more, so we've taken the next step.

We've developed what we call Innovator's Edge Exchange. The foundation of IEx is the Market Maturity Review, which takes startups about a half-hour to fill out and which provides detail on where they stand in five crucial areas: the maturity of their technology, of their experience in the insurance industry, of their work with relevant regulations, of their operations and of their funding. Startups have begun to fill out these surveys to be discovered by potental customers, and I encourage everyone on the list to do so by going to the IE Exchange site. The more complete the list of startups in IEx, the more valuable it'll be to incumbents looking for innovators, and the more likely everyone will benefit. If you're not on the list and should be, please let me know at paul@insurancethoughtleadership.com. If you're an incumbent and want to be notified when you can sign up to get access to all the market maturity information we're gathering -- probably in late February -- you can sign up here. 

Cheers,

Paul Carroll,
Editor-in-Chief

It looks like 2017 is continuing right where 2016 left off—with news of a massive data leak and thousands of passwords being exposed on the internet and cached by search engines. This refers to the gaping security flaw recently discovered in the widely used Cloudflare service. It goes without saying that you should immediately change all your passwords, given how deeply embedded into the internet Cloudflare is. You also should seriously consider using a multifactor step-up capability to access your more sensitive websites and services. Related article: Cloudflare bug spills passwords in plaintext Your identity has become a “currency,” and criminals are able to sell it like other data. Unfortunately, many organizations are dragging their feet in adopting more advanced and secure methods for allowing customers to connect with their services. For the near term at least, passwords are here and will be here for the next few years. See also: The 7 Keys to Strong Passwords   In terms of security and availability, passwords are the lowest common denominator. They are cheap to deploy, users understand how to interact with them, and the risks associated with the username and password paradigm—while not fully understood—are accepted. But, there are three key factors converging that will replace these username and passwords in the future. Many more savvy about security First, policy- and decision-makers are becoming more sophisticated in their understanding of the risks and security profile that simple reliance on passwords presents. Recent announcements from Yahoo CEO Marissa Mayer and General Counsel Ronald Bell should be a bellwether in this regard. Following YAYB (Yet Another Yahoo Breach), Bell resigned without severance pay, and Mayer lost her annual cash bonus and equity award—which some reports estimate to be worth upward of $14 million. Governmental regulations—such as the revised payment services directive (PSD2) in Europe—are requiring more stringent authentication requirements for financial institutions while the National Institute of Standards and Technology in the U.S. no longer recommends one-time passwords (OTPs) being delivered via SMS in its Digital Authentication Guideline. Password reliance and its associated pain is a global problem. Advances in biometrics, other alternatives Second, viable alternatives to the password are gaining widespread acceptance. Since the release of the fingerprint scanner on the Apple iPhone 5S, biometrics have exploded as an alternative to PINs and passwords. Related article: China embraces FIDO Alliance standards The FIDO Alliance has grown as an industrywide organization popularizing a set of specifications that increase privacy, increase security and increase usability while at the same time allowing the multitude of players from the authentication marketplace to ensure interoperability. Adoption of such alternatives is moving along at a solid clip with millions of users worldwide already using this technology. Consumers demand more Finally, users are fed up. They have learned of breach after breach after breach. The added features that complicate a password are not actually making it more secure, but they do make passwords significantly more difficult to input on the small touchscreens that are becoming our primary computing devices. As these three forces continue to converge, passwords will be replaced in greater and greater numbers. As a society, we need to overcome password pain and look to the future. Using a fingerprint or other biometric authentication measure helps users look beyond the failed username and password infrastructure. In time, the public will understand how flawed traditional password usage is. It’s both inconvenient and insecure. See also: How to Make Smart Devices More Secure   In 2017, we will see more companies erring on the side of security, removing passwords and implementing modern authentication strategies that eliminate the opportunity for large-scale password leaks and theft. This post originally appeared on ThirdCertainty. It was written by Phil Dunkelberger. 

According to a recent Innovation Management post, Are Corporate Innovation Centers Too Big To Fail?, the number of such centers or labs across the globe jumped from 301 to 456 over the course of the 15 months ended in October 2016. This 60%-plus increase reflects legacy enterprise efforts to deal with inescapable disruption. Boards and the C-suite see labs adding value by:

• Driving understanding and alignment of their businesses to changing customer and market needs,
• Attracting people with expertise who are entirely different from the talent running day-to-day operations. These are the kinds of people who can spot trends far in advance and connect the execution dots to commercial opportunity, and
• Accelerating innovation of all types – business model, product, brand, experience and process, among others – attacking the revenue anemia and margin compression that afflicts pre-digital companies.

See also: What Is the Right Innovation Process?   One segment of innovation labs is just theater. There is another segment spawning high-potential revenue streams that would not have come about otherwise. But too many are established with good intentions and high expectations, yet run into a common set of seven failure points:

• Innovation is treated like a project or activity. It is deemed essential yet isn't integral to the strategic plan projections. Worse, innovation is booked as an expense line for a year or two, with a vague future dependent on how everything else is going. I empathize with the CEO’s decision to assume no upside on revenue as a way of managing the high risk of failure associated in particular with disruptive innovation. However, this decision creates unintended consequences, starting with undermining accountability for results. The lab may be destined to deliver a self-fulfilling prophecy – either not getting results or not being able to measure the results that pilots generate.

• Innovation is placed in a protective but isolating bubble. It is smart to protect new ideas from the natural instincts of a mature organization. The problem becomes that being placed inside a bubble – e.g., physical location or reporting relationship -- makes the lab feel even more foreign to everyone else. The result is a reduced chance of ever being integrated to accelerate scale, leverage the organization's reusable infrastructure, access the client base or tap into existing brand equity.

• Top-of-house leadership lacks skills or courage. At the end of the day, if the CEO or business unit head do not have skin in the game, or do not actively hold all directs accountable for the lab’s success, innovation efforts cannot take off. Perhaps the CEO has checked the box for the board by creating an innovation lab, and then allows a budget cycle or two to pass, assuming this is enough time to produce results. Or this is such a new game that, through nobody’s fault, there is a lack of expertise on how to drive a successful lab effort. New roles are created, and hiring mistakes are made.

• The lab is expected to find the silver bullet answer to a poorly defined problem. "It's a technology problem." Or, "We need partners." Or, "We need to move everyone to Silicon Valley." And so on. Survey results reviewed in Digital Dynasties: The Rise of Innovation Empires Worldwide reveal that “partnering with ecosystem” is the core operating objective. Toward what end? What marketplace problems is the lab trying to solve? There is often not an answer grounded in an understanding of the unmet needs of large enough segments toward which the lab can point its energy.

• Enabling capabilities and governance get insufficient attention. Gaps in infrastructure, data access, process, policies, metrics, goals and communications require as much or perhaps more attention than coming up with the innovation concepts themselves. Executing innovation to achieve commercial impact demands that those involved get dirt under their nails at every level of the organization. Ideas get lots of focus. Reality is that the hard work is in the unglamorous details of navigating bureaucracy, reforming status quo procedure to allow for speed and agility and motivating the organization as a whole to support change.

• Talent criteria to succeed are demanding, making people hard to find. Succeeding as a team member of an innovation lab takes a complex set of personal, leadership and functional abilities and skill. Identifying the right profile is tough, and then finding the people who match the spec is even tougher. There are conflicts between the politics of consensus that may be part of continued funding and the lab’s inherent challenge to the status quo.

• The culture built on past greatness can stop an innovation lab in its tracks. The right construct for an innovation lab must achieve a tough balancing act: fitting alongside the corporate culture, challenging it, and leveraging it, all at the same time.

See also: How to Create a Culture of Innovation   The most persistent failure point I have seen is to not recognize and connect the dots between the desire to innovate and the mechanics of execution and followthrough. That is why one of the biggest wins can be to break innovation execution down into small manageable steps that produce signals along the way, including progress markers such as hitting important milestones, getting a pilot to market and seeing impact, enabling capabilities to deliver or mobilizing resources against a defined set of market needs.

Insurance is the industry most affected by disruptive change, according to the percentage of CEOs who are extremely concerned about the threats to their growth prospects from the speed of technological change, changing customer behavior and competition from new market entrants. Insurers know they need to innovate to remain competitive. In fact, 67% of insurance respondents to PwC’s 2017 CEO Survey see creativity and innovation as very important to their organizations, ahead of other financial services sectors and the CEO Survey population as a whole. And, insurance CEOs noted that the area they would most like to strengthen to capitalize on growth opportunities is digital and technological capabilities, followed by customer experience (reflecting the connections between the two). However, the industry’s traditional conservatism and the dizzying pace of technological change has made it difficult to change. As a result, most insurers are looking outside the industry – typically in the insurtech space (e.g., drones, sensors, internet of things (IoT)) – for the best ways to improve their systems, processes and products. And there is no doubt that industry stakeholders think insurtech has real promise: Annual investment in insurtech startups has increased fivefold over the past three years, with cumulative funding reaching $3.4 billion since 2010, based on the companies that PwC’s DeNovo platform follows. See also: What Is the Right Innovation Process?   To facilitate a diverse approach to identifying opportunities and potential partners from different industries and specialty areas, an enterprise innovation model (EIM) is table stakes. An EIM facilitates:

• New product and service development: Being active in insurtech can help insurers discover emerging coverage needs and risks that require new insurance products and services. As a result, they can improve their product portfolio strategy and design of new risk models.
• Market exploration and discovery: Prescient insurers actively monitor new trends and innovations, and some have even established a presence in innovation hotspots (e.g., Silicon Valley) where they can directly learn about the latest developments in real-time and initiate innovation programs.
• Partnerships that drive new solutions: Exploration typically leads to the development of potential use cases that address specific business challenges. Insurers can partner with startups to build pilots to test and deploy in the market.
• Contributions to insurtech’s growth and development: As we describe below, venture capital and incubator programs can play an important role in key innovation efforts. Established insurers that clearly identify areas of need and opportunity can work with startups to develop appropriate solutions.

Most insurers are looking outside the industry for the best ways to improve their systems, processes and products. Maintaining awareness, influencing the market and identifying the right partners To ensure an organization’s innovation efforts are in sync with – or even driving – the latest developments in the market, an EIM needs a formalized yet agile process for identifying and incorporating best practices. Dedicated assessment of insurtech advancements can allow insurers to identify and promote best practices and key technologies. Moreover, maintaining a close connection with the insurtech market can help a company develop its external knowledge and relationships with innovators. Through this process, insurers can identify potential partners that can help them understand evolving technologies and their applications, and even contribute to developing the capabilities they desire. With a deeper understanding of the market, capabilities and key players, insurers can be better positioned to facilitate innovation, ideation and design. While some fintech companies already have compelling insurance applications, insurers have a great opportunity to identify and design new potential use cases. Fast prototyping is key to quickly creating minimally viable products (MVP) and bringing ideas to life. Early-stage startups develop and deploy full-functioning prototypes in near real time and go to market with solutions that evolve with market feedback. The development cycle is shortened, which allows startups to quickly deliver solutions and tailor future releases based on usage trends and feedback and to accommodate more diverse needs. Established insurers can follow the same approach or can partner with existing startups that have a MVP to help them to move to the next stage, scaling. The ways to accomplish all of this vary based on how the organization plans to source new opportunities and ideas, how it plans on executing innovation and how it plans to deploy new products and services. The following graphic provides examples of EIMs by primary function. The innovation center The innovation center (also named “lab” or “hub”) is a structure at a corporate level that bridges external innovation with business unit needs and innovation opportunities. It relies on internal subject matter experts and innovation champions to ignite and drive innovation initiatives at a business unit level. With this model, innovative new products and services go to market under the company’s brand. The innovation hub provides an outside-in view while promoting innovation internally. With this model, the company dedicates a team to constantly monitor trends and market activity, build and maintain relationships with key insurtech players, identify potential future scenarios and determine new partnership opportunities. The hub should be managed through business units to effectively innovate (i.e., building prototypes and scaling models). Execution is a key success factor, and we recommend insurers consider complementary innovation models to help promote positive outcomes. Regardless of the model they use, we recommend that insurers of all sizes consider developing an innovation center and create an external connection based on potential future scenarios. The incubator An incubator can drive innovation from idea to end product by identifying new opportunities and developing related solutions. Although it does require a significant investment of both money and resources, it has proven especially effective in addressing complex problems and devising new approaches to them. Although the incubator can be internal, external structures typically create unique development environments and attract necessary talent. Via an external approach, ideas come mostly from outside the company and a panel of internal or external innovation specialists provide high-level guidance and approval for the innovation the company is seeking through the incubator. Although the incubator initially drives innovation, business units typically become involved during the development process. They have an important role, especially when planning to deploy new solutions within the organization. The incubator can wind up as a start-up that can go to the market under its own name. One of the main strengths of the incubator model is that it facilitates execution. It holds an idea until a prototype is developed and a minimally viable product is available. The gradual involvement of business units during the process enables the model to adequately scale. Upon adoption by its future owner, the incubator and business units can address any related challenges related to operating capacity, cyber risk, regulation and other issues. Strategic venture capital (SVC) The SVC model offers the opportunity to participate via stake or acquisition in relevant insurtech-related players. This is a way to influence and shape the development of specific startups (e.g. pushing them to solve specific problems) and acquire key capabilities and talent, and as a way to derive value from strategic investments. In the SVC model, the company establishes a new ventures division, which sources ideas from the outside. The company provides funding and support for equity, while a SVC from this new structure explores, identities and evaluates solutions and markets new ventures under its own brand. The funds thatSVC invests in a startup help new players augment their capabilities and scale their business model. This could lead to potential market joint ventures, acquisitions or other deals to monetize the initial investment. Established insurers with SVC arms are usually leaders in specific market segments and therefore leverage their experience and knowledge to select key ventures. To become more active with insurtech, these structures can be linked to innovation centers, thereby allowing companies to connect ventures with business units. Instead of choosing one model over the other, we propose one that combines key elements from each. Companies can select elements based on their need for external innovation, the availability of talent, their ability to execute and the amount of investment the organization is willing to commit. EIM operating options EIM operating characteristics Bridging the cultural divide Complicating the need to innovate is the fact that an insurer’s culture often influences an external company’s decision about partnering with it. In fact, according to our 2016 Global FinTech Survey, more than half of fintechs see differences in management and culture as a key challenge in working with insurers. Insurers also realize this, and 45% of insurance companies agree that this is a major challenge. See also: How to Create a Culture of Innovation   Accordingly, insurers will need to assess the availability and compatibility of existing resources and determine how and where they can find what may not currently be available. By clearly articulating the organization’s needs, defining explicit roles and establishing a model for enterprise innovation, an insurer can address any underlying concerns it may have about partnerships. While insurers can create internal structures to support innovation, most of them will have to enlist external resources in one way or another. In fact, we expect many talented professionals without insurance-specific skills will be the ones who wind up driving innovation. Attracting and developing innovators Insurers can create internal structures to support innovation, but – as EIMs stipulate – success ultimately depends on having the right talent. And, most insurers will have to enlist external resources – ones who have an entrepreneurial mindset and who are well-connected to insurtech – in one way or another. How does a company attract and retain this kind of talent? There are four primary ways:

• Acquire the new talent from start-ups. This works well if the acquired company keeps running its business under its own start-up rules, away from the acquirer’s bureaucracy. Otherwise, if there is too much acquirer interference, then retention will be a challenge in a market that covets innovators.
• Attract the talent directly from the market. This option typically requires a new mindset from the hiring company in terms of business role, work environment and even location. Establishing a presence in relevant innovation hotspots will help make an offer more attractive, facilitate external connections and demonstrate the insurer’s commitment to letting innovators be free to innovate.
• Partner with startups, technology vendors, universities, researchers and other proven innovators. This option represents a major opportunity because it enables the insurer to create the connections to and formal partnerships with new talent. However, while identifying desired capabilities is relatively easy, there will need to be strong alignment of purpose between the organization and the new partners for the relationship to work. In this case, the Innovation Hub should be the most helpful model.
• Grow the talent. This option is probably the least disruptive because it doesn’t require external changes. Large organizations have the opportunity to discover talent within their structures. But, the organization will have to ascertain and leverage the mentality and professional background of employees in many different ways. Gamification, internal collaboration groups and other resources can help in the search for potential in-house innovators, but most companies will need a more sophisticated staffing model to develop this talent (e.g., having specific development plans and offering “external” experiences in projects and with partners).

Complementing these options is the insurance industry leadership’s advocacy of new methods to foster change in employee skill sets. According to insurance respondents to PwC’s 2017 CEO Survey,

• 61% are exploring the benefits of humans and machines working together (considerably higher than any other FS sector), and
• 49% are considering the impact of artificial intelligence on future skills needs (also considerably higher than any other FS sector).

Implications In response to this rapidly changing environment, incumbent insurers are approaching insurtech in various ways, prominently through joint partnerships or startup programs. But whatever strategy an organization pursues, insurtech’s main impact will be new business models that create challenges for market players and other industry stakeholders (e.g., regulators). In this environment, insurers will need to move away from trying to control all parts of their value chain and customer experience through traditional business models, and instead move toward leveraging their trusted relationships with customers and their extensive access to client data. For many traditional insurers, this approach will require a fundamental shift in identity and purpose. The new norm will involve turning away from a linear product push approach, to a customer-centric model in which insurers are facilitators of a service that enables clients to acquire advice and interact with all relevant actors through multiple channels. By focusing on incorporating new technologies into their own architecture, traditional insurers can prepare themselves to play a central role in the new world in which they will operate at the center of customer activity and maintain strong positions even as innovations alter the marketplace. To effectively develop these new business models and capabilities and establish mutually beneficial insurtech relationships, established insurers will need to start with a well-thought-out innovation strategy that incorporates the following:

• An effective enterprise innovation model (EIM) will take into account the different ways to meet an organization’s various needs and help it make innovative breakthroughs. The model or combination of models that is most suitable for an organization will depend on its innovation appetite, the type of partnerships it desires and the capabilities it needs. EIMs feature three primary approaches to support corporate strategy, partnering via innovation centers (or hubs), building capabilities via incubators and buying capabilities via a strategic ventures division. Companies can select elements from each of these models based on their need for external innovation, the availability of talent, their ability to execute and the amount of investment the organization is willing to commit.
• Even though insurers can create the internal structures that support innovation, most of them will have to enlist external resources in one way or another. Accordingly, they will need to assess the availability and compatibility of existing talent and determine how and where they can find what may not currently be available. Much like with enterprise innovation models, there are certain ways (often in combination) that insurers can locate and obtain the resources they need, including acquiring it, trying to attract it, partnering and growing it internally.

We know the single greatest roadblock to timely work injury recovery and controlling claim costs. And it’s not overpriced care, or doubtful medical provider quality or even litigation. It is the negative impact of personal expectations, behaviors and predicaments that can come with the injured worker or can grow out of work injury. This suite of roadblocks is classified as “psychosocial” issues – issues that claims leaders now rank as the No. 1 barrier to successful claim outcomes, according to Rising Medical Solutions’ 2016 Workers’ Compensation Benchmarking Study survey. Psychosocial roadblocks drive up claim costs far more than catastrophic claims, mostly due to delayed recovery, and claims executives told us they occur regardless of the nature of injury. In other words, one cannot predict from medical data the presence of a psychosocial issue; one has to listen to the injured worker with a fresh mind. See also: Power of ‘Claims Advocacy’   It’s likely no coincidence that, while the industry has progressively paid more attention to psychosocial issues this past decade, there’s also been a shift toward advocacy-based claims models over adversarial, compliance- and task-based processing styles. Simply put, advocacy models – which treat the worker as a whole person – are better equipped to control or eliminate psychosocial factors during recovery. According to the 2016 Benchmarking Study survey, claims advocacy and greater training in communication and soft skills, like empathy, are associated with higher-performing claims organizations. Psychosocial – What It Is, What It Is Not The Hartford’s medical director, Dr. Marcos Iglesias, says that the “psych” part does not mean psychiatric issues, such as schizophrenia, personality disorders or major depressive disorders. Instead, he points out, “We are talking about behavioral issues, the way we think, feel and act. An example is fear of physical movement, as it may worsen one’s impairment or cause pain, or fear of judgment by coworkers.” The Hartford’s text mining has found the presence of “fear” in claim notes was predictive of poor outcomes. Similar findings were recently cited by both Lockton (“Leading with Empathy: How Data Analytics Uncovered Claimants’ Fears”) and the Workers’ Compensation Research Institute (“Predictors of Worker Outcomes”). Emotional distress, such as catastrophic reaction to pain and activity avoidance, is predictive of poor outcomes. Other conditions, behaviors and predicaments include obesity, hard feelings about coworkers, troubled home life, the lack of temporary modified work assignments, limited English proficiency and – most commonly noted – poor coping skills. Additionally, being out of work can lead to increased rates of smoking, alcohol abuse, illicit drug use, risky sexual behavior and suicide. When peeling back the psychosocial onion, one can see how adversarial, compliance- and task-driven claim styles are 1) ill-suited for addressing fears, beliefs, perceptions and poor coping skills and 2) less likely to effectively address these roadblocks due to the disruption they pose to workflows and task timelines. Screening and the One Big Question Albertsons, with more than 285,000 employees in retail food and related businesses, screens injured workers for psychosocial comorbidities. To ensure workers are comfortable and honest, the company enlists a third-party telephonic triage firm to perform screenings. “It’s voluntary and confidential in details, with only a summary score shared with claims adjusters and case managers,” says Denise Algire, the company’s director of risk initiatives and national medical director. At The Hartford, Iglesias says claims adjusters ask one very important question of the injured worker, “Jim, when do you expect to return to work?” Any answer of less than 10 days indicates that the worker has good coping skills and that the risk of delayed recovery is low. That kind of answer is a positive flag for timely recovery. If the worker answers with a longer duration, the adjuster explores why the worker believes recovery will be more difficult. For example, the injured worker may identify a barrier of which the adjuster is unaware: His car may have been totaled in an accident. This lack of transportation, and not the injury, may be the return-to-work barrier. It Takes a Village Trecia Sigle, Nationwide Insurance’s new associate vice president of workers’ compensation claims, is building a specialized team to address psychosocial roadblocks. Nationwide’s intake process will consist of a combination of manual scoring and predictive modeling, and then adjusters will refer certain workers to specialists with the “right skill set.” Albertsons invites screened injured workers to receive specialist intervention, usually performed by a network of psychologists who provide health coaching consistent with cognitive behavioral therapy (CBT) principles. This intervention method is short in duration and focuses on active problem-solving with the patient. The Hartford also transfers cases with important psychosocial issues to a specialist team, selected for their listening, empathy, communication skills and past claims experience. Emotional Intelligence – Can It Be Learned? Industry professionals are of mixed minds about how and if frontline claims adjusters can improve their interpersonal skills – sometimes called “emotional intelligence” – through training. These soft skills include customer service, communication, critical thinking, active listening and empathy. Experts interviewed agree that some claims adjusters have innately better soft skills. But they also concur that training and coaching can only enhance these skills among claims staff. See also: The 2 Types of Claims Managers   Pamela Highsmith-Johnson, national director of case management at CNA, says the insurer introduced a “trusted adviser” training program for all employees who come into contact with injured workers. Small groups use role-playing and share ideas. An online training component is also included. Advocacy – The Missing Link to Recovery Could it be that advocacy – treating the injured worker as a whole person and customer at the center of a claim – is the “missing link” for many existing claim practices to work, or work better? Whether for psychosocial issues or other barriers, organizations like The Hartford, Nationwide, CNA and Albertsons are paving the road to a more effective approach for overcoming pervasive barriers to recovery. Participants in the 2016 Workers’ Compensation Benchmarking Study confirm that higher-performing claims organizations are taking this road. The coming 2017 study will continue to survey claims leaders on advocacy topics. A copy of that report may be pre-ordered here.