Technology. Innovation. Even in 2016, when “technology” and “innovation” are oftentimes brushed aside as clichéd buzzwords, technology and innovation can still be daunting to many of us. Even more daunting is implementing technology in your insurance company to transform your claims and legal department. By breaking down innovation into the two simple steps described here, using technology to spur innovation becomes a lot less daunting. In fact, using the technology described here, you can achieve breakthrough improvements in performance while simultaneously decreasing expenses.

1. Document Automation

Automating legal documents is a simple way to use technology to improve processes and save money. Claims and legal executives at insurance companies know that pleadings and other legal documents full of the same old legalese are par for the course. No matter the case, claims and legal executives see the same pleadings containing the same content time and again. Nevertheless, attorneys continue to charge for each legal document, and insurance companies continue to pay for each legal document. Even more troubling, these documents typically have little to no impact on the pending litigation, and they are impossible to manage. The solution is legal document automation. Imagine if you had a robust library of hundreds of automated legal documents including pleadings, discovery, letters, notices and motions at your fingertips. These standardized forms allow for stricter quality control and instant access to top-shelf legal documents. Insurers do not pay for the same document twice, leading to huge financial savings. Perhaps most important, a software-based platform aligns with changes in strategy, case law and legislative change to ensure these are captured in every legal document. Taken together, automation allows insurers to take control of legal outcomes. Legal documents are the toolbox of every legal department and attorney handling a case. If you are not busy and are not trying to profit, go ahead and use a hammer and nail to litigate. But if you looking to innovate and transform your department, why not use a power drill?

2. Analytics

Insurance company executives handling claims and litigation have data. Lots and lots of data. Turning that data into intelligence is no easy task, but it is crucial in this rapidly changing insurance industry. You must move your business from yesterday’s hard data environment to today’s efficient virtual platform. Real-time intelligence, including descriptive and predictive analytics, will take your claims and legal department into the future. Descriptive Analytics Descriptive analytics answer one simple question: “What happened?” Using software to capture yesterday’s hard data, your claims and legal department can transform latent data into actionable descriptive analytics, allowing you to answer many of the important questions:

• When is the claims process most likely to break down?

• Which adjusters and engineers realize the least overall cost, including indemnity and expense?

• Which attorneys achieve the best combination of results and expenses?

• What are the emerging issues and how can we mitigate them?

Predictive Analytics Predictive analytics answer another simple question: “What might happen?” For example, predictive analytics could provide a range of the number of times an insurance company may be sued next year based on data trends from last year. Predictive analytics allow a claims and legal department to:

1. Allocate resources
2. Reserve
3. Produce effective and efficient settlement values
4. Identify potentially fraudulent claims
5. Identify potentially large losses
6. Manage expenses
7. Analyze emerging issue trends to aid the underwriting process

Technology can capture yesterday’s hard data and makes it searchable, sortable and reportable. Further, using a customized collaboration tool with the right fields accessible to the right users, you could automatically collect the most pertinent financial data in real time. This technology allows access to descriptive and predictive analytics and gives insurers the ability to evaluate expenses and outcomes on a real-time basis, as well as obtain efficient resolutions. By focusing on these two simple steps, insurers can turn claims and litigation expenses into valuable assets. Gone are the days of zero return on investment. Implement these two steps, and your litigation costs will produce countless opportunities to reduce expenses and write better business.

Research by Accenture on the extent of cyber risk suggests how carriers can steel themselves against threats to their IT and cyber security. Knowing your exposure is always critical. But the Accenture survey, Business Resilience in the Face of Cyber Risk, found just 5% of carriers run simulated attacks and system failures to test their systems’ resilience. Just more than half—52%—of insurance executives surveyed reported that their organizations have produced threat models for existing and planned business operations. Less than half of the executives—47%—map and prioritize security, operational and failure scenarios. And only 14% said they consistently design resilience parameters into the operational models and technology architectures. The survey also found that just a little more than one-third—38%—of executives "strongly agreed" that their organizations balance spending on iron-clad security measures and growth and innovation strategies. Some 49% "merely agreed," indicating there is room for improvement in this critical area. View the infographic that provides details of the insurance specific results. Accenture’s 2015 Global Risk Management Study: North American Insurance Report provides more insight on how insurers can better prevent IT failures and cyber security breaches. For example:

• 50% of respondents "strongly agreed" and 36% more "slightly agreed" that digital presents an opportunity to present the risk function as a business partner.
• 44% of North American respondents say that their risk management functions, to a great extent, have the necessary skills to understand cyber risk. While that level of confidence was nine points higher than among insurers elsewhere in the world, it demonstrates that the risk functions at more than half of North American insurers either do not have this expertise or have not demonstrated it.

We also suggest insurers consider:

• Embracing the digital ecosystem—Take advantage of digital capabilities and technologies outside of the enterprise to strengthen strategic decision-making.
• Managing digitally— Develop the ability to orchestrate, in real time, the myriad internal and external services required for a multi-speed business and IT.
• Institutionalizing resilience, because it is not a point-in-time initiative—Resilience must be part of the fundamental operating model, engrained into objectives, strategies, processes, technologies and the culture.

To learn more about the study, download Business Resilience in the Face of Cyber Risk (PDF).

More than a quarter of the world’s population owns a smartphone. In 2014, global mobile data traffic reached 2.5 billion gigabytes per month, a figure that is 30 times as large as all the traffic on the Internet for the full year 2000. No wonder global companies are moving rapidly to reshape their businesses to meet this new level of connectivity. One way they are doing so is by appointing a new kind of executive, the chief digital officer (CDO). The CDO’s mandate: to equip companies for the digital future. This executive has the dual task of developing an all-inclusive digital experience for customers and the internal capabilities needed to support that experience — while simultaneously managing the considerable investment required. The emergence of the new role to lead the organization’s digital efforts may in part be a reaction to the chronically weak relationships between CIOs and CMOs, which we’ve observed over the last few years. The number of companies that have hired CDOs remains small — just 6% globally, according to the results of the inaugural Strategy& study of digital leadership at 1,500 of the world’s largest companies. But the number is growing rapidly. Of the 86 CDOs we found, 31 were appointed in 2015. The sectors where the highest proportion of companies have CDOs are travel and tourism, with 31%; entertainment, media, and communications companies, with 13%; and food and beverage companies, with 11%. At the other end of the spectrum, only 1% of mining and metals companies had a CDO; just 2% of those in the automotive, machinery and engineering sectors did; and only 3% in technology and electronics did. One is also more likely to see CDOs in European companies than in their U.S. or Asian counterparts, and CDOs are more likely to appear in large companies than small ones. We suspect that in many cases where a CDO has not been appointed, it is because the related responsibilities are already distributed among other top management roles and are entrenched in all aspects of the company’s culture. In the past, traditional CIOs and CTOs were focused primarily on their companies’ IT, managing employee desktops and enterprise-wide ERP and CRM systems. The CDO role, although it varies from one company to another, is far more comprehensive. Besides customer experience, the development of digital features in new products and services and the relevant operational changes, the CDO may oversee changes in technical infrastructure and innovations in data collection and analysis. The CDO must also be an agent of cultural change, championing the digital transformation throughout the company and linking it to the development of the distinctive capabilities that form the basis of a company’s strategy. Here are glimpses of chief digital officers (or people in similar roles) at four major companies, and the ways in which they meet the challenge of digital transformation: --Jessica Federer is head of digital development at Bayer. “The data piece is actually the easiest,” she says. “Data is data. It’s the people piece that’s the challenge. So we focus first on the people in the organization, and how we connect across synergies, across silos, over platforms and data.” Soon after she was appointed, Federer created a digital council consisting of the CIOs and CMOs of the relevant divisions at Bayer. Their task was to look at potential synergies. She also fostered a huge network of people involved in some aspect of digital transformation, to which she gave the acronym NERD (Network for Enterprise Readiness and Digital). “They bring together digital marketing with digital product supply with digital R&D,” Federer says. “We used to do this in silos, but now we do it by sharing information.” --At Renault, CDO Patrick Hoffstetter is creating a centralized digital transformation organization, which he calls the Digital Factory. This is not a literal factory, but a metaphorical center for people throughout the company who already work on digital projects and another group working at about 65 outside suppliers. The factory is the nexus of communications about the digital strategy, and the place where resources and experts come to design the transition to what Hoffstetter calls “the connected employee.” The changes put into place at the Digital Factory will affect how people work, what they expect from the company and what tools they are given. Balancing the timetable for this complex shift is a key part of the CDO’s role. “One reason most operations in digital strategy and transformation are focused on sales and marketing is that these functions have a direct, quite short-term impact on the business,” Hoffstetter says. “Whereas when it comes to the evolution of internal processes, internal social networks, acceleration of collaborative tools and internal training, it’s much harder to show any payback, and it takes a lot longer.” --Corinne Avelines, CDO of the decorative paints division of the Dutch chemical company AkzoNobel, says broad support is critical: “Commitment at the top management level to innovation and digitization has made my job considerably easier,” she says. “Senior support is key to ensuring commitment to digital at the company, especially one of this size.” At the same time, she says, overall strategy must always drive decisions about how and what to digitize. “Gaining a competitive advantage in a fast-digitizing age is a challenge, so CDOs must understand their company’s current position and future strategy — what will make an impact on providing value to the customer — and focus on that. Worry about the other things later.” --Visa CDO Chris Curtin says that he has learned to participate actively in the creation of the overall business strategy — and lead the process when necessary. “I not only think that the best CDOs are reflective of the business,” he says, “I think that in many respects they are the business.” To that end, he believes that CDOs should “forget about digital. Forget about new media. The business objective has to permeate the thinking and the strategies and the go-to-market approach of the CDO and his and her team. Never make the means the end. A million followers on Twitter is just a means. The end is the business goal.” The CDOs interviewed for this study all emphasized the importance of working closely with every function of the business. Being part of top management gives them a critical strategic perspective, but they must also be given the power and support they need from functional groups. Otherwise, they may find themselves with a seat at the table but without the strategic and operational input that the digital transformation needs. Ultimately, the goal of every CDO is to ingrain the digital agenda so deeply and efficiently that it will become a way of life for everyone and every function in the organization, and a priority for every member of its C-suite. Sooner or later, companies may get to a point where a transformation isn’t necessary, because it has already happened. Digital technology will be so well-integrated that it won’t be a separate issue anymore. It will simply be part of the way people work, and the CDO will move to some new type of challenge. This article was written by:

Soon after the Soviet Union erected the Berlin Wall, President Kennedy uttered the now-famous words, "Ich bin ein Berliner" (I am a Berliner). Initially, this was a statement aimed at the Russians that was intended to show Western resolve. Over the years, however, it has become adopted by Berliners as a statement of individuality and freedom of expression. During a recent holiday in Berlin, I was reminded of this famous speech by the many historical sites I visited and the people I met. Berlin feels like a city of contradictions. Hard to categorize, it feels like an individual who refuses to be neatly pigeon-holed. Is that true for your customers, too? We have an armory of customer insight tools at our disposal these days (from various segmentation approaches, to predictive analytics delivering real-time personalized marketing content). It's, perhaps, too tempting to focus on what is possible, rather than what your customers actually value. How often do you find yourself thinking about your customers in terms of stable segments or predictable behaviors your models can "understand"? In Berlin, immersing yourself in the smorgasbord of sites, entertainment, food, drink and sheer variety of people is a great tonic for that simplification. It can also help dispel a number of misconceptions that Brits like me still have about our Anglo-Saxon cousins. Here are a few apparent contradictions that struck me:

• You can be fined for crossing the road before the "green man" is illuminated, and most people obey this rule. That plays right into my assumption that Germans are rigid rule followers, almost control freaks. But then, as you walk around Berlin, you find a widespread acceptance of graffiti everywhere. At first, it can seem scruffy and run-down, but it seems that people value this freedom of expression, this individuality.
• Berlin has many historical sites, beautiful museums and art galleries. Indeed, much of the information from the tourist office would lead you to expect that this classic, historical city is full of affluent middle-aged Germans and other tourists appreciating the many forms of culture the city has to offer. But, in my experience, 80% of those traveling in Berlin appear to be under 30. This is a youthful and vibrant city, with more nightlife and social venues than you could fit in your itinerary.
• The British are famous (perhaps infamous) for believing the Germans have no sense of humor. Much of the comedy I grew up watching, including Dad's Army, plays into such stereotypes. However, anyone attending a cabaret show called "The Wyld" will find an entertaining and hysterical cocktail of comedy, dance, circus acts and risqué performances that suits all orientations.
• Like us Brits, the Germans are not renowned for their cuisine. People could easily assume all people in Berlin eat is currywurst (which is tastier than I expected) and beer. But this most cosmopolitan of cities has quality cuisine from all over the world. I, personally, enjoyed the food at a Jamaican-European fusion restaurant that was better than any I've visited in the U.K.

So, what's my point for customer insight leaders (apart from recommending a vacation in Berlin if you haven't been)? I want to remind you to remember that your customers are individuals whose lives will be filled with apparent contradictions. Don't be surprised and discount research or analysis that appears to contradict what you think you already know about your customers. Rather, I'd encourage being open to insights about contradictory and changing customer wants and needs. How do you respond to this challenge? Have you managed to stay focused on the jobs your customers want to get done—without assuming you fully understand them? Have you embedded a test-and-learn norm in your marketing that keeps your approach fresh and flexible? Please do share your tips and tricks for avoiding stereotypes as well as any insight musings you have had from your holiday. I'd love to hear them.

Good communication is always a two-way street. Insurers that employ data scientists or partner with data science consulting firms often look at those experts much like one-way suppliers. Data science supplies the analytics; the business consumes the analytics. But as data science grows within the organization, most insurers find the relationship is less about one-sided data storytelling and more about the synergies that occur in data science and business conversations. We at Majesco don’t think it is overselling data science to say these conversations and relationships can have a monumental impact on the organization’s business direction. So, forward-thinking insurers will want to take some initiative in supporting both data scientists and business data users as they work to translate their efforts and needs for each other. In my last two blog posts, we walked through why effective data science storytelling matters, and we looked at how data scientists can improve data science storytelling in ways that will have a meaningful impact. In this last blog post of the series, we want to look more closely at the organization’s role in providing the personnel, tools and environment that will foster those conversations. Hiring, supporting and partnering Organizations should begin by attempting to hire and retain talented data scientists who are also strong communicators. They should be able to talk to their audience at different levels—very elementary levels for “newbies” and highly theoretical levels if their customers are other data scientists. Hiring a data scientist who only has a head for math or coding will not fulfill the business need for meaningful translation. Even data scientists who are proven communicators could benefit from access to in-house designers and copywriters for presentation material. Depending on the size of the insurer, a small data communication support staff could be built to include a member of in-house marketing, a developer who understands reports and dashboards and the data scientist(s). Just creating this production support team, however, may not be enough. The team members must work together to gain their own understanding. Designers, for example, will need to work closely with the analyst to get the story right for presentation materials. This kind of scenario works well if an organization is mass-producing models of a similar type. Smooth development and effective data translation will happen with experience. The goal is to keep data scientists doing what they do best—using less time on tasks that are outside of their domain—and giving data’s story its best possibility to make an impact. Many insurers aren’t yet large enough to employ or attract data scientists. A data science partner provides more than just added support. It supplies experience in marketing and risk modeling, experience in the details of analytic communications and a broad understanding of how many areas of the organization can be improved. Investing in data visualization tools Organizations will need to support their data scientists, not only with advanced statistical tools but with visualization tools. There are already many data mining tools on the market, but many of these are designed with outputs that serve a theoretical perspective, not necessarily a business perspective. For these, you’ll want to employ tools such as Tableau, Qlikview and YellowFin, which are all excellent data visualization tools that are key to business intelligence but are not central to advanced analytics. These tools are especially effective at showing how models can be used to improve the business using overlaid KPIs and statistical metrics. They can slice and dice the analytical populations of interest almost instantaneously. When it comes to data science storytelling, one tool normally will not tell the whole story. Story telling will require a variety of tools, depending on the various ideas the data scientist is trying to convey. To implement the data and model algorithms into a system the insurer already uses, a number of additional tools may be required. (These normally aren’t major investments.) In the near future, I think data mining/advanced analytics tools will morph into something able to contain more superior data visualization tools than are currently available. Insurers shouldn’t wait, however, to test and use the tools that are available today. Experience today will improve tomorrow’s business outcomes. Constructing the best environment Telling data’s story effectively may work best if the organization can foster a team management approach to data science. This kind of strategic team (different than the production team) would manage the traffic of coming and current data projects. It could include a data liaison from each department, a project manager assigned by IT to handle project flow and a business executive whose role is to make sure priority focus remains on areas of high business impact. Some of these ideas, and others, are dealt with in John Johansen’s recent blog series, Where’s the Real Home for Analytics? To quickly reap the rewards of the data team’s knowledge, a feedback vehicle should be in place. A communication loop will allow the business to comment on what is helpful in communication; what is not helpful; which areas are ripe for current focus; and which products, services and processes could use (or provide) data streams in the future. With the digital realm in a consistent state of fresh ideas and upheaval, an energetic data science team will have the opportunity to grow together, get more creative and brainstorm more effectively on how to connect analytics to business strategies. Equally important in these relationships is building adequate levels of trust. When the business not only understands the stories data scientists have translated for them but also trusts the sources and the scientists themselves, a vital shift has occurred. The value loop is complete, and the organization should become highly competitive. Above all, in discussing the needs and hurdles, do not lose the excitement of what is transpiring. An insurer’s thirst for data science and data’s increased availability is a positive thing. It means complex decisions are being made with greater clarity and better opportunities for success. As business users see results that are tied to the stories supplied by data science, its value will continue to grow. It will become a fixed pillar of organizational support. This article was written by Jane Turnbull, vice president - analytics for Majesco.

Just about every organization in every industry is rolling in data—and that means an abundance of opportunities to use that data to transform operations, improve performance and compete more effectively. "Big data" has caught the attention of many—and perhaps nowhere more than in the healthcare industry, which has volumes of fragmented data ready to be converted into more efficient operations, bending of the "cost curve" and better clinical outcomes. But, despite the big opportunities, for most healthcare organizations, big data thus far has been more of a big dilemma: What is it? And how exactly should we "do" it? Not surprisingly, we've talked to many healthcare organizations that recognize a compelling opportunity, want to do something and have even budgeted accordingly. But they can't seem to take the first step forward. Why is it so hard to move forward? First, most organizations lack a clear vision and direction around big data. There are several fundamental questions that healthcare firms must ask themselves, one being whether they consider data a core asset of the organization. If so, then what is the expected value of that asset, and how much will the company invest annually toward maintaining and refining that asset? Oftentimes, we see that, although the organization may believe that data is one of its core assets, in fact the company's actions and investments do not support that theory. So first and foremost, an organization must decide whether it is a "data company." Second is the matter of getting everyone on the same page. Big data projects are complex efforts that require involvement from various parties across an organization. Data necessary for analysis resides in various systems owned and maintained by disparate operating divisions within the organization. Moreover, the data is often not in the form required to draw insight and take action. It has to be accessed and then "cleansed"—and that requires cooperation from different people from different departments. Likely, that requires them to do something that is not part of their day jobs—without seeing any tangible benefit from contributing to the project until much later. The "what's in it for me" factor is practically nil for most such departments. Finally, perception can also be an issue. Big data projects often are lumped in with business intelligence and data warehouse projects. Most organizations, and especially healthcare organizations, have seen at least one business intelligence and data warehouse project fail. People understand the inherent value but remain skeptical and un-invested to make such a transformational initiative successful. Hence, many are reticent to commit too deeply until it's clear the organization is actually deriving tangible benefits from the data warehouse. A more manageable approach In our experience, healthcare organizations make more progress in tapping their data by starting with "small data"—that is, well-defined projects of a focused scope. Starting with a small scope and tackling a specific opportunity can be an effective way to generate quick results, demonstrate potential for an advanced analytics solution and win support for broader efforts down the road. One area particularly ripe for opportunity is population health. In a perfect world with a perfect data warehouse, there are infinite disease conditions to identify, stratify and intervene for to improve clinical outcomes. But it might take years to build and shape that perfect data warehouse and find the right predictive solution for each disease condition and comorbidity. A small-data project could demonstrate tangible results—and do so quickly. A small-data approach focuses on one condition—for example, behavioral health, an emerging area of concern and attention. Using a defined set of data, it allows you to study sources of cost and derive insights from which you can design and target a specific intervention for high-risk populations. Then, by measuring the return on the intervention program, you can demonstrate value of the small data solution; for example, savings of several million dollars over a one-year period. That, in turn, can help build a business case for taking action, possibly on a larger scale and gaining the support of other internal departments. While this approach helps build internal credibility, which addresses one of the biggest roadblocks to big data, it does have some limitations. There is a risk that initiating multiple independent small-data projects can create "siloed" efforts with little consistency and potential for fueling the organization's ultimate journey toward using big data. Such risks can be mitigated with intelligent and adaptive data architecture and a periodic evaluation of the portfolio of small-data solutions. Building the "sandbox" for small-data projects To get started, you need two things: 1) a potential opportunity to test and 2) tools and an environment that enable fast analysis and experimentation. It is important to understand quickly whether a potential solution has a promising business case, so that you can move quickly to implement it—or move on to something else without wasting further investment. If a business case exists, proceed to find a solution. Waiting to procure servers for analysis or for permission to use an existing data warehouse will cost valuable time and money. So that leaves two primary alternatives for supporting data analysis: leveraging Software-as-a-Service solutions such as Hadoop with in-house expertise, or partnering with an organization that provides a turnkey solution for establishing analytics capabilities within a couple of days. You'll then need a "sandbox" in which to "play" with those tools. The "sandbox" is an experimentation environment established outside of the organization's production systems and operations that facilitate analysis of an opportunity and testing of potential intervention solutions. In addition to the analysis tools, it also requires resources with the skills and availability to interpret the analysis, design solutions (e.g., a behavioral health intervention targeted to a specific group), implement the solution and measure the results. Then building solutions For building a small-data initiative, it is a good idea to keep a running list of potential business opportunities that may be ripe for cost-reduction or other benefits. Continuing our population health example, this might include areas as simple as finding and intervening for conditions that lead to the common flu and reduced employee productivity, to preventing pre-diabetics from becoming diabetics, to behavioral health. In particular, look at areas where there is no competing intervention solution already in the marketplace and where you believe you can be a unique solution provider. It is important to establish clear "success criteria" up front to guide quick "go" or "no-go" decisions about potential projects. These should not be specific to the particular small-data project opportunity but rather generic enough to apply across topics—as they become the principles guiding small data as a journey to broader analytics initiatives. Examples of success criteria might include: - Cost-reduction goals - Degree to which the initiative changes clinical outcomes - Ease of access to data - Ease of cleansing data so that it is in a form needed for analysis For example, you might have easy access to data, but it requires a lot of effort to "clean" it for analysis—so it isn't actually easy to use. Another important criterion is presence of operational know-how for turning insight into action that will create outcomes. For example, if you don't have behavioral health specialists who can call on high-risk patients and deliver the solution (or a partner that can provide those services), then there is little point in analyzing the issue to start with. There must be a high correlation between data, insight and application. Finally, you will need to consider the effort required to maintain a specific small-data solution over time. For instance, a new predictive model to help identify high-risk behavioral health patients or high-risk pregnancies. Will that require a lot of rework each year to adjust the risk model as more data becomes available? If so, that affects the solution's ease of use. Small-data solutions need to be dynamic and able to adjust easily to the market needs. Just do it Quick wins can accelerate progress toward realizing the benefits of big data. But realizing those quick wins requires the right focus—"small data"—and the right environment for making rapid decisions about when to move forward with a solution or when to abandon it and move on to something else. If in a month or two, you haven't produced a solution that is translating into tangible benefits, it is time to get out and try something else. A small-data approach requires some care and good governance, but it can be a much more effective way to make progress toward the end goal of leveraging big data for enterprise advantage. This article first appeared at Becker's Hospital Review.

Over the last two years, employers and groups that self-insure their workers’ compensation exposures have enjoyed reasonably favorable terms on their excess insurance policies. Both premiums and self-insured retentions (SIRs) have remained relatively stable since 2014. This trend is likely to continue through 2016, but the long-term outlook for this line of coverage is less promising. Changing loss trends, stagnant interest rates, deteriorating reinsurance results and challenging regulatory issues are likely to have a negative impact on excess workers’ compensation insurance in the near future. Predictions for 2016 Little direct information is available on the excess workers’ compensation marketplace even though written premiums well exceed $1 billion nationwide. Accurately forecasting changes in the marketplace is largely a function of the prevalent conditions of the workers’ compensation, reinsurance and financial marketplaces. But, based on available information, premium rates, retentions and policy limits should remain relatively flat on excess workers’ compensation policies for the balance of the 2016 calendar year. This projected stability is because of four main factors: positive results in the workers’ compensation industry over the last two years, availability of favorable terms in the reinsurance marketplace, an increase in the interest rate by the Federal Reserve at the end of 2015 and continued investment in value-added cost-containment services by excess carriers. For calendar year 2014, the National Council on Compensation Insurance (NCCI) reported a 98% combined ratio for the workers’ compensation industry nationwide. In 2015, the combined ratio is projected to have improved slightly to 96%. This equates to a 2% underwriting profit for 2014 and a projected 4% underwriting profit for 2015. This is the first time since 2006 that the industry has posted positive results. The results were further bolstered by a downward trend in lost-time claims across the country and improved investment returns. Reinsurance costs and availability play a significant role in the overall cost of excess workers’ compensation coverage. On an individual policy, reinsurance can make up 25% or more of the total cost. Excess workers’ compensation carriers, like most insurance carriers, purchase reinsurance coverages to spread risk and minimize volatility generated by catastrophic claims and adverse loss development. Reinsurers have benefited from underwriting gains and improved investment returns over the last three years. These results have helped to stabilize their costs and terms, which have directly benefited the excess workers’ compensation carriers and, ultimately, the policyholders that purchase excess coverage. According to NCCI, the workers’ compensation industry has only posted underwriting profits in four of the last 25 years. This includes the two most recent calendar years. To generate an ultimate net profit and for the industry to remain viable on a long-term basis, workers’ compensation carriers rely heavily on investment income to offset the losses in most policy years. For the first time since 2006, the Federal Reserve increased target fund rates at the end of 2015. Although the increase was marginal, it has a measurable impact on the long-term investment portfolios held by workers’ compensation and excess workers’ compensation carriers. Workers’ compensation has a very long lag between the time a claim occurs and the date it is ultimately closed. This lag time is known as a “tail.” The tail on an excess workers’ compensation policy year can be 15, 20 and even as much as 30 years. An additional 0.25% investment return on funds held in reserve over a 20-plus-year period can translate into significant additional revenue for a carrier. Excess workers’ compensation carriers have moved away from the traditional model of providing only commodity-based insurance coverage over the last 10 years. Most have instead developed various value-added cost-containment services that are provided within the cost of the excess policies they issue. Initially, these services were used to differentiate individual carriers from their competitors but have since evolved to have a meaningful impact on the cost of claims for both the policyholder and the carrier. These services include safety and loss control consultation to prevent claims from occurring, predictive analytics to help identify problematic claims for early intervention and benchmarking tools that help employers target specific areas for improvement. These value-added services not only reduce the frequency and severity of the claims experience for the policyholder, but excess carriers, as well. Long Term Challenges The results over the last two years have been relatively favorable for the workers’ compensation industry, but there are a number of long-term challenges and issues. These factors will likely lead to increasing premiums or increases in the self-insured retentions (SIRs) available under excess workers' compensation policies. Loss Trends: Workers’ compensation claims frequency, especially lost-time frequency, has steadily declined on a national level over the last 10 years, but the average cost of lost-time claims is increasing. These two diverging trends could ultimately result in a general increase in lost-time (indemnity) costs. Further, advances in medical technology, treatments and medications (especially opioids) are pushing the medical cost component of workers’ compensation claims higher, and, on average, medical costs make up 60% to 70% of most workers’ compensation claims. Interest Rates: While the Federal Reserve did increase interest rates by 0.25 percentage point in late December, many financial analysts say that further increases are unlikely in the foreseeable future. Ten- year T-bill rates have been steadily declining over the last 25 years, and the current 10-year Treasury rate remains at a historically low level. A lack of meaningful returns on long-term investments will necessitate future premium increases, likely coupled with increases in policy retentions to offset increasing losses in future years. Reinsurance: According to a recent study published by Ernst & Young, the property/casualty reinsurance marketplace has enjoyed three consecutive years of positive underwriting results, but each successive year since 2013 has produced a smaller underwriting profit than the last. In 2013, reinsurers generated a 3% underwriting profit followed by a 2% profit in 2014 and finally an underwriting profit of less than 1% in 2015. Like most insurance carriers, reinsurers utilize investment income to offset underwriting losses. As the long-term outlook for investments languishes, reinsurance carriers are likely to move their premiums and retentions upward to generate additional revenue, thus increasing the cost of underlying policies, including excess insurance. Regulatory Matters: Workers’ compensation rules and regulations are fairly well-established in most states, but a number of recent developments at the federal and state levels may hurt workers’ compensation programs nationwide. The federal government continues to seek cost-shifting options under the Affordable Care Act (ACA) to state workers’ compensation programs. Later this year, state Medicaid programs will be permitted to recover entire liability settlements from state workers’ compensation plans – as opposed to just the amount related to the medical portion of the settlement. At the state level, there are an increasing number of challenges to the “exclusive remedy” provision of most workers’ compensation systems. Florida’s Supreme Court is currently deliberating such a challenge. Should the court rule in favor of the plaintiffs, Florida employers could be exposed to increased litigation from injured workers. A ruling against exclusive remedy could possibly set precedent for plaintiff attorneys to bring similar litigation in other states. Lastly, allowing injured workers to seek remedies outside of the workers’ compensation system would strip carriers and employers of many cost-containment options.

Research has found that one in four car crashes is caused by phone-related distracted driving. However, a recent LifeSaver study of agents suggests this figure to be a vast understatement. More than 60% of agents responded that half or more of all claims are now related to distracted driving. It’s downright scary to think about the injuries, property damage and loss of life that results from distracted driving. If our survey bears out on a national scale, the full cost could be north of $60 billion a year. And, of course, this cost is passed on to drivers in the form of increased premiums. In fact, we’re already seeing some major insurers (GEICO, Allstate and Zurich) publicly conceding that they are feeling the pain from this fast-growing epidemic. Assuming the annual cost to insurance companies ranges from $30 billion (if one in four accidents stems from phone-related distracted driving) to $60 billion (using the numbers from our research), a mere 10% reduction in distracted driving accidents would save insurance carriers and their customers several billion dollars annually, in addition to saving lives and drastically reducing injuries. The infographic below highlights the cost of distracted driving to the insurance industry. It also offers some insight into the minds of insurance agents receiving these claims, as well as the habits of today’s distracted drivers. Take a look and let us know your thoughts in the comments below.

Globally, the World Economic Forum estimates that the planet is under-investing in infrastructure by as much as $1 trillion a year. Since 1990, for example, the global road network has expanded by 88%, but demand has increased by 218%. With the global population continuing to grow – and urban populations, in particular – the pressure on existing infrastructure is only set to worsen. And in the developed world, that infrastructure is creaking: In the U.K., 11 coal-fired power stations are nearing 50 years old, the end of their operational lives, and replacements have yet to be built; in the U.S., the average age of the country’s 84,000 dams is 52 years old; in Germany, a third of all rail bridges are more than 100 years old; parts of London’s Underground rail system, still in daily use by hundreds of thousands of commuters, run through tunnels that are more than 150 years old. According to the Report Card on America’s Infrastructure by the American Society of Civil Engineers (ASCE), the U.S. alone will need $3.6 trillion of infrastructure investment by 2020.  The report assigned near-failing grades to inland waterways and levees, and poor marks for the state of drinking water, dams, schools, road and hazardous waste infrastructure. Europe’s infrastructure is in worse shape. The Royal Institute of International Affairs has suggested that the continent needs $16 trillion of infrastructure investment by 2030, more than any other region in a world. Taxing Issues, Tragic Consequences While taxes once covered the cost of building and maintaining public infrastructure, entitlement programs such as Social Security and healthcare have started to claim a larger share of these funds as a percentage of government tax revenue, particularly as the number of people in retirement has expanded. In addition, as the cost of social programs grew, governments came under pressure to cut taxes, leaving even less money available to maintain existing infrastructure, let alone invest in the requirements of growing populations. “Too often infrastructure is seen only through the lens of cost, expenditure and not as core to society’s prosperity”, says Geoffrey Heekin, executive vice president and managing director, global construction and infrastructure, Aon Risk Solutions. “Since the 1950s, investment in infrastructure in developed countries has been declining,” he says. “In the U.S., for example, investment as a percentage of GDP has fallen from around 5% to 6% in the 1950s to around 2% today.” Tragically, train derailments, road closures, water main breaks and even bridge collapses have become commonplace. “Until situations like the water crisis in Flint or a bridge collapse happens, infrastructure does not hold proper weighting in the psyche of leaders in government,” Heekin says. This lack of attention to infrastructure is costing developed economies billions of dollars in lost productivity, jobs and competitiveness. Without addressing the infrastructure investment gap, the U.S. economy alone could lose $3.1 trillion in GDP by 2020, according to the ASCE, while one estimate attributes 14,000 U.S. highway deaths a year to poorly maintained road infrastructure. A Private Sector Solution to Public Sector Under-Investment? To begin reversing the infrastructure gap, it is likely that governments will need to find ways to encourage private sector investment toward replacing, renewing and upgrading physical infrastructure. Governments of all political stripes are increasingly supportive of private investment in infrastructure. One model that is now gaining attention is the Public Private Partnership (P3) model. P3s in one form or another have been used successfully in developed countries for several decades. They are being used to procure everything from public healthcare facilities, schools and courthouses to highways, port facilities and energy infrastructure. While the volume and type of P3 deal can vary widely by country, there continues to be an upward trend for the model’s use by the public sector. In 2015, for example, Canada procured 36% of its infrastructure with the P3 model. Aon Infrastructure Solutions anticipates that 21 P3 projects will close in Canada in 2016, with a total capital value of US$12.8 billion – the highest value of P3 projects in Canadian history. In the U.S., where adoption of the P3 model is less widespread, 11 projects are expected to close in 2016, with a capital value of US$8.7 billion. Like traditional design-bid-build procurement, P3 projects involve public authorities' putting public projects or programs up for competitive tender and selecting a preferred bidder from multiple consortia. The key difference is that the contractual structure in P3 allows the public authority to transfer a different set of risks to the private party – including (but not always) the financing for the project. The arrangement can allow the private partner that designs, builds and finances construction of the asset to operate and maintain it in return for either a share of the revenue generated by the use of the asset, or a stream of constant payments from the public authority (also called availability payments). Keeping Focused on the Big Picture “The public sector benefits from P3 delivery when the model is applied to a project that meets a community need and is procured through a transparent, accountable process,” says Gordon Paul – senior vice president, Aon Risk Solutions and member of Aon Canada’s Construction Services Group executive committee and Aon’s  global PPP Centre of Excellence. “Public authorities seek ‘value for money’ in a P3 project by looking to the long-term value,” Paul says. This means identifying whether the private sector party is able to design, build, finance, operate and maintain an infrastructure project for a price lower than if the public authority did it on its own over the same period. It’s about the full lifecycle of the project – not just the building costs. Taking a big picture view is equally important for the private sector party, says Alister Burley, head of construction for Aon Risk Services Australia. He points to the importance of taking a holistic view to P3 projects and investments to enable efficiencies to be built that will carry forward. If done right, P3 arrangements can be a significant benefit to both the public and private sectors. Public bodies gain a much-needed boost to their infrastructure, often with long-term maintenance included in the deal, reducing the potential negative economic and health consequences of infrastructure failure. And private investors can secure a stable, long-term return through a stake in some of the underlying essentials of our economies. Whatever route governments take to secure the integrity of our underlying infrastructure, one thing is clear – without a significant increase in infrastructure investment over the coming years, the world’s economy and health could well be put at further risk.

See the full infographic here.