At a time when innovators are trying to start with customers—not with our old ways of doing business—and work backward to what products, processes and systems should be, J.D. Power reports that the customer experience with auto insurers has made marked progress. Satisfaction with auto insurers has actually risen even though prices have been climbing steadily.

What's the secret?

J.D. Power cites increased digital interaction with customers, especially for monthly billing. The firm says: "Customer satisfaction is at its highest when customers take care of transactions themselves and save the high-value interactions for live channels." The firm says that 73% of those customers surveyed said they wanted to verify payment receipt digitally, that 70% wanted to pay digitally and that 66% wanted to order proof of insurance cards digitally.

Underscoring the interest in more digital interactions, J.D. Power says that 10% of those surveyed said they participated in usage-based insurance programs, up from 8% in the surveys last year and the year before.

In general, the firm says customers credit auto insurers with being better able to interact via multiple channels, ranging from a face-to-face meeting with an agent to a fully digital transaction executed directly with the insurer, and appreciate the "omni-channel" approach.

The firm concludes: "The auto insurers that increase customer satisfaction across all facets of the customer experience make price just one part of the overall relationship.” (The full summary is here: http://www.jdpower.com/press-releases/jd-power-2018-us-auto-insurance-study)

My take:

The point about self-service is key. It may seem counterintuitive that customers want you to do less and them to do more for themselves, but, let's face it, companies aren't much fun to deal with. Customers are told they need to provide their account number, understand many things about your processes, correct errors that companies make in data entry, listen to bad music or obnoxious sales pitches if they've called in and are on hold, etc. Who needs it? Customers in all industries have consistently shown that they'd rather handle interactions digitally while sitting in front of the TV or listening to music. So, help your customers help you by having them take as much work as possible off your plate.

J.D. Power sounds a bit too optimistic to me both about how much progress auto insurers have made and about how much more loyal customers will be despite rising prices. It's still tough out there, and insurers have a long way to go.

But progress is progress, and we should all celebrate gains when we see them.

Have a great week.

Paul Carroll
Editor-in-Chief

Based on the trend toward value-based health insurance and reimbursement, health benefit plans are being designed to reduce barriers to maintaining and improving health and to promote higher-value healthcare services. Value-based reimbursement requires providers to track and report a host of adverse events and population health measures, including biometrics, patient engagement and other measures required to demonstrate quality performance. Unlike the traditional fee-for-service model, value-based care attempts to align incentives of providers to deliver the right care in the right setting in lieu of maximizing the revenue of each encounter by delivering more services. Providers receive incentives to use standardized, evidence-based medicine, engage patients, upgrade health IT and use more advanced data analytics to optimize their clinical and financial performance. When patients receive more coordinated, appropriate and effective care, providers are rewarded. Accessing Care Quality and Safety Data Plan sponsors and their benefits consultants or brokers who advise them need access to information about care cost, along with the quality and safety performance of those hospitals and physicians delivering care to their plan members. See also: Taming of the Skew in Healthcare Data   Quality measures are essential in optimizing the benefits of value-based models for all stakeholders. Success for all stakeholders depends upon how well healthcare providers can manage quality of care within tighter financial parameters. This presents an opportunity for benefits consultants and brokers who are well-positioned to act as trusted advisers in educating and defining how best to design benefits plans to optimize for value. As educators and advocates, they can guide plan sponsors toward partners who will help them evaluate provider quality and safety. Research shows that many U.S. employers that offer health insurance to employees are unfamiliar with objective metrics of health plan quality information. This gives benefits consultants and brokers an opportunity to outline the advantages of evaluating hospital quality to ensure that plan designs and benefits options include only high-quality hospitals and physicians who provide services at the lowest costs and encourage plan members through incentives to avail themselves to this narrower group of providers. The Challenge: Hospital Ranking Variability The significant challenge is the prevalence of numerous hospital quality rating methodologies. Even the slightest differences in adjustment methodology, data source, time period and inclusion/exclusion rules can produce differences in the hospital or physician ratings. This variation makes it more difficult for hospitals and physicians to prioritize and improve the quality of care delivered. For instance, hospital ranking organizations, such as U.S. News & World Report, Healthgrades, Centers for Medicare and Medicaid Services (CMS) and Leapfrog, reflect substantially different results, fostering confusion to those less literate in healthcare analytics. In 2016, CMS gave 102 hospitals its top rating of five stars, but only a few of those were considered as the nation’s best by private ratings sources such as U.S. News & World Report or viewed as the most elite within the medical profession. First-tier academic journals like JAMA expressed deep concern about the lack of academic credibility in the methods used to assess performance and aggregate the conclusions into a single rating across many different measures. Plan sponsors and their benefits consultants or brokers must educate themselves on assessing provider quality. While there is a myriad of rating services, many do not include elements essential to a precise and comprehensive assessment of providers. See also: Healthcare Data: The Art and the Science   Ratings approaches that use reputation or self-reported data should be considered less reliable than objective outcomes measures using patient level claims data. Additionally, hospital overall surveys or patient reported outcomes do not offer a valid basis for comparison. It is also not possible to use a single outcome measure – for example, risk-adjusted mortality -- as a proxy for all outcomes like complications or readmissions because provider performance varies widely across measures. For a comprehensive assessment, all available measures should be incorporated for a specific clinical category. Lastly, aggregating outcomes data into composite scores must be scientifically sound. As more employers seek greater value for their healthcare dollars, and as benefits consultants and brokers continue to pursue opportunities to help them reduce the upward cost spiral, quality ratings are an important first step toward realizing these goals and advancing the quest for improved employee health.

One of the outcomes of economic and technological changes has been the rise of on-demand insurance products, offered both by insurtech startups and incumbents alike. This includes products with continuous underwriting attributes, microinsurance products and insurance offerings for workers in the gig economy. These offerings aren’t typically grouped together, but they share an on-demand aspect that wasn’t required or technologically possible in the past. Continuous underwriting refers to the use of regularly updated (and possibly real-time) policyholder data to rapidly determine consumer risk and adjust policy terms and prices accordingly, as opposed to traditional term-based updates and renewals. Some forms of continuous underwriting have been around for a long time (example: pay as you go Workers’ Comp, with monthly updates based on submitted payroll) but now has applications to many lines. Microinsurance refers to coverage of smaller risks via rapid underwriting; including on-demand products like travel or event insurance, renters’ insurance broken out for specific high-value household items or pay-per-mile auto coverage. Gig economy insurance is most familiar to those outside the insurance space: as more and more freelance and “gig” opportunities like Uber and Postmates emerge, carriers are developing products to keep these independent contractors covered in a part-personal, part-commercial hybrid coverage. See also: On-Demand Insurance: What’s at Stake   While these three arenas of modern insurance might seem disparate in their final forms, they are emerging today due to a new consumer-focused approach to product definition and the connected technology necessary to allow a real-time approach. This foundation for all of them is built on three pillars: Data: On-demand insurance requires data, if not in real time then something close to it. If insurers are only getting updates as to policyholder risks and scheduled items after an end-of-term audit, then only a traditional approach will work. But as connected technologies and the Internet of Things have created a continuing pipeline of data, a new approach emerges. Insurers now have the ability to tap into discrete data points about coverages times and risks in an automated fashion, including: When is someone driving their car for Uber vs. for personal use? When is a business stocking high amounts of valuable goods? What is monthly payroll for workers’ comp? Product: It’s not enough to have access to the data. Insurers can’t just adjust rates on the fly. Instead, they need to take a consumer-first approach to modeling their insurance product. This means the restructuring and sale of a product with a variable pricing agreement and a flexible term. Done properly, this will allow the insurer to have the most insight into the collective risk and allow the consumer to have a transparent product that covers them for exactly what they need when they need it. Systems: Just because the data is available and the business has rethought the product structures doesn’t mean the infrastructure will be able to support it. On-demand products mean real-time web service calls and at least some component of automated underwriting decisions. Variable rates mean a rating engine that can calculate new rates on the fly based on updated risk info as well as a billing system that can adapt to variable billing amounts and dates. Without flexible and agile core systems, an insurer can’t roll out new products that behave in nontraditional ways. Insurers may be able to make progress with an on-demand offering even if they only have one or two of these pillars. Workers’ comp insurers, for example, have offered pay-as-you-go for a long time via manual form submission. But to make new products viable for a mass audience—and to compete with the consumer-driven ethos of Silicon Valley startups—automated data needs to be simple and convenient to turn on and off. This might take the form of a mobile app with a button to turn a microinsurance product on or off or perhaps the form of an automated data feed to a third-party system like payroll. Conversely, all three pillars are valuable to an insurer even if it hasn’t fully embraced an on-demand approach to their products. See also: Reinsurance: Dying… or in a Golden Age? Real-time data allows an insurer to understand its overall risk profile at any given moment and to make decisions and new sales and renewals. If, for example, you are selling a commercial liability policy and have up-to-date info about a business’ risks, it’s helpful even if individual policy pricing isn’t affected. In fact, this is how automotive telematics typically works: Auto insurers are gathering masses of data that demonstrates real-time risk and driving behavior, but they aren’t using it to do continuous underwriting/rating. Likewise, rethinking a product structure to take a more consumer-focused approach can happen even within the constraints of traditional insurance offerings or without real-time data. And, obviously, having modern and flexible core systems allows new product rollouts, better automation and digital interactions regardless of what products are sold. New insurance products like microinsurance and continuous underwriting aren’t just about gathering data or having a modern core system. Rather, they are based on a multi-faceted approach: understanding risk in a semi-real-time way; selling a different type of product; and having the core systems to handle it.

Wearables continue to be the hottest topic in smart technology, because of gadgets like Fitbits, Apple Watches and Nike Fuelbands. But what about a wearable that uses big data to revolutionize workplace safety? In a world where almost 1,000 workers don’t come home each day due to workplace injury, understanding how workplace incidents happen and taking steps to prevent future injuries should be a company’s top priority. Insurers want to provide the most efficient workers’ compensations and P/C policies, and now they can from the data and machine learning of wearables. Wearables are providing efficiencies in gathering data that can then be processed to provide insights for workplace injury trends. Automated collection of individualized worker safety data at scale is far more efficient than the traditional observation techniques used by safety experts to collect risk data. Wearables don’t require employees to log information or have their cell phone constantly handy, and they offer a seamless information transfer between users, especially important in industries with high employee turnover rates. At MākuSafe, we’re developing a wearable solution that collects and tracks environmental data, which is processed through MākuSmart, our cloud-based machine learning platform, to help manufacturing facilities build a culture of safety. See also: Workplace Wearables — Now What?   So, we understand that wearables are essential for the safety management of an organization. But wearables can provide data just as valuable to insurance carriers. Manufacturing companies and warehouses across the world are losing time and money on avoidable safety hazards and compensation. Data from workplace wearables creates remediation steps to help streamline reducing worksite risk and allow carriers to generate tailored advice for policies and more efficiently justify premiums. IoT capabilities fill this picture in even further, with the ability to alert safety managers to potential risks or even take automated steps to help mitigate risks based on identified trends. With insurance companies often only having limited visibility into the risks policy holders’ workers are experiencing, IoT devices give risk reduction professionals the eyes and ears they need to understand what environmental conditions could be contributing to worker hazards. That means quicker intervention when data shows leading indicators of risk are present, instead of waiting for an injury or claim. Armed with this more complete picture of workplace risk, thanks to more accurate and precise trend data, insurance carriers can target, select and price risk more specifically for policyholders and accelerate time to value on policies. The individualized view of risk permits safety and risk mitigation experts to precisely prescribe remediation steps that are specific to worker risks and better measure the remediation efficacy. None of this data is biometric—rather, workplace wearables like the one from MākuSafe track the environment around an employee, not from the employee. It is intended to generate a 360-degree view of a worker’s risk exposure. Through data analytics and machine learning, wearables can transform from an informative personal health-monitoring device to an essential workplace data tool, without invading employee privacy. See also: The Case for Connected Wearables   The predictive value of individualized workplace safety data can clearly expose risks before they turn into an injury. With this in mind, insurance companies should be looking for companies like MākuSafe to provide solutions for their manufacturing clients, while warehouses and manufacturing companies should be jumping at the chance to test these money/time/life-saving devices. By building a strong partnership between data-driven intelligence, workers and the resources that can be deployed by insurance companies and other safety providers, workplace risks can be reduced and, ultimately, more workers will make it home safely to their friends and families each day.

Warren Buffett’s caution about underwriting cyber-insurance put the spotlight on one of the big challenges facing carriers today – how to address a slew of new insurance risks. The Oracle of Omaha told shareholders at the Berkshire Hathaway annual meeting that he didn’t want the group’s insurance business to pioneer cyber-cover because the risks were largely unknown and potentially too big. Berkshire Hathaway might write some cyber-policies to stay competitive, Buffett added, but it would not be among the top three providers in this market. Underwriting complex new risks such as cyber-insurance, as well as meeting the rising demand for cover for other risk-heavy occurrences such as natural catastrophes and corporate fraud, promises substantial revenue for carriers. Global premium revenues for cyber-insurance, for example, could hit $7.5 billion by 2020, according to researcher Statista. Cover related to digital products and services could also yield healthy additional income. The new revenue streams are welcome news for many insurers that have watched income from traditional products plateau in the past few years. However, as Buffett points out, venturing into uncharted territory can be hazardous -- especially when we don’t know the scope of the hazards. Catastrophe cover, for example, which must now contend with uncertainty related to climate change, cost U.S. insurers dearly last year. The effects of three major hurricanes, Harvey, Irma and Maria, as well as the extensive wildfires in California, all contributed to a spike in underwriting losses. The net underwriting deficit among U.S. property and casualty insurers leaped from $4.7 billion in 2016 to $23.2 billion the following year, according to a report compiled by research firm ISO and the Property Casualty Insurers Association. Insurers are not only being forced to make calls on new types of risk. They must also handle the growing complexity of the underwriting required for some of their established offerings. The spread of corporate ecosystems and supply chains across many varied countries, for example, has heightened the complexity of commercial risk assessment. So, too, has the rise in trade and business regulations imposed by governments around the world. What’s more, insurers must also accommodate a flood of new data streams. While these additional sources of data provide valuable insight into commercial risks and consumer behavior, they also compound the complexity of insurers’ underwriting systems and processes. To meet the rising challenge of new and more complex underwriting requirements, insurers need to get a lot smarter. Improving workers’ skills and hiring more talent won’t be enough. Insurers need to deploy intelligent technology. Only by using artificial intelligence (AI) will underwriters be able to manage the new, complex risks that are confronting them. Our research shows that more than 75% of insurers plan to use AI to automate tasks in the next three years. Many of these applications are intended to improve efficiency and productivity. The big gains in AI, however, are likely to be achieved by using this technology to improve decision-making. In my next blog post, I’ll discuss how advances in AI can help underwriters make smarter, quicker decisions. Until then, have a look at these links. I think you’ll find them useful.

• Accenture Technology Vision for Insurance 2018
• Realizing the full value of AI
• Model behavior. Nothing artificial.

Artificial intelligence is advancing quickly and customer service technology along with it. More and more companies are choosing to assist customers with the AI equivalent: chatbots. Chatbots are attractive to companies for obvious reasons. They are significantly cheaper than their human counterparts and are available 24/7. After all, call center employees are "only human.” But how do consumers feel about this rapid change in customer service? Could the switch from humans to chatbots test the loyalty of current customers, or repel the interest of potential customers? How much chatbot is too much for the typical consumer? Current research has found that about half the population prefers talking to a human when seeking customer service. We can rightly conclude that people are open to the chatbot transition, but how can we cater to the full population? How can companies pick up the slack where chatbots are falling short, to make them more appealing to customers as the default? Where Chatbots Are Failing Customers The transition to chatbot ubiquity is already well underway. AI applications that give us sales recommendations and perform insurance underwriting, as well as Apple's Siri and Amazon's Alexa, are already a part of daily life for many people. In fact, Gartner has predicted that chatbots will power 85% of all customer service interactions by the year 2020, contributing to billions in savings for companies. But how do companies win over the half of the population that can’t be served by chatbots? One recent survey found that half of customers thought that chatbots wouldn't be able to correctly identify what they were looking for when they called in with a question. See also: Chatbots and the Future of Interaction   One Forbes article, "Will AI Replace Humans in the Customer Service Industry?" placed customer needs on a spectrum of emotion and urgency. If a customer feels that something very important is at stake, or is unhappy with the service provided, the customer wants to be understood by someone showing empathy, something that chatbots can't provide. Bridging the Emotional Gap Chatbots may not be the definitive answer for improving the customer experience. That doesn’t mean that companies should abandon chatbots, but they should not be positioned as a “catch-all solution” and require a proper fallback to a real conversation where a company representative can help the customer. It’s important that companies bridge the gap between chatbots and humans. Companies will require “visual engagement technology” that will really help them understand their customer problems and allow them to help their customers in a collaborative way. One such technology that many companies are using to understand their customers’ needs, connect emotionally and increase trust during customer service interactions is co-browsing. Co-browsing enables agents to remotely assist customers in real time. In the case of customer support, the agent can co-browse simultaneously with customers who need assistance on any web application. Co-browsing allows the agent to see what the customer sees and guide the customer through complex forms in real time. This helps to reduce frustration and friction during high-value purchases. See also: Chatbots and Agents: The Dynamic Duo Consider this scenario: Let’s say a customer is driving on a roadway when his car strikes a large object. He realizes that his car was damaged. The customer doesn’t know if his insurance policy will cover the damage. If a chatbot cannot resolve a customer’s issue or the chatbot notices frustration, the chatbot interaction can be upgraded to a co-browsing session with a human in seconds. This will allow agent to quickly diagnose the issue and guide the customer smoothly through complex claims forms. When an agent hops on a co-browsing session with a customer, the agent gains the right insights to deliver contextual support. This reduces the time it takes the agent to diagnose the issue, resulting in lower handle time and ensuring customer satisfaction. Conclusion Lack of empathy is really at the heart of skepticism surrounding chatbots. Companies fail to embrace chatbots, because they focus too much on the technology and don’t clearly define their purpose. Research shows that customers are comfortable using chatbots if they feel that they will receive trusted support. Companies looking to personalize their customer experience must understand both the benefits and limits of chatbots. They require a lot of resources. Having a successful AI customer service program depends on having a blended approach. Humans will always play a role in the optimization of chatbots. Consider using visual engagement technology to ensure that customers with high-emotion scenarios will be met with human empathy and understanding.

Artificial intelligence has opened up a multitude of transformative opportunities for insurers to leverage within nearly every part of the value chain. This includes everything from risk management and fraud prevention to the development of new personalized products and enhancing customer service. Machine learning still has a long way to go before enabling the capabilities of Star Wars’ gangly droid C-3PO. Even in machine learning's current form, there are adoption challenges that prevent some insurers from moving forward with business initiatives based on AI. Insurers must recognize these challenges and address them head on to start taking advantage of the technology. Top Machine Learning and AI Challenges Currently, many insurers hesitate to move forward with AI and machine learning initiatives because of potential job losses, data management and limited time and skilled resources. Job Replacement. A significant percentage of an insurer’s investment and cost is staff. As the insurance industry continues to adopt more AI solutions, there is a valid fear among insurers that the livelihood of their agents, underwriters and other professionals is at stake. While commercial AI is not advanced enough to replace humans altogether, it can be a valuable tool today to enable and enhance humans. In fact, the AI solutions being built should have the perspective – “How to get 1+1 = 3?”, combining human capital with AI solutions. This can be observed in the use of intelligent chatbots. With NLP (natural language processing), machine learning and integration with back-end services, chatbots can be a great complement to a human agent. The chabot can provide insights to the agent for a more contextualized conversation with the customer. This allows the agent to deliver an empathetical and enhanced customer experience. See also: 4 Ways Machine Learning Can Help AI solutions should be viewed as opportunities to think outside the box to offer customer-centric solutions and not just to replace a current process with an automated AI solution. Data Management. Digitization and automation will create significantly greater amounts of data, which are necessary for a successful ML solution. The key, however, lies in the quality of data – whether one-time events or a continuous stream. The insurance industry is no stranger to using large volumes of data in developing insurance products, establishing premiums and better managing risks. To have a successful machine learning solution, insurers must combine traditional expertise with data management processes and harness the power of mature products that manage and cleanse data. Limited Time and Skilled Resources. Today’s insurers are working with full plates. As priorities often compete for time and resources, it is difficult to pick and choose from equally essential initiatives. While many are aware of the benefits that machine learning can bring to the table, insurers continue to grapple with the time, personnel and tight budgets to implement these new technologies. The other challenge is access to skilled resources who could implement AI/ML solutions. Unfortunately, these challenges create a “wait and see” attitude, pushing insurers further behind other industries and competitors that act to secure the first mover advantage. To take advantage of this new technology now versus later, insurers are partnering with innovative Business Process as a Service (BPaaS) firms that have made ML their focus to stay at the forefront of technology and innovations. Apart from leveraging the capabilities from the BPaaS and their partner ecosystem, this approach allows the insurers to free management and technical resources to focus on AI/ML Initiatives. See also: Strategist’s Guide to Artificial Intelligence   Conclusion The AI and ML technologies are mature enough and accessible for insurers. However, it is essential to view these technologies as enablers of new business capabilities and opportunities that might not exist today. For insurers to future-proof the way they do business and remain competitive, they must address these challenges and leverage existing foundational data management capabilities or BPaaS relationships to deliver customer-centric solutions.

Even a casual look into the history of insurance reveals its rapid evolution over the last decade - from a slow-paced and highly regulated industry to one consumed with technology transformation. Until recently, insurers have grappled with challenges of engaging millennials, managing investments, simplifying systems, improving combined ratios and driving growth. Today, however, a slew of disruptive forces are changing the playing field. The availability of new user experiences for policy holders, coinciding with the sprouting of insurtech, has created asymmetric competition for established carriers. Legacy products designed decades ago are unable to support the deluge of new data, while millennials are ride-sharing and buying fewer cars. Technologies such as connected health, homes and autonomous vehicles are forcing traditional insurers to re-invent offerings at an unprecedented pace. See also: 10 Essential Actions for Digital Success   Market reports reveal that over the past few years technology spending for insurers is higher than the market growth rates, signaling a shift to technology-led-models. The UK FinTech sector alone hopes to create 100,000 jobs and seed $8 billion in investments by 2020. In view of these developments, speed-to-launch will become a real differentiator. Carriers strive to launch products in three to four months to stay abreast of customer demand. They also need to accelerate the integration of enterprise risk management into decision-making for these emerging products. Data monetization and customer-centricity will become key imperatives while lights-on cost continues to be sucked out of legacy platforms. Using technology to re-invent insurance In the face of myriad transformation alternatives and confusing consultant-speak, choosing the right path can be tedious. To address this challenge, I suggest a three-dimensional approach that maps outcomes to technologies. I strongly believe that this framework will empower insurance organizations in making informed decisions on how technology can drive future growth. A2C: Artificial intelligence (AI), Automation and Cloud — This category comprises new and emerging technologies that help insurers improve efficiency, reduce cost and scale easily. For instance, the adoption of cloud platforms and agile infrastructure continues to be a hot trend among insurance providers given the radical performance advantages. Automation is helping organizations achieve huge cost benefits and efficiency improvements by automating repetitive processes and eliminating the risk of human error. I find that robotic process automation (RPA) or software robots are best-suited for back-office insurance processes such as claims processing, billing reconciliation and subrogation. While adoption of machine learning is still nascent in the industry, companies are beginning to deploy chatbots for front-end processes. For instance, ICICI Lombard has developed a chatbot, MyRA, that engages with customers to sell policies and execute transactions without human intervention. D3C: Design, Digitization, Data and Consulting — This category comprises mature technologies that help insurance companies accelerate revenue growth. In my opinion, as demand for intuitive policies rises, product innovation will become a key differentiator for insurers. Carriers must listen closely to what their customers are saying and develop products that meet their needs. Here, Design thinking can be a vital tool for product rethink that meets the key criteria of desirability, feasibility and viability. When design thinking is coupled with digitization, companies can access advanced ways of improving efficiency and tracking customer sentiment. Analyzing such customer feedback provides valuable insights into how insurers should revamp user interfaces to deliver delightful customer and user experiences. Digitization also supports insurers in providing self-service dashboards and omni-channel capabilities for customers to interact with their providers, thereby increasing customer stickiness. However, any initiative involving digital or design thinking must be reinforced with a strong data strategy. This is why I highlight the importance of investing in intelligent systems that collate unstructured and structured data to gain a holistic customer view. Such solutions enable extreme product and service personalization such as usage-based policies, customized pricing and claims validation across auto, life and home insurance. Consider how Ford is partnering with IVOX to develop a technology that gives insurers insights into driver performance, to lower premiums. Finally, such innovation requires robust partner ecosystems, underscoring the need for strong consulting services. Seamless collaboration across partners is critical if design, digital, data and consulting are to generate tangible value. CoLT: Core systems, Legacy systems and Total outsourcing — Over the years, while some insurers have built robust albeit monolithic enterprise applications, others have grown through mergers and acquisitions. Both now have an intricate web of IT infrastructure and legacy systems. Managing these inherited systems is a cost that insurers are forced to bear. McKinsey estimates that handling this complexity accounts for 75% of the operational and IT costs when it comes to servicing policies. Not surprisingly, many insurers choose outsourcing as a solution because it makes the bloat appear low. Third-party service providers are better equipped with the skills and infrastructure as well as the agility to adopt innovative technologies. Further, insurers will need to reinvent existing systems to meet increasing customer demand for better services and products. This can be a heavy burden on organizational budgets, particularly when dealing with legacy core systems. This is a key concern as stricter data security laws increase the liability for penalties. I strongly feel this is where leading technology service providers can demonstrate their expertise. Best-in-class technology solutions can help insurers modernize their legacy systems at lower cost to improve efficiency and performance. Additionally, intuitive solutions allow insurers to on-board new technologies and enjoy sophisticated digital capabilities while reducing total cost of ownership (TCO). See also: Seeing Through Digital Glasses   Thus, technology service providers seeking to provide real business value to insurance organizations must design solutions that deliver innovation in the above three categories. Application modernization, cloud computing, automation and other new technologies will help insurers optimize their core systems, develop customer-centric insurance products and streamline underwriting and risk management. Such capabilities will empower insurance companies to build and sustain competitive edge in the digital age.

Out here in Northern California, the hot debate over the past few days has been a luxury: whether the NBA Finals MVP should have been the spectacular Kevin Durant or the spectacular Steph Curry. (I'm Team Steph but have no problem with Team KD—or even with those who say Golden State Warriors fans are hopelessly spoiled after three titles in four years and should just stop talking.) But there's a conversation to be had about a more consequential MVP, and Dan Bricklin is just the guy to introduce it.

In this case, MVP stands for Minimum Viable Product. The concept has been the rage in innovation circles for a few years now, helping companies see the need to push a product into the market quickly, even though not fully baked, to gauge how real customers react in real situations and to adapt quickly. Historically, many companies have finetuned products so much that they have been late to market, only to find that what they're offering doesn't really match what customers want.

Now that MVPs have been in vogue for a while, Dan pointed me to some thoughts from a colleague on how to improve on them. Dan is always right on such issues. He invented the electronic spreadsheet back in the '70s as a Harvard Business School student bored of having to constantly recalculate so many cells in a paper spreadsheet every time a variable changed, and I've seen him be smart on a whole range of subject over the 30-plus years I've known him. 

The finetuning, suggested by Dan's colleague Jensen Harris, consists of four points:

• For many products, you can't go "minimum" on the user interface. The customer experience is often key to success or failure, so make sure people get a feel for the soul of your product.
• Do ship bad code. You'll have plenty of time to fix it if you strike a chord with customers. Don't waste time making it pretty now.
• Be absolutely sure about what you want to learn by shipping your MVP quickly, and make sure you can measure what you're testing. Also be sure ahead of time that you can act on what you learn.
• Realize that MVPs aren't the only way to go. Sometimes, an innovative idea is so complex that you have to tackle the whole thing at once, not in parts where continual experimentation can occur.

If you want to see the full set of Jensen's thoughts, click here: https://twitter.com/jensenharris/status/1001662472305106944. 

Have a great week.

Paul B. Carroll
Editor-in-Chief

The combination of professional administration with a structured settlement (annuity) is often the best way to protect an injured party’s settlement dollars in the event of an unexpectedly very costly year due to higher-than-anticipated medical needs after settlement. The combination of these services in a costly scenario allows the injured party to access more coverage from Medicare and pay less out of their own pocket. What Is Professional Administration? Professional administration involves the use of a professional third party to help manage the injured party’s medical settlement funds or Medicare Set Aside (MSA) after settlement. “Professional administration achieves two important goals,” says Marques Torbert, CEO of Ametros. “It saves the injured party significant money on their medical expenses by providing them with access to discounted medical network prices, and it ensures that all their reporting to Medicare for a Medicare Set Aside account is done properly.” When an MSA account runs out of funds and reaches a zero-dollar account balance, as long as it is administered properly Medicare agrees to step in as the secondary payer covering the continuing and needed medical expenses. Medicare “highly recommends” the use of professional administration to make sure that funds are extended as long as possible through discounts, used appropriately for medical care and ultimately reported properly so that Medicare will know when to step in as the payer. See also: Big Misunderstanding on MSAs   What Is a Structured Settlement? A structured settlement is a stream of periodic payments paid to an injured party by the defendant primarily through the purchase of annuity (fixed and determinable) issued directly by highly rated life insurance companies. In the case of an MSA, the annuity will enable the issuance of annual payments that cover the entire MSA amount. As Eric Vaughn, executive director of the National Structured Settlements Trade Association, explains, “Structured settlements provide an injured party with a reliable, stable source of income which can be critical to cover their ongoing medical costs. A structured settlement removes the variability of the markets and guesswork out of funding their future expenses.” The Centers for Medicare and Medicaid Services (“CMS” or “Medicare”) is accustomed to the use of annuities with MSAs. Medicare has provided clear guidelines for how the MSA should be set up when annuities are involved, with two years of costs funded upfront and the rest of the cost broken out annually over the injured person’s lifetime. When an MSA is sent to Medicare for approval, Medicare will review and approve MSAs with structures. When assessing future medical costs in an MSA, it’s important to take a very conservative approach. Using a structured settlement and professional administration for the MSA can provide valuable protection to an injured party should they have a costly year. The combination of these services will allow the injured party to properly get coverage from Medicare in the event their MSA funds run out. That Medicare coverage can, in many cases, ensure that the injured person pays less out of their own pocket. As Vaughn points out, “Annuities are a natural fit with MSAs, given the annual medical expenses are already budgeted over the individual’s lifetime.” Torbert adds, “Attorneys and adjusters alike are recognizing the power of combining the annuity with administration not only to assist the injured party in saving money, but also to provide them with support for their medical care over the long run.” It’s important to keep in mind, not all professional administrators and annuities are the same. Choose an administrator that provides the best service and saves the injured party most on medical expenses. When choosing annuities, it’s important to work with a trusted broker and to select a reliable, highly rated life insurance company. Speak with experts in both administration and structures to make sure you and your client make the right selection to ensure you have the most financial protection. Case Study Let’s take a look at an example of how an injured party, Joe, can leverage these two important services to protect his settlement dollars in the MSA. Let’s assume that Joe accepted a settlement with an MSA and has a life expectancy of 10 years. In the first, good scenario, Joe is doing well and is using professional administration to receive discounts so he has relatively low spending of a few thousand dollars a year on MSA medical items. Both a lump sum and structured account would have the same amount spent at the end of Joe’s life expectancy. Let’s take a look at the unique protection that professional administration and a structured settlement together can offer Joe in the scenario where he undergoes a costly surgery or other adverse outcomes. Let’s assume that Joe is offered the exact same MSA settlement amount and starts out on the same pace. Unfortunately, three years after settlement, Joe needs to pay for a complex surgery. With a lump sum account, Joe ends up having to pay for the remaining cost of the surgery after using what funds he currently has in his MSA account. Unfortunately, with a lump sum settlement, he will never receive MSA funds again. If he is Medicare-eligible, Medicare will cover about 80% of the remaining balance, and Joe will have to pay 20% out of pocket for all future treatment costs for the rest of his life (such as Medicare premiums and his regular treatments). If Joe has a structured account managed by a professional administrator, his funds will take a large hit at the time of his surgery, but the administrator will have ensured the funds were spent appropriately, so Medicare will step in as the primary payor. Medicare will pay for 80%, and he will take care of 20% out of pocket for the remaining balance of the surgery only for that year. After that year, his account will continue to replenish annually, and he can use his MSA funds to pay for future treatment. In summary, the outcomes for Joe can be strikingly different. With the lump sum settlement, he is losing personal funds, and he never again has the chance to build value in his MSA account. With the structured settlement, Joe is better off over time. The way Joe settles his case has a very powerful impact on his finances, and the combination of a structured settlement and professional administration protects the injured party more effectively. To view the math behind why administration and structures are the best combination, click here. You can find the full whitepaper here.