Why do you buy a product or pay for a service? What motivates your customers to say “yes” to what you are offering? Have you ever thought about it, really? The list in your mind is probably endless, but do you think it has anything to do with persuasion? Yes, persuasion. For a number of years many companies have persuaded us (the public) to buy their products or try their service using some very catchy ads like: Proctor and Gamble's “Thank you, Mom” campaign; The ever-so-catchy “Every Kiss Begins with Kay” that’s helped the jeweler sell loads of diamonds; and My local favorite, Digicel, “The Bigger, Better Network.” A lot of companies understand the science behind what makes you say “yes,” and you can thank Dr. Robert Cialdini for it. In his book ,“Influence: The Psychology of Persuasion.” Dr. Cialdini showed that people do what they observe other people doing. It’s a principle that’s based on the idea of safety in numbers. For example, when I am feeling for a good doubles (a sandwich sold on the street that those of you not from Trinidad and Tobago are missing out on), I will automatically gravitate to the doubles man who has a lot of people around him. I will be very cautious of someone selling doubles who has just a few people buying. But that is the science of social proof. If a group of people is looking to the back of the elevator, an individual who enters the elevator will copy it and do the same, even if it looks funny. Companies use this all the time. Anyone shopping on Amazon can read tons of customer feedback on any product. Some companies show their Facebook likes and Twitter followers. Whether we admit it or not, most of us are impressed when someone has a ton of subscribers, Twitter followers, YouTube views, blog reviews, etc. Calidini's six principles of persuasion (which are very similar to mine, even though I didn't know who he was until a month ago) are:

1. Reciprocity
2. Commitment and consistency
3. Social proof
4. Likability
5. Authority
6. Scarcity

If you are wondering if these principles are still relevant after almost 30 years, yes, they are. As a matter of fact, these principles are the foundation for many marketing campaigns, and many companies use them to get you to buy their product or service. Most people can’t explain why they made a particular decision. But Dr. Cialdini can. After countless experiments and research, Dr. Cialdini identified those six underlying factors that influence decisions and explained how to use the factors to get more positive responses. Let look at the factors and their applications individually in a business context: Reciprocity According to Dr. Cialdini, reciprocation explains why free samples can be so effective. People feel indebted to those who do something for them or give them a gift. People who receive an unexpected gift are more likely to listen to a product’s features, donate to a cause or tip a waiter more. Give something — information, samples, a positive experience, etc. — and people will want to give you something in return. A lot of companies have adopted this principle of reciprocity. Netflix, Amazon and Hubspot all offer a free service for a stipulated period of time. And some bloggers offers free downloads, free webinars, free ebooks. These companies and individuals understand that human beings are wired to return favors and, as a result, site visitors will be more likely to feel obligated to buy something from the company or individual's website. Commitment and Consistency People take a lot of pride in being true to their word. Dr. Cialdini suggests that oral and written commitments are powerful persuasive techniques and that people tend to honor agreements — even after the original incentive or motivation is no longer present. Cialdini indicated that people want to be consistent and true to their word. Getting customers or co-workers to publicly commit to something makes them more likely to follow through with an action or a purchase. Getting people to answer “yes” makes them more powerfully committed to an action. Conversion Voodoo helped a mortgage company increase its completed application conversion rate by more than 11% with the simple addition of a commitment checkbox. That simple act of commitment propels the mortgage company's customers toward making a larger commitment. Social Proof We dealt with social proof above. People will normally follow the crowd (safety in numbers). Likability Dr. Cialdini explained that likability is based on sharing something similar with people you like. People will naturally associate with people who are like them, and this applies to businesses as well. Customers tend to buy from companies they like. Everyone has a favorite brand that appeals to them — the more similarities there are between the customer and brand, the more positive that relationship will be over time. A lot of companies conduct extensive research to segment their market, target their niche and position the company to appeal to its target market. These companies design their products, services, logos, websites, outlets, etc. to mirror their customers. We are influenced by a product or service we like. See also: How Customers Really Think About Insurance   Likability may also come in the form of trust. Being fair, open, genuine and honest in your actions and having a general interest in people and their welfare will begin to build that trust with your staff, which is one of the branches of likability and respect. Authority Are you more likely to take instruction from a person who you perceived to be an authoritative person? According to Dr. Cialdini, job titles such as “doctor” can infuse an air of authority and, as a result, this can lead the average person to accept what a person is saying without question. If you take LinkedIn influencers, for example, their posts attract thousands of views and comments simply because people considers the influencers to be people of authority in their field because of their success. According to Dr. Cialdini, “When people are uncertain, they don’t look inside themselves for answers — all they see is ambiguity and their own lack of confidence. Instead, they look outside for sources of information that can reduce their uncertainty. The first thing they look to is an authority. We’re not talking about being in authority but about being an authority.” Nike is one of the most coveted brands in the world, and one of their major strengths is their association with very successful athletes who are considered an authority in their sport. So, quite naturally with that association, Nike has become an authoritative brand in the world of sports apparel. Scarcity In economic terms, “scarcity” relates to supply and demand. The less there is of something, the more valuable it is. According to Dr. Cialdini, the more rare and uncommon something is, the more people want it.  For example, a lot of companies use phrases like “Don’t miss this chance,” or, “Book your spots early; Limited seating available.” Many companies may manufacture a limited amount of a product in an attempt to generate a sense of limitation to the general public. Have you ever notice the long lines for a new product? People camping outside a store? If you create that environment of scarcity, you will create a demand for your product or service. See also: How to Exceed Customer Expectations   The six principles I've mentioned are very powerful simply because they bypass our rational mind and appeal to our subconscious instincts. A good seller will always refer to the positive opinion of other users and how successful the product is. Or the seller will give customers a free trial, etc. But it is important to note that if you are unethical and are trying to con your customers, people will see right through your scam. These principles will only be effective if you are genuine in your efforts and you deliver on your promise to your customers. To gain more insight into the use of persuasion, you can secure a copy of my Kindle eBook, “The 6 Principles of Persuasion Everyone in Business Should Know, Release the Trigger of Compliance in Your Staff and Customers.” You will learn influential strategies that many successful companies use to increase their sales, attract more customers, manage their employees more effectively and communicate to influence others.

This is the second part of a two-part series. The first part can be found here.  Abstract In the second part of this article, we extend the discourse to a notional micro-economy and examine the impact of diversification and insurance big data components on the potential for developing strategies for sustainable and economical insurance policy underwriting. We review concepts of parallel and distributed algorithmic computing for big data clustering, mapping and resource reducing algorithms.   1.0 Theoretical Expansion to a Single Firm Micro-Economy Case We expand the discourse from part one to a simple theoretical micro-economy, and examine if the same principles derived for the aggregate umbrella insurance product still hold on the larger scale of an insurance firm. In a notional economy with {1…to…N} insurance risks r_1,N and policy holders respectively, we have only one insurance firm, which at time T, does not have an information data set θ_T about dependencies among per-risk losses. Each premium is estimated by the traditional standard deviation principle in (1.1). For the same time period T the insurance firm collects a total premium π_T[total] equal to the linear sum of all {1…to…N} policy premiums π_T[r_N] in the notional economy. There is full additivity in portfolio premiums, and because of unavailability of data on inter-risk dependencies for modeling, the insurance firm cannot take advantage of competitive premium cost savings due to market share scale and geographical distribution and diversification of the risks in its book of business. For coherence we assume that all insurance risks and policies belong to the same line of business and cover the same insured natural peril - flood, so that the only insurance risks diversification possible is due to insurance risk independence derived from geo-spatial distances. A full premium additivity equation similar to an aggregate umbrella product premium (3.0), extended for the case of the total premium of the insurance firm in our micro-economy, is composed in (9.0) In the next time period T+1 the insurance firm acquires a data set θ_T+1 which allows it to model geo-spatial dependencies among risks and to identify fully dependent, partially dependent and fully independent risks. The dependence structure is expressed and summarized in a [NxN] correlation matrix - ρ_i,N. Traditionally, full independence between any two risks is modeled with a zero correlation factor, and partial dependence is modeled by a correlation factor less than one. With this new information we can extend the insurance product expression (7.0) to the total accumulated premium π_T+1[total] of the insurance firm at time T+1 The impacts of full independence and partial dependence, which are inevitably present in a full insurance book of business, guarantee that the sub-additivity principle for premium accumulation comes into effect. In our case study sub-additivity has two related expressions. Between the two time periods the acquisition of the dependence data set θ_T which is used for modeling and definition of the correlation structure ρ_i,N provides that a temporal sub-additivity or inequality between the total premiums of the insurance firm can be justified in (10.1). It is undesirable for any insurance firm to seek lowering its total cumulative premium intentionally because of reliance on diversification. However an underwriting guidelines’ implication could be that after the total firm premium is accumulated with a model taking account of inter-risk dependencies, then this total monetary amount can be back-allocated to individual risks and policies and thus provide a sustainable competitive edge in pricing. The business function of diversification and taking advantage of its consequent premium cost savings is achieved through two statistical operations: accumulating pure flood premium with a correlation structure, and then back-allocating the total firms’ premium down to single contributing risk granularity. A backwardation relationship for the back-allocated single risk and single policy premium π'_T+1[r_N] can be derived with a standard deviations’ proportional ratio. This per-risk back-allocation ratio is constructed from the single risk standard deviation of expected loss σ_T+1[r_N] and the total linear sum of all per-risk standard deviations  in the insurance firm’s book of business. From the temporal sub-additivity inequality between total firm premiums in (10.1) and the back-allocation process for total premium  down to single risk premium in (11.0), it is evident that there are economies of scale and cost in insurance policy underwriting between the two time periods for any arbitrary single risk r_N. These cost savings are expressed in (12.0). In our case study of a micro economy and one notional insurance firms’ portfolio of one insured peril, namely flood, these economies of premium cost are driven by geo-spatial diversification among the insured risks. We support this theoretical discourse with a numerical study. 2.0 Notional Flood Insurance Portfolio Case Study We construct two notional business units each containing ten risks, and respectively ten insurance policies. The risks in both units are geo-spatially clustered in high intensity flood zones – Jersey City in New Jersey – ‘Unit NJ’ and Baton Rouge in Louisiana – ‘Unit BR’. For each business unit we perform two numerical computations for premium accumulation under two dependence regimes. Each unit’s accumulated fully dependent premium is computed by equation (9.0). Each unit’s accumulated partially dependent premium, modeled with a constant correlation factor of 0.6 (60%), between any two risks, for both units is computed by equation (10.0). The total insurance firm’s premium under both cases of full dependencies and partial dependence is simply a linear sum – ‘business unit premiums’ roll up to the book total. In all of our case studies we have focused continuously on the impact of measuring geo-spatial dependencies and their interpretation and usability in risk and premium diversification. For the actuarial task of premium accumulation across business units, we assume that the insurance firm will simply roll - up unit total premiums, and will not look for competitive pricing as a result of diversification across business units. This practice is justified by underwriting and pricing guidelines being managed somewhat autonomously by geo-admin business unit, and premium and financial reporting being done in the same manner. In our numerical case study we prove that the theoretical inequality (10.1), which defines temporal subadditivity of premium with and without dependence modeled impact is maintained. Total business unit premium computed without modeled correlation data and under assumption of full dependence  always exceeds the unit’s premium under partial dependence computed with acquired and modeled correlation factors. This justifies performing back-allocation in both business units, using procedure (11.0), of the total premium  computed under partial dependence. In this way competitive cost savings can be distributed down to single risk premium. In table 4, we show the results of this back-allocation procedure for all single risks in both business units:   For each single risk we observe that the per-risk premium inequality (12.0) is maintained by the numerical results. Partial dependence, which can be viewed as the statistical – modeling expression of imperfect insurance risk diversification proves that it could lead to opportunities for competitive premium pricing and premium cost savings for the insured on a per-risk and per-policy cost savings. 3.0 Functions and Algorithms for Insurance Data Components 3.1 Definition of Insurance Big Data Components Large insurance data component facilitate and practically enable the actuarial and statistical tasks of measuring dependencies, modeled loss accumulations and back-allocation of total business unit premium to single risk policies. For this study our definition of big insurance data components covers historical and modeled data at high geospatial granularity, structured in up to one million simulation maps. For modeling of a single (re)insurance product a single map can contain a few hundred historical, modeled, physical measure data points. At the large book of business or portfolio simulation, one map may contain millions of such data points. Time complexity is another feature of big data. Global but structured and distributed data sets are updates asynchronously and oftentimes without a schedule, depending on scientific and business requirements and computational resources. Thus such big data components have a critical and indispensable role in defining competitive premium cost savings for the insureds, which otherwise may not be found sustainable by the policy underwriters and the insurance firm. 3.2 Intersections of Exposure, Physical and Modeled Simulated data sets Fast compute and big data platforms are designed to provide various geospatial modeling and analysis tasks. A fundamental task is the projection of an insured exposure map and computing its intersection with multiple simulated stochastic flood intensity maps and geo-physical properties maps containing coastal and river banks elevations and distances to water bodies. This particular algorithm performs spatial cashing and indexing of all latitude and longitude geo-coded units and grid-cells with insured risk exposure and modeled stochastic flood intensity. Geo-spatial interpolation is also employed to compute and adjust peril intensities to distances and geo-physical elevations of the insured risks. 3.3 Reduction and Optimization through Mapping and Parallelism One relevant definition of Big Data to our own study is datasets that are too large and too complex to be processed by traditional technologies and algorithms. In principle moving data is the most computationally expensive task in solving big geo-spatial scale problems, such as modeling and measuring inter-risk dependencies and diversification in an insurance portfolio. The cost and expense of big geo-spatial solutions is magnified by large geo-spatial data sets typically being distributed across multiple hard physical computational environments as a result of their size and structure. The solution is distributed optimization, which is achieved by a sequence of algorithms. As a first step a mapping and splitting algorithm will divide large data sets into sub-sets and perform statistical and modeling computations on the smaller sub-sets. In our computational case study the smaller data chunks represent insurance risks and policies in geo-physically dependent zones, such as river basins and coastal segments. The smaller data sets are processed as smaller sub-problems in parallel by assigned appropriate computational resources. In our model we solve smaller scale and chunked data sets computations for flood intensity and then for modeling and estimating of fully simulated and probabilistic insurance loss. Once the cost effective sub-set operations are complete on the smaller sub-sets, a second algorithm will collect and map together the results of the first stage compute for consequent operations for data analytics and presentation. For single insurance products, business units and portfolios an ordered accumulation of risks is achieved via mapping by scale of the strength or lack thereof their dependencies. Data sets and tasks with identical characteristics could be grouped together and resources for their processing significantly reduced by avoiding replication or repetition of computational tasks, which we have already mapped and now can be reused. The stored post-analytics, post-processed data could also be distributed on different physical storage capacities by a secondary scheduling algorithm, which intelligently allocates chunks of modeled and post-processed data to available storage resources. This family of techniques is generally known as MapReduce. 3.4 Scheduling and Synchronization by Service Chaining Distributed and service chaining algorithms process geo-spatial analysis tasks on data components simultaneously and automatically. For logically independent processes, such as computing intensities or losses on uncorrelated iterations of a simulation, service chaining algorithms will divide and manage the tasks among separate computing resources. Dependencies and correlations among such data chunks may not exist because of large geo-spatial distances, as we saw in the modeling and pricing of our cases studies. Hence they do not have to be accounted for computationally and performance improvements are gained. For such cases both input data and computational tasks can be broken down to pieces and sub-tasks respectively. For logically inter-dependent tasks, such as accumulations of inter-dependent quantities such as losses in geographic proximity, chaining algorithms automatically order the start and completion of dependent sub-tasks. In our modeled scenarios, the simulated loss distributions of risks in immediate proximity are accumulated first, where dependencies are expected to be strongest. A second tier of accumulations for risks with partial dependence and full independence measures is scheduled for once the first tier of accumulations of highly dependent risks is complete. Service chaining methodologies work in collaboration with auto-scaling memory algorithms, which provide or remove computational memory resources, depending on the intensity of modeling and statistical tasks. Challenges still are significant in processing shared data structures. An insurance risk management example, which we are currently developing for our a next working paper, would be pricing a complex multi-tiered product, comprised of many geo-spatially dependent risks, and then back-allocating a risk metric, such as tail value at risk down to single risk granularity. On the statistical level this back-allocation and risk management task involves a process called de-convolution or also component convolution. A computational and optimization challenge is present when highly dependent and logically connected statistical operations are performed with chunks of data distributed across different hard storage resources. Solutions are being developed for multi-threaded implementations of map-reduce algorithms, which address such computationally intensive tasks. In such procedures the mapping is done by task definition and not directly onto the raw and static data. Some Conclusions and Further Work With advances in computational methodologies for natural catastrophe and insurance portfolio modeling, practitioners are producing increasingly larger data sets. Simultaneously single product and portfolio optimization techniques are used in insurance premium underwriting, which take advantage of metrics in diversification and inter-risk dependencies. Such optimization techniques significantly increase the frequency of production of insurance underwriting data, and require new types of algorithms, which can process multiple large, distributed and frequently updated sets. Such algorithms have been developed theoretically and now they are entering from a proof of concept phase in the academic environments to implementations in production in the modeling and computational systems of insurance firms. Both traditional statistical modeling methodologies such as premium pricing, and new advances in definition of inter-risk variance-covariance and correlation matrices and policy and portfolio accumulation principles, require significant data management and computational resources to account for the effects of dependencies and diversification. Accounting for these effects allows the insurance firm to support cost savings in premium value for the insurance policy holders. With many of the reviewed advances at present, there are still open areas for research in statistical modeling, single product pricing and portfolio accumulation and their supporting optimal big insurance data structures and algorithms. Algorithmic communication and synchronization cost between global but distributed structured and dependent data is expensive. Optimizing and reducing computational processing cost for data analytics is a top priority for both scientists and practitioners. Optimal partitioning and clustering of data, and particularly so of geospatial images, is one other active area of research.

Creating businesses is the challenge of the day for large organizations. After years of cost-cutting and downsizing, companies have realized they can’t shrink their way to success.

In a world where what’s possible is advancing at breakneck speeds, social behavior, technology and global economy are driving forces for change. Established brands have realized they can’t stay relevant, differentiate themselves or gain a competitive advantage by tweaking aging product portfolios, buying out rivals or expanding to developing nations.

Innovation is crucial now more than ever, so companies must become Janus-like — looking in two directions at once, with one face focused on the old that still accounts for the bulk of their revenue and the other seeking out the new.

Innovation brings the hope of new value and the fear of the unknown. It is often born at the fringes of an organization’s established divisions and, at times, it exists in the spaces between. The truth is that innovation is a messy business. The high levels of uncertainty associated with new ventures need adaptive organizational structures to succeed. A company's operating, financial and governance models are seldom the same as existing businesses. In fact, most new business models are not fully defined in the beginning; they become clearer as new strategies are tried, customer needs are understood and anticipated and new applications are developed to facilitate new experiences. This uncertainty results in half-baked superficial changes that happen at the edge because it is easiest there, that require minimal organizational effort and that get the most visibility. Launching innovation labs, incubators or venture units requires a few bodies on the ground in a trendy office — even if they don’t produce much tangible value after the post-launch media hype wears off.

See also: Secret Sauce for New Business Models?  

Crossing the threshold to innovate is imperative, but transitions from the current tried-and-tested state to the new state with unfamiliar rules and values is daunting for most people. It takes clarity of vision to create momentum and inspire others. Above all, it’s a balancing act between the old and the new cultures that are often placed in conflict with one another if the company takes an either or approach to corporate entrepreneurship.

Even when a breakthrough innovation is ready to be implemented, delivery becomes impossible in this corporate environment. Most leaders find there’s a fine line between corporate entrepreneurship and insubordination.

I get asked by CEOs and heads of departments how we solve these problems. How do we make a real impact with consensus and harmony? I suggest a new approach is called for, one that blends these cultures to avoid extreme behavior and creates equilibrium in areas of strategy, operations and organization. We have only to look at any successful enterprise such as Apple, Uber or Netflix, and we’ll find innovation at its core. These companies are bold about taking risks, driving change for the better and doing it at scale through human-centered design. This understanding and building a collaborative culture to actively seek out solutions to challenging problems and identifying relevant strategies continues to expand the realm of the possible.

Who knew proof of insurance could be a major news story? I suppose you could frame the question as, How many more ways can Dwight Howard get fans mad at him? But I prefer the insurance angle.

The story goes like this:

The 31-year-old center for the Atlantic Hawks was pulled over a bit after 2 in the morning on April 28 for driving 95mph in a 65mph zone about 10 miles from his home. Police found that he was driving his Audi Rs7 with a suspended registration and without proof of insurance. They let him off with a verbal warning for the speeding and the suspended registration—one of the perks of being an NBA star, I suppose—but towed his car because he couldn't prove his claim that he had insurance.

The incident might have stayed under the radar without the towing but popped up on sports sites this week and caused a stir among fans. Why was Howard out at 2 in the morning on the day of a playoff game? Was his late night the reason he played poorly later that day in a game that eliminated the Hawks from the playoffs? The car towing fed into the narrative about Howard, a supremely talented player who has never lived up to expectations, especially in the playoffs, and is now with his fourth team. Atlanta fans are outraged, while fans of his three previous teams are chuckling and saying, "Told you so."

All because Howard couldn't produce proof of insurance.

Did he have insurance? He certainly can afford it. He earns more than $23 million a year and has been making that kind of money for a long time now. But we don't know for sure, because of the archaic systems we use that mean most of us carry proof of insurance as little pieces of paper in our cars. 

At its core, insurance is as digital as any industry there is—we basically track a whole lot of data on people, curate a mass of very precise promises and wire money—so it strikes me as odd that we turn the data into paper and PDFs and handle them manually. Why can't we leave the data in its native state and just make it available whenever and wherever the bits and bytes are needed?

That question is why I'm a fan of GAPro, a startup that is trying to rewire the industry to stop these unnatural acts that we perform on data and to make the industry much more efficient. If you share my belief that data should stay in its native state, then I encourage you to read the article on Luddites below, by Chet Gladkowski of GAPro, which lays out the company's argument in detail.

Speaking of rewiring the industry for efficiency...our friends at Pypestream and our friends at EY (yes, we introduced them to each other) made an important announcement this week. EY will help clients implement Pypestream's intelligent messaging, which is cutting the costs of customer service while making customers happier (how many times can you say that with a straight face?) and which is now moving into core operations at insurers, too. Pypestream is becoming the industry standard for chatbots and other aspects of intelligent messaging—as it should, in my humble opinion—and the alliance with EY will accelerate the trend. 

Cheers,

Paul Carroll,
Editor-in-Chief 

Late in the 18^th century, at the dawn of the Industrial Revolution, a young mechanic named Ned Ludd became famous for destroying two machines. His actions spawned an anti-technology movement that lasted into the early 19^th century. Adherents of that movement became known as Luddites, a term that is used to this day to describe an individual or group of people who are against technological progress. As technology has continued to advance, modern-day Luddites have materialized. In today’s increasingly connected world, there are already those that go “off-the-grid,” or at least sympathize with the idea. Since we are on the verge of connecting and automating virtually anything you can imagine, it is worthwhile to ponder the possibility of segments that will oppose this progress or just rebel outright. What will this mean for society and the insurance industry?

First, it is important to explore just how connected the world is becoming. In addition to smart home devices, drones, and connected cars that are receiving so much attention, there are smart things and solutions to address every facet of life. Even items such as toothbrushes, underwear(!), and patio umbrellas have smart versions that are collecting real-time data about their usage and their surroundings. More and more of the world is being monitored, analyzed, and automated.

See also: It’s Time to Act on Connected Insurance  

Increasingly, these smart things are being powered by artificial intelligence which allows the connected things to become animated – taking action without any human intervention. This is leading some people to fear job loss, intrusions into privacy, and in the worst case future scenarios, machine overlords à la The Terminator series. While there are many potential benefits of emerging tech advances for society, it is not difficult to see why there are a growing number of people that fear the prospect of a connected world. Some of these are modern-day Luddites with irrational fears, but others raise important concerns that society must address.

There are a number of key considerations for insurers regarding these concerns and individuals.

• Customer segmentation: Insurers recognize that smart technologies have great potential to improve safety and security across many situations, lowering the risk for individual and business customers. New products and services, fueled by new partnerships, will be increasingly offered by insurers. At the same time, the actual adoption uptake for connected technologies is still an unknown. There may be large segments of the population that will make the conscious choice not to be connected, and insurers will have to continue to accommodate their needs too.
• The insurtech movement: Much of the insurtech movement is based on the increasing connectivity and data generated by connected things. Approximately 30% of the 900+ insurtech startups tracked by SMA fall directly into the connected world space, in areas like connected vehicles, smart homes, connected commercial, or connected life and health solutions. Another 14% are digital data/analytics firms. Many of those provide capabilities for insurers to use the new, real-time data sources to gain insights for underwriting, loss control, claims, or other business areas. Although many of these startups are likely to fail, others will succeed and have a key role in reshaping the insurance industry.
• A digital divide: One scenario predicted by some is a dichotomy between urban and rural environments. The urban centers are more likely to be highly connected, with vehicles, buildings, infrastructure, and medical and educational facilities all contributing to a smart city environment. Rural settings may be relatively unconnected, as the value is questioned by individuals and small businesses and the desire for independence trumps the benefits of connected technology. Insurers should factor in the potential for a growing digital divide between cities and rural environments.
• Customer interactions: Insurers already have difficulty convincing customers to go green by converting to electronic documents and communications. For lots of them, this is unlikely to change overnight due to the feeling that their electronic footprint is already greater than they would like it to be. Many new ways to communicate with policyholders and agents are being introduced, and insurers are already taking advantage of these. But, not everyone will want to opt-in for these types of interactions.
• A back-to-nature movement is already evident for some individuals that are looking for a simpler, healthier lifestyle. This has driven part of the transformation in the agriculture and the grocery sectors.

See also: The New Paradigm of Connected Insurance  

Insurers are likely to see implications across many industry verticals as certain segments choose to be unconnected in the future.

Twenty five years ago labor experts warned employers about an impending shortage in the skilled manufacturing workforce caused by the soon-to-be-departing baby boomers. Almost no one listened. Those few employers who did realized preparation meant investing in training. Investment = money so many employers put it off, especially during the Great Recession of 2008 – 2010. So here we are America … needing to fill 3.5 million manufacturing jobs in the next 10 years, according to the Deloitte publication, “The Skills Gap in U.S. Manufacturing 2015 & Beyond.” Deloitte opines that we’ll be lucky to fill 1.5 million of those openings, leaving a gap of 2 million jobs. This potential shortfall didn’t go unnoticed by Daimler Trucks North America (DTNA), a manufacturer of class 5-8 commercial vehicles, school buses, and heavy-duty to mid-range diesel engines. The company saw this bullet coming years ago. See also: Insurance And Manufacturing: Lessons In Software, Systems, And Supply Chains   To those in the know, the skilled workforce shortage conundrum isn’t new. As far back as 1990, the National Center on Education and the Economy identified this job shortfall in its report, “The American Workforce – America’s Choice: High Skills or Low Wages,” stating large investments in training were needed to prepare for the slow workforce growth. If you look at the burgeoning skills gap, coupled with vanishing high school vocational programs, how, as an employer, do you recruit potential candidates?

To not address the millennials’ employer predilections is to miss an opportunity to tap into a vast resource of potential talent.

DTNA addresses the issue by reaching out to high schools throughout the U.S. via the Daimler Educational Outreach Program, which focuses on giving to qualified organizations that support public high school educational programs in STEM (science, technology, engineering and math), CTE (career technical education), and skilled trades’ career development. Daimler also works in concert with school districts to conduct week-long technology schools in one of the manufacturing facilities, all in an effort to encourage students to consider manufacturing (either skilled or technical) as a vocation. Like all forward-looking companies, Daimler must address the needs of the millennials who – among a number of their desires – want to make the world a better place. Jamie Gutfreund, chief strategy officer for the Intelligence Group notes that 86 million millennials will be in the workplace by 2020 — representing 40 percent of the total working population. To not address the millennials’ employer predilections is to miss an opportunity to tap into a vast resource of potential talent. To that end, Daimler has always emphasized research in renewable resources and community involvement as well as a number of philanthropic endeavors. Not only is it the right thing to do, but it also appeals to the much-needed next generation who will fill the boots of the exiting boomers. See also: 4 Steps to Integrate Risk Management   Just because a company manufactures heavy-duty commercial vehicles doesn’t mean it can’t give back to the environment and the community at large. And, in the end, that will help make the world a better place.

This article is the second in a series on key forces shaping the insurance industry. Here is Part One. Trend #2: Data: External data and contextual information will become increasingly more important than historical internal data for predicting risk and pricing. The insurance industry tends to look backwards to understand the future. Underwriting and pricing are based on historical data using proxy factors. However, the explosion of information from IoT devices, wearables, genomics, bionomics and health tech is driving a fundamental change in the approach to pricing, risk selection and underwriting across all lines of business. It is now possible to base underwriting decisions on real time detailed information specific to the individual risk and monitor and update those decisions over the life of the policy. See also: 10 Trends at Heart of Insurtech Revolution How different product lines will accommodate external and contextual information P&C personal Lines – personal lines products, particularly motor and home, are already seeing positive momentum in the use of data. For example, insurance propositions leveraging smart home devices. Data from these devices can help prevent accidents (for example, responding to a burst pipe) and can help inform and assess the risk profile of the policy in real time (for example, the period each day when the property is empty). Similarly, in motor the data from telematics devices can be used to determine the relative quality of the driver. Going forward it will be possible to adapt pricing based on the length and nature of a journey (for example, motorway versus city centre, weather conditions and weight of traffic). “Real time pricing of motor and home based on an actual risk profile” Commercial lines – the dynamic in commercial lines is slightly different and likely to drive commercial insurers and brokers more towards risk mitigation and risk management rather than traditional risk transfer solutions. For example, as the quality of monitoring devices and technology significantly reduces the chances of a piece of machinery going wrong the need for insurance falls. To remain relevant insurers therefore need to help their customers better manage their risk profile whilst providing protection in the event of a catastrophe. Also, IoT devices can supply detailed information about a risk during the life of the policy, presenting the opportunity to change the pricing or more likely allow the insurer to manage their reinsurance program in real time and provide valuable support to their client to help reduce accidents. “Commercial lines insurance will become increasingly about risk mitigation rather than risk transfer” Life and Health – in life and health, initial moves have seen insurers adopt wellness programs to help encourage policyholders to live more healthy lives. This is the tip of the iceberg. Over the next few years the quality and quantity of information about an individual is going to increase exponentially and can be used to identify potential health issues much earlier than traditional means thereby allowing intervention and increased likelihood of a successful outcome. Information will also be able to tell us a lot more about the relative health of the individual and susceptibility to certain diseases. In this environment, insurance is about prevention and providing access to the technology rather than simply protection after something has happened. “Leading tech companies believe that historical underwriting factors in life insurance are completely irrelevant” Where next? The problem is that whilst the velocity of data is going to increase exponentially the ability of the vast majority of insurers to capture and use this new information is very limited due to the legacy IT environment. Insurers cannot respond with their current systems and that creates the opportunity for insurtech companies, particularly those who can provide an end to end solution that both engages the customer and facilitates the capturing, processing and use of the information. See also: 10 Predictions for Insurtech in 2017   We hope you enjoy these insights, and look forward to collaborating with you as we create a new insurance future. Next article in the series: Trend #3: Majority of the simple covers will be bought in standard units through a ‘marketplace/ exchange’, permitting just-in-time, need and exposure based protection through mobile access This article was written by Sam Evans, Carl Bauer- Schlichtegroll, and Jonathan Kalman

There’s barely a week that goes by without some sort of cyber security incident — a system hack, a data breach, putting thousands if not millions of people’s personal information at risk. Although big corporations generate the most headlines, the reality is that small and mid-sized businesses are equally, if not more, vulnerable to cyber attacks. Smaller organizations don’t have the resources to put up firewalls or deploy high-powered system monitoring software that larger firms can afford. Like the house on the block with an open window and no burglar alarm installed, these businesses are easy prey for hackers, and they’re getting hacked more often than you think. According to IBM, small and mid-sized businesses are hit by 62% of all cyber attacks, at a rate of 4,000 per day. A more sobering statistic? Sixty percent of small businesses go out of business within six months of a breach. As insurance professionals, we have the opportunity to change that outcome. Although we can’t deter the cyber thieves from striking, we can help our business customers protect themselves by effectively educating them on their risks and providing the cyber liability coverage they need. See also: Why Buy Cyber and Privacy Liability. . .   A Quick 411 on Cyber Liability Coverage The good news is, there are 20 or more cyber liability carriers in the marketplace today, which keeps pricing low for budget-conscious business owners. Typically, every million dollars of protection, for a company that has never been hacked, runs about $2,500 per year. That’s well within most businesses’ budgets. However, not all policies are created equal. It’s critical for insurance professionals to spend time educating themselves on the details of what each policy offers before heading off to sell. This ensures that you offer the best solution to each of your customers and can adequately review any coverage they currently have for gaps. Cyber liability policies should always include coverage for the following: Notification Costs and Credit Monitoring Most states require companies to inform anyone affected by the breach of personally identifiable information in a timely manner, and offer credit monitoring for the 12 months following the incident. Typically, businesses have to set up call centers to answer frequently asked questions, as well. A good cyber policy should cover all of these costs. Cyber Extortion According to the FBI, the incidence of ransomware attacks is on the rise. This attack typically begins when an employee clicks on a legitimate-looking email attachment. That one click releases malware that locks digital files until the company pays a ransom to release them. Unless the company pays the tens of thousands of dollars that hackers demand, businesses could lose proprietary information, product schematics, customer orders and other sensitive information. The right policy will help cover the cost of payments to extortionists, as that’s typically the only way to get the data back. Business Interruption If the company’s systems are compromised, hackers encrypt company software or overload Web servers to block legitimate orders, and business comes to a screeching halt. Think about the financial impact a day or a week down could have on a small e-commerce company, a CPA firm or manufacturing operation if they’re not adequately covered for the loss. Public Relations One hack can ruin a local business’s reputation in a heartbeat. If a breach occurs, that company has to hire an experienced public relations team to explain what they’re doing to protect the affected individuals and mitigate reputational risk associated with the breach. Forensics Costs Finally, and perhaps most significantly, a cyber liability policy should cover forensics — hiring computer technologists to come in and identify where and how the breach occurred, and how big the impact was. It’s important to note that this is typically the biggest cost associated with a breach, and the most frequently exhausted limit in cyber liability policies. So, it’s important to make sure the policy you recommend provides adequate coverage in this area. Explaining Cyber Insurance to Your Customers The most effective way to talk to your customers about cyber liability insurance is to show them their exposures. Typically, small and mid-sized businesses don’t think of themselves as being at risk. For example, a restaurant owner might believe that, by using a third-party payment card processor, her business is protected. The reality is: Her patrons don’t care who processes her transactions. They come to her restaurant, eat her food and hand her servers their credit cards. The place where people do business is going to get the blame — and be the one liable for the costs. It’s not just retailers and restaurants that are at risk. Any company with personally identifiable information – Social Security numbers, health records or employee data – is exposed. With the average cost-per-compromised record averaging $221, the more records a company has, the more exposure it has. When you explain that one incident could cost a smaller business $50,000 or $100,000 to rectify, the value of paying a few thousand dollars a year for cyber liability insurance becomes very clear. See also: Cyber Attacks Shift to Small Businesses   In addition to being affordable, cyber policies are quick and easy to get — if the business hasn’t been hacked before. For most carriers, it’s a one-page application that asks basic questions to find out if the company has a firewall, antivirus software and encryption, as well as its use of mobile devices. Typically, you can get a quote in an hour or less, issue the policy and be on your way. Just as important, your customers will know that you’re looking out for their best interests. If I can leave you with one thought, it’s this: In this technology-reliant world, every business has a target on its proverbial back. If some form of cyber-attack hasn’t affected your customers yet, there’s a high probability that they’ll get hit in the near future. No business is too small, and no one is immune. With the right cyber liability coverage, your business customers will be prepared for the inevitable breach — and have the protection they need to survive it.

Insurance agents know that selling insurance is unlike selling anything else. Selling insurance means selling trust, selling promises and selling ideas. As an insurance agent, you know your clients trust your expertise and expect you to have their best interests at heart. But even the best agents need something to make their jobs easier. That something, a lot of insurance professionals find, can be mobile applications. Here are three that have proven very popular: 1. DocuSign The insurance industry still relies on a lot of paperwork. And it requires both insurance professionals and clients to sign a lot of documents. This is why the DocuSign app deserves a place on this list. It’s an e-signature app that you can use to sign documents online with your mobile device. Statistics also show that a large number of insurance companies use e-signatures to sell their products. So, an app that makes this possible ought to be pretty useful for insurance professionals like yourself. Here are some benefits of this app: Sign legal documents immediately:  Any online documents can be signed and delivered in minutes instead of days. Go completely paperless: With this app, you can send documents to be signed any time without having to rely on manual means to do so. Available for different mobile platforms: The DocuSign app can be downloaded for free on iPad, iPhone, Windows and Android devices. See also: Will Policies Break Down Into Apps?   2. Go For an insurance agent to succeed, he needs to know more than his clients do. In the case of car insurance, this can be information about legalities or current deals that are available for clients based on their current situations. However, comparing insurance is a hassle that even the most experienced insurance agent want to skip. This is why the Go car insurance app on iTunes deserves a place on this list. This app can help agents like yourself find the best car insurance for clients in seconds 60 seconds. The best part is that it can help you find packages that can actually save your clients money. Here are other benefits: Chat with a seasoned expert: Even insurance agents who go solo need advice from professionals who have been in the industry for a while. With the Go app, you can chat with agents who can give you tips on getting cheaper rates for your clients. Why you should get it: The Go app was developed for the iPhone 6 Plus, SE, iOS 9 and even the Apple Watch. Reviews rave about the app’s easy-to-use interface. 3. CamScanner Looking for the right financial packages and drafting agreements sometimes requires insurance agents to pore over countless technical and legal documents. With this app in your smartphone, you can capture images of documents like insurance agreements, policies and any other documents. You can: Scan on the go: Think of CamScanner like a mini scanner that you can carry around with you and use whenever you come across an interesting document that can help you service your clients. Convert images to PDF files: To email documents, you have to scan them first. And the whole process becomes incredibly tedious if you have to scan several documents at once or do so countless times. Just take a picture of the document you want to scan, use auto cropping to remove unused edges and use the auto enhancing feature to make the image sharper. Then convert into a PDF file and share the file with clients or colleagues. Get crystal-clear documents: The app takes pretty clear and crisp images that don’t blur even when you zoom into them. It has five enhancement modes that you can use to customize your scans and make them look more professional. With this app, the scanning process is reduced to a few taps on your smartphone. It has options that allow users to send scanned documents via email and social media and to even upload them on third-party cloud services. See also: 5 Insurance Apps to Download Today   The proliferation of mobile tools is having a huge impact on business, and these three apps will help.

Fitness trackers can be a convenient way to monitor the number of steps taken every day. Some insurers have even started using them as a proxy for good health, selling life cover to people who are already fit and who track their steps. Insurers may even reward policyholders’ physical activity with lower premiums and other incentives. The assumption is that regular exercise, especially the number of steps taken, is a predictor of lower mortality. Exercise is known to confer health benefit by improving mental health, reducing cardiovascular risk and lowering cancer mortality. The question is, how many steps might lead to a longer life? Adult walking cadence is 100 steps per minute, a rate that demarks the lower end of moderate-intensity exercise. The World Health Organization suggests an ambitious minimum of 150 minutes of “moderate-intensity” aerobic physical exercise throughout the week, or 75 minutes of vigorous-intensity or a combination (setting aside recommendations for muscle strengthening). Public health authorities across the world have adopted these guidelines to help people improve health, build stamina and burn excess calories. See also: Wearables: Game Changer or a Fad?   Manufacturers of fitness trackers and wearable technology, ever since the Japanese pedometer that came out for the 1964 Olympics, have commonly set the goal at 10,000 steps a day, a marketing ploy not rooted in science or WHO guidelines. Although this "10,000 steps" goal varies greatly by leg length and gait, it translates into roughly five miles a day for the average person and remains a considerable distance, especially considering that the average British adult walks 3,000 to 4,000 steps daily. The figure encourages sedentary people to move but isn’t a magic number on a doorway to health nirvana. Even 5,000 steps a day could be too high for some older adults or people with chronic illness, but small increases will confer health benefits. It’s also important to distinguish between incidental and session-based physical exercise. Incidental exercise is the result of steps taken during the course of the day to get us from A to B, but it neither accounts for the pace nor intensity of the exercise or the true level of fitness. A three-hour workout “session” requires a much higher level of fitness than just walking, not to mention a significant level of motivation. Insurance products that discount for steps walked each day are likely to have broader appeal than those that mandate "session-based" exercise. Asking additionally for, say, three hours per week of sweat-inducing exercise could literally be a step too far. Those unaccustomed to such levels of exercise are likely to conclude that this insurance product is not designed with them in mind. Recent evidence suggests activity tracking brings no immediate measurable health benefit but this misses the point. Regular exercise has benefits that are not necessarily related to easily measurable variables such as weight and blood pressure. It’s important to understand that long-term outcomes are what are important for insurers. See also: Wearable Tech Raises Privacy Concerns   Although the WHO recommends 150 minutes of moderate-intensity exercise a week for ages 18 to 64, a critical review of the literature indicates that just half this level still brings marked health benefits. This suggests insurers could lower the bar and design life insurance programs that would also appeal to older people or those with chronic disease or restricted mobility, who may otherwise rule out buying a policy explicitly linked to fitness.