The benefits of a formalized onboarding program are well-established. Across all industries, companies use onboarding to achieve three primary goals, according to research from Aberdeen:

• Engage new hires in company culture
• Improve new-hire productivity
• Reduce first-year turnover

In the insurance industry, where just shy of 50% of new-producer hires reach validation, these three onboarding objectives are closely related and even more crucial to the success of agencies and brokerages. Yet, only 32% of companies currently have a formalized onboarding program, Aberdeen reports. As we’ve mentioned previously, onboarding programs must be formalized to create any lasting, demonstrable effect. New producer hires must have a similar experience throughout their first several months on the job to determine which actions further the goals. The proven approach is to establish a framework for the onboarding process that is required of all producers, complete with a set schedule, key milestones and benchmarks. With that focus on structure and schedule in mind, here’s an overview of The Institutes Producer Accelerator, featuring Polestar, a successful four-part producer onboarding program 1. Getting started — the first month The first four weeks of any new job are a whirlwind. Producers are tasked with shoring up their sales expertise and insurance industry specifics while also ingraining themselves in their new company’s culture. Finding the right balance of these elements will depend heavily on the producer and his or her job history. A sales pro with little hands-on experience in the insurance business will have very different needs from a recent risk management and insurance graduate who’s never made a sales call. Similarly, a successful producer from a competitor may have all the knowledge and experience needed to succeed but may need to learn new basic processes to fit with your organization’s culture. The best approach for most producers is to create a blend of training and refresher content on sales and insurance basics with a heavy dose of your company culture. Make sure that your program covers insurance topics, like client loss exposures and commercial liability, as well as sales and time management principles, like understanding the sales cycle and best practices for delegation. See also: How New Producers Can Get Fast Start   Producers should also be introduced to senior managers who can detail the company’s culture in the context of its business strategy, competitors and industry landscape. Most importantly, during the first month of a new producer’s tenure, he or she should be matched with a mentor. According to research from Reagan Consulting, 55% of new producers have a mentor—most often a senior producer or sales leader. Reagan researchers concluded that mentors offer the most help to producers hired from outside the insurance industry and individuals with little sales experience. 2. Building relationships — months two and three For new producers, their second and third months should optimize their performance in their new roles within your organization. That means continued meetings with mentors with a heavy focus on goals and objectives, along with specific challenges facing your firm. It’s also a time to continue building insurance and productivity know-how. The second onboarding phase centers on helping the producer establish strong relationships—not only with mentors, co-workers and company leadership, but clients, as well. Weeks five through 12 should focus heavily on refining producers’ sales tactics and targeting specific trouble areas commonly facing new producers, including asking for referrals and shifting from price to value when working with prospective clients. These skills are best learned in a coaching-call environment where the producer and coach role-play specific interactions and the coach provides highly tailored feedback. 3. Expanding skills — months three and four After a few months on the job, new producers should begin to switch from learning material to maintaining their knowledge and staying current on the insurance industry and sales best practices. New producers should identify the industry publications they’ll use to keep up with the industry. They can also take advantage of webinars and other forms of group learning, where insight from other producers is often just as valuable as the material being presented. It’s also a time for producers to start developing specialties to set themselves apart and present unique value to the organization. Producers should work with their mentor to select a specialty industry to focus on and familiarize themselves with industry trends, like data analytics and other technological advances. See also: 4 Good Ways to Welcome Employees   4. Developing strategies — months five and six As a producer enters the sixth month on the job, formalized onboarding should begin to taper off in favor of more specific career guidance through mentors or direct supervisors. At this point, producers are probably not fully verified, but their path toward greater success and productivity should be relatively clear. Part of their transition to a fully contributing team member may be to start networking at industry meetings and seminars and providing unofficial mentoring resources to more recent producer hires.

In Part I of our profile for Europe, we reviewed our statistics for the region, which we gathered in the course of our Global Trend Map (download the full thing here), and outlined a number of qualitative themes, exploring the first two of these:

1. Growth opportunities in a relatively saturated market
2. The European consumer and Europe’s early adopter status
3. How European insurers can deliver on their customer promise with new tech
4. Dynamic, real-time insurance and IoT
5. Progress on developing connected insurance models across the continent

Here we explore themes three to five in discussion with two in-region influencers:

• Switzerland-based venture capitalist Spiros Margaris, VC (InsureScan.net, moneymeets and kapilendo)
• Charlotte Halkett, former general manager of communications at U.K.-based telematics provider Insure The Box (now MD of Buzzvault at Buzzmove)

Delivering on the Customer Promise In Part I, we posited that Europe holds a slight innovation lead over our other major regions, finding this borne out in the more disrupted distribution landscape (with affiliate, aggregator and direct-to-customer channels all relatively well established). However, embracing innovative distribution methods is only part of the story for European insurers seeking to engage digitally savvy and ever-more-demanding consumers; another key aspect is to incorporate a greater level of personalization into products. "The consumer is used to a really personal experience now, and that is exactly the same as when they’re buying a pair of shoes online," comments Charlotte Halkett, formerly of Insure the Box (and now at Buzzmove). "They’re used to being able to get something if they want it, where they want it and at the cost they want, including complete information like the exact half hour it’s going to turn up in their house and what color it is. "That’s the same for the £1,000 insurance they’re going to buy, they want to have that real personalized experience to get the cover they want, how they want it, and to be able to influence the price that they’re going to pay. The big, overwhelming message is that the insurance industry is going to need to be flexible and innovative, because consumers are becoming ever-more-demanding, and the base level of their expectations is rising all the time." Personalization in insurance extends from offering positive customer service across channels to customizing policy prices on an individual basis (UBI). Halkett believes that the U.K. market in particular has been a leader in this sense: "The complexity of pricing has always been at the cutting edge in the U.K.," she says. "From developing general linearized modeling through to telematics, the initial development has occurred within the U.K. And it’s partly to do with this being a worldwide center of insurance, that’s true, but it’s also to do with the consumer. It’s very consumer-led: consumers are very willing to adopt, consumers are very willing to try new things." Halkett believes that the U.K. has served as a guinea pig for in-car telematics and that the models developed here can benefit a wide range of insurance markets. This impression fits in with our product-development stats for Europe overall: Auto was indeed one of the lines respondents identified as driving the most product innovation in the region, the other being health (see our earlier post on product development). We explore UBI models, especially as they relate to the auto line, as our next theme.

"It is important to listen to your customers and speak their language in order to influence your top and bottom line. If you want to satisfy your customers, you have to know what they want and need, what they're saying about you, and how they feel about your products, services and brand." — Monika Schulze, global head of marketing at Zurich Insurance

All these customer initiatives, if they are to be more than just good intentions, require far-reaching back-office transformation; investment is required in new technologies and solid digital capabilities (such as analytics), and these in turn need to be grounded in well-conceived strategies if they are to truly take root and flourish at an organizational level. Let’s look now at what European insurers are doing practically to deliver on their customer promises. Encouragingly, a large majority of European respondents acknowledged having formal digital, mobile and cross-platform strategies, so digitization appears to be well underway among European (re)insurers, consistent with our other regions (see our earlier post on digital innovation). We also found a strong increase in analytics focus/investment among our European respondents, as well as a reasonable level of coordination of analytics across their organizations (see our earlier post on analytics and AI). Analytical and machine-learning models have plenty to get their teeth into with what customer data has been captured directly by insurers, but they can additionally be supplied with external data from third parties. We found this practice to be widespread in Europe, as indeed were formal data-governance strategies.

"The one who is doing similar business to you should be considered as a chance and not as a risk - being connected via Open APIs based on your open insurance ecosystem. You will win because your processes and technologies are faster, cheaper and more customer-oriented than others, because you are open." — Oliver Lauer, formerly head of architecture/head of IT innovation at Zurich

One major hurdle for the implementation of more data-driven, customer-centric systems is the presence of legacy, and this is just as present in Europe as anywhere else. Legacy systems came in second place among the internal challenges for Europe (in line with the global trend), and was additionally identified by our European contributors Halkett and Margaris as a serious challenge for the region. Margaris highlights a couple of particular pain points as far as legacy systems go: "If you have legacy systems, it’s difficult to put cutting-edge technology on top of them," he says. "Legacy systems make it so much harder for incumbents to innovate and to comply with regulations." Taking Insurance into the Real World, Real-Time In Part I of our profile on Europe, we tentatively identified Europe as an early adopter, and we saw this tendency manifested in the prevalence of new-age distribution channels and personalized, customer-centric products. Here, we extend this line of inquiry by turning to the vanguard of personalization in insurance, namely the Internet of Things, and exploring the progress it has made within European insurance. IoT is the final frontier of customer-centricity in the sense that it takes insurance into the real world on a real-time basis, placing the customer literally, and not just figuratively, at the center. If Europe is marginally further along the journey of customer-driven disruption than our other regions, as we have suggested, then we would expect IoT to be marginally ahead, as well. And while the technology is making strides the world over, our stats do place Europe above trend on the IoT-for-insurance adoption curve, at least in terms of current platform implementation (more details in our dedicated Internet of Things section), and the pre-eminence of the continent in this field is borne out by much of our broader research. While Internet of Things was not a priority area that Europe led on in our insurer priorities section (it came second behind Asia-Pacific), Europe did achieve top spot for mobile, customer-centricity and claims – which form a constellation very auspicious for IoT-enabled business models and innovation. Margaris tends to agree on the importance of IoT for European insurers, and Halkett, as we have already mentioned, credits the U.K. market as having fostered the development of in-car telematics.

"The IoT development (expected to reach $20.8 billion by 2020, according to Gartner Inc forecast) should help a new insurance to emerge, increasing customer-centricity and decreasing costs. An example of IoT impact on insurance is wearable tech, a passive way to monitor health and wellbeing, in real time and for everything. By identifying those who seem to be looking after themselves, insurers can drive premiums down for them." — Minh Q Tran, general partner at AXA Strategic Ventures

The real opportunity consists not just in personalized experience à la retail but in personalized pricing, so that the price customers pay reflects their real-world usage as captured by connected devices. It is thus that personalization and premium-price reductions actually go hand in hand; rather than requiring two strategic thrusts, they can be part of one IoT-enabled customer-centric approach. These two Ps – price and personalization – are the two main advantages enjoyed by insurtechs, so insurers looking to the future, and to future-proof themselves, should definitely be taking an interest in IoT. See also: Global Trend Map No. 15: Products   While still only a minority of insurers in Europe have a strategy on usage-based insurance (UBI), this is in line with our other key regions; we expect to see this percentage rise dramatically across the board over the coming years. Auto, home and health are the leading lines across all our regions in terms of the expected IoT benefits, though the benefits of sensor networks in other lines should not be ignored. Auto is an example of a line that has already been extensively transformed by IoT in the form of telematics. This area is home to solutions of varying sophistication, from smartphone apps to "black boxes" built into cars. Depending on the richness of data coming from in-car sensors, a variety of insurance use cases and business models are enabled. The one that most immediately jumps to mind is UBI, incorporating dynamic pricing and driving behavior modifications. By making customers’ premiums dependent on how they drive, insurers both encourage better driving (which is good for everybody) and lower the cost of premiums, which helps to get more people, more affordably, on the road. "The joy of all insurance is the same: the financial desire of the insurance company is completely aligned with customers’ needs. So nobody wants to have crashes! The consumer doesn’t want to have crashes, and the insurance company would like to reduce the risk on their books," Halkett says. "With telematics, you really get to do that; it’s not only that you get to understand the risk of the individual consumer, it’s that you get to influence that risk, so the risk that you write does not have to be the risk that you keep." Even if premium prices remain the same, a premium with the potential for reduction is an infinitely more saleable proposition than the fixed-price alternative. And it is not solely up to drivers to educate themselves – insurers can take a much broader tutelary role by communicating tips and advice on a continuing basis. In this way, companies like Insure The Box are much more than just providers of telematics. "We take customers, and then we make them safer drivers," Halkett says, "and we do that via communications, online portals and via direct messages to the consumers, all the time rewarding safer driving behaviors." From language courses to money-saving apps, gamification has proven itself time and time again to be a powerful force for bringing about positive outcomes, and the case with telematics is no different. The key is to engage the customer via whichever touchpoints are the most natural and offer the highest level of trust and engagement. Insurers should not therefore conceive IoT solely in terms of inbound traffic (data traveling from customer devices to their back office) but also as a means of achieving higher engagement for their outbound messaging (from insurers to customers). Halkett points out the potential of connected home devices, such as the voice-enabled Amazon Alexa, for initiating contact with consumers in a world where "mobile" refers to much more than portable telephones.

"Automated data capture through IoT does not just help insurers preempt claims, it also helps mitigate losses and improve customer service when claim events do occur, by rooting out fraudulent or inflated claims and enabling faster turnaround of legitimate ones. Provided customer privacy concerns form part of the discussion, there is no reason why connected claims cannot be a win-win for everyone." — Mariana Dumont, head of new projects at Insurance Nexus

Beyond facilitating UBI models and continuous customer engagement, IoT solutions also give insurers detailed insight into what is actually happening on the ground on a second-by-second basis. Admittedly, this requires a lot of data and sophisticated models and, in telematics for example, is certainly a lot more than just detecting high G-forces. Indeed, Halkett recounts an example from the early days of Insure The Box, where a spike in G-forces triggered an accident alert but actually turned out to be nothing more than the forceful slamming of one of the car doors. Nowadays, though, the company can reliably detect the telltale signs of accidents and other claim events from the incoming stream of black-box data in real time and react accordingly. With motor accidents, speed is of the essence, so being able to dispatch an ambulance instantaneously to the scene can be the difference between life and death: the ultimate in claims loss mitigation. This data is also useful in the inverse case, where insurers want to demonstrate that an accident has not in fact occurred (and that, therefore, an associated claim is fraudulent). The business case for IoT in claims is self-evident; as we recall from our Internet of Things post, a majority of our respondents selected claims as one of the areas best-placed to benefit from IoT. Further still, in our stats on claims, a majority of respondents believed that IoT would affect the claims department, and a majority also acknowledged having a high level of focus on claims loss mitigation. The immediate access that IoT gives to data, which does not have to be sought out and gathered but simply ends up in insurers’ back-end systems as a matter of course, is driving the development of automated, or straight-through, claims-handling. We found a reasonable incidence of automated claims-handling among our European respondents, whose claims departments also expressed a strong focus on customer experience. In the context of continually expanding horizons, we asked ourselves what the next stage of dynamic real-time insurance might be. Continuing this section’s particular focus on the auto line, we of course cannot ignore the amount of chatter around autonomous driving and what it means for the insurance industry. While some believe that autonomous driving may eliminate the auto line, the truth of the matter is that human error is not the sole source of catastrophic events on the road. "You don’t just eliminate all risks by making your vehicles autonomous," Halkett points out. "And that’s before you even start to think about what you’d need to do to have an entirely autonomous ecosystem. The environment is going to have to have so many significant changes before it can support current autonomous functionality, and the journey between now and 100% autonomous – even if that does happen, and it’s not certain it will – is not straightforward at all, and there will be lots of different forms of mobility between now and then." Halkett underlines rural and city driving as two key hurdles to be overcome on the way to full autonomy. For now and the immediate future, she believes there is food for thought enough in the intermediate stages between today’s conventional cars and the putative point of total autonomy in the future: "We’re going to have multiple different vehicles, some with ADAS systems, some with minor help for driving in there and some with barely more than a glorified cruise control, up to fully autonomous vehicles, all on the road at the same time with drivers behind the wheel with very differing levels of experience and expectations for that driving, too. "And what they are going to want from their insurance is a seamless product that just covers them for whatever they’re going to do – that is the reality of what the insurance industry is facing over the next 10-20 years." Instead of focusing exclusively on different degrees of autonomy within what is essentially a private ownership paradigm, Halkett believes insurers should also be looking laterally, at emerging mobility formats: "I would be looking at things like ride-sharing, things like shared ownership and different forms of vehicles, before we ever got to the point of complete autonomy," she concludes. Driving Connected Insurance Models Across the Continent Our exploration of Insurance IoT and telematics has so far leaned toward the U.K. But what sort of progress have new-age insurance models made across the continent as a whole? Another country that currently boasts plenty of IoT buzz is Italy. Our influencer Matteo Carbone, of the Connected Insurance Observatory, draws attention to the telematics leadership shown by the Italian market, citing the nation’s 2.4 million connected cars (as of the start of 2016), compared with 3.3 million in the U.S. and 0.6 million in the U.K. However, to compare IoT progress in blanket fashion across different national markets and insurance lines can be like comparing apples and oranges with pears and plums, given the uncategorizable variety of the problems IoT solves and the sheer number of different business models it enables. In Italy, for example, telematics boxes have been mandatory in all new cars for several years now, as a result of legislation aimed at reducing fraudulent whiplash claims. Such legislation does not currently exist in the U.K., but, as we have pointed out, the U.K. telematics market could be considered a front-runner in other respects.

"Italy is recognized as the most advanced auto insurance market at the global level for telematics. Leveraging the experience of the auto business, the country is affirming its position as a laboratory for the adoption of this new paradigm by other business lines." — Matteo Carbone, founder and director at Connected Insurance Observatory

Leaving aside the question of who leads and who trails, one thing is certain: that IoT-based solutions for insurance, both within the auto line and beyond, are only going to become more prevalent as the unit cost of sensors comes down and the demonstrable savings from the technology rise further. "The cost of technology is coming down all the time, and customer understanding is going up," Halkett says. "So the business model becomes easier and easier for a wider portion of the market. Consumers in other countries will more readily adopt these sorts of technology-led products, and insurance markets are becoming more sophisticated, as well." To continue with our auto focus, we can see how the advantages of in-car telematics – whether we are talking road safety, lower premiums or counter-fraud – are advantages for people of every age in every market, so there is no fundamental limit on the applicability of the technology.

"At some point in time, everyone is going to get connected. People will feel more empowered as they have a greater control on preventing risk events. This will be the origin of the new business model. In some countries, insurers don’t have a high level of trust because they are establishing conditions and changing prices, and the relationship is only one way. This is going to change, because in the future clients will have their data as an asset." — Cecilia Sevillano, head of partnerships, Smart Homes, at Swiss Re

This is not to say that the specific use cases will be the same everywhere. Halkett believes that the technology will bring about a bigger quantum leap, from a road-safety and world-health point of view, in those countries where infrastructure currently lags. "I think when you stand back and start looking at the benefits of telematics, there’s an awful lot that could be used in different markets for very different reasons," she says. "For example, if you look at the accident alert service and it tells you when someone has had a serious road accident – that would be so useful in rural areas in poorer countries which perhaps do not have the same infrastructure or the same emergency services as we do in the U.K. And to have that pinpointed alert would be even more valuable in countries where not everyone has a mobile phone and hospitals are perhaps less accessible." This is a classic case of high-end technology bringing the full benefits of insurance to the lower-end market, a recurring theme across our other regional profiles, as well; underdeveloped markets, especially when they lack the burden of legacy systems, have a chance to catch up with and even leapfrog more established markets. Margaris believes that this will be the case, not just for IoT adoption but for innovation more generally, in those parts of Europe that are currently less developed. "The truth of the matter is that in less affluent countries you will see a faster adoption of insurtech because it’s cheaper and more personalized than what the incumbent insurance players offer," he says. "Furthermore, I believe that the richer the countries, the less there is a need by consumers to adopt the cheaper business models that are offered by fintech and insurtech startups. So, therefore, I would say, the more developed the country, the longer it will take for innovative technology and business models to be adopted." Looking beyond Europe for other emerging markets with leapfrogging potential, Margaris points to Africa as a ready-made example, referring specifically to mobile technology: "Look at Africa, where with a normal phone – not even a smartphone – you can already transfer money, you can do anything," he comments. "Because with low incomes, you will find a greater need for innovation." This forms an unfavorable contrast with some established markets, and Margaris sees his native Switzerland as a case in point: "In Switzerland, where I live, there is a lesser need for innovative business models because people have enough money. Not everyone is well-off, of course, but in general, there’s such a comfort level that people say, the status quo works well, so we don’t need to go for fintech or insurtech solutions that are or might be cheaper or better." Margaris picks out insurtech and AI as two growth areas towards which sizeable investments are currently flowing, with London and Berlin being the premier European hubs. As for how the insurer-insurtech confrontation will play out, he points to the case of fintech – which has a couple of years’ lead on insurtech – as a likely indicator of how things will go here as well. See also: Global Trend Map No. 16: Regions   "If we look at fintech, which is in a more advanced phase than insurtech, you see a clear trend of cooperation, meaning partnership or outright buying by incumbents. I think this will also happen to the insurtech space," he explains. While this prognosis (cooperation winning out over competition) is generally positive for insurers, Margaris believes that in some ways insurers have it more difficult than banks: "Banking has the same issues, but banks are much more experienced with customer interaction on a daily basis, while, with insurance, usually you talk to an insurance agency once a year, like when you have a claim. So legacy technology and the insurtech industry as a whole is worrisome for the insurance industry, but it’s also an opportunity."

"Insurtech will offer new ways to harness IoT potential, with use of AI and machine learning. Through partnerships with these startups, incumbents can definitely accelerate their modernization. And this is a win-win situation as insurtechs have technological expertise and, in return, insurance leaders can provide them the one resource which they lack: money." — Minh Q Tran, general partner at AXA Strategic Ventures

This compromise between incumbents and new entrants, at least for now, stems from the fact that neither has all the ingredients to win outright. While we pointed out the two trump cards of insurtechs in Part I our our Europe profile (price and personalization), let’s now examine the advantages enjoyed by incumbent insurers. "Insurers have the customers, they have the money and they have the brand," Margaris says. "They can adapt quickly and say: OK, let’s take the cutting-edge technology, and we can make it happen." He gives the pharma industry by way of an analogy: "The pharma industry spends billions on R&D and innovation. At the end, most of them – the big pharma players – who have much more experience in this field of innovation, they buy biotech companies and integrate. Because what the big guys do well is selling and distribution. If you give an insurance company a great product, they know how to make the most out of the potential. Incumbents and insurtech startups have to play to each other’s strengths.’ Halkett agrees that traditional insurers have plenty to offer as part of any insurance model of the future, in particular the sheer volume of data, insights and expertise that they have at their disposal. However, she questions whether today’s incumbents are structured in such a way as to make the most out of these assets. There may need to be a move away from a centralized model toward more of an ecosystem play, with the insurer overseeing different components of a technology stack. Insure the Box is itself an example of this, being owned as it is by Aioi Nissay Dowa Insurance Europe, which is the ultimate bearer of risk and also has a long-standing partnership with automotive OEM Toyota.

"The insurtech discussion all too often centers on the premise that shiny new startups will win at the expense of the tired old incumbents. Many see the battleground between them being at the distribution end of the customer journey. For me, the insurtech opportunity extends all the way along the value chain." — Nick Martin, fund manager at Polar Capital Global Insurance Fund

At the end of the day, it is not a case of either/or with the partnership and insurtech-domination models, and we are likely to see some insurtechs eventually make it big alongside insurer-insurtech tie-ups. "It will happen. We’ve seen the Googles, Amazons, Facebooks of this world, and we’ll see the same thing occur in insurtech, whereby some will become huge players. However, I believe we will see more partnerships or acquisitions because it’s very hard to scale," Margaris concludes. As ever, you can read ahead straight away and gain access to all our global trends, key themes and regional profiles, by downloading your complimentary copy of the full Trend Map whenever you like.

It’s clear developments in artificial intelligence (AI), machine learning and other innovative technologies will have an impact on nearly every industry — including insurance and financial services. But what areas will be affected most in the near future, and how will they be affected? These are questions we explored with nearly 100 industry leaders during Denim Summit 2017 in Des Moines. When we posed the question, “What insurance process will be most affected by AI?” in a live poll, “underwriting” won with 39% of the responses. Following closely were “marketing and distribution” with 32% and “customer service” with 26%. Clearly, it’s not just one area that will be affected. Perhaps a better question is, “How will each area be affected?” Let’s take a look. Underwriting As author, speaker and futurist Blake Morgan writes in Forbes, AI has the potential to automate the entire underwriting process. Imagine a world in which bots scan a consumer’s social and digital profile to gather information and find trends and patterns. Someone who has a healthy lifestyle and steady job may be less likely to get into car accidents or rack up medical bills, which could lower insurance premiums. “AI can analyze data better than humans to more accurately predict each customer’s risk, thereby providing customers with the right amount of insurance and companies with protection from risky customers,” Morgan writes. See also: Strategist’s Guide to Artificial Intelligence   Marketing and distribution Hyper-personalization is the new norm in marketing and distribution. Brands are becoming dramatically more attuned to the needs and priorities of consumers and increasingly shaping their product offerings around rising lifestyle trends. Traditional blanket methods like cold calling no longer cut it in today’s uber-connected, digital age. AI can pull in consumer data to create a full profile that can be used to offer only relevant insurance products and remember a consumer’s preferences. Customer service According to a study by Oracle, nearly eight out of 10 businesses have already implemented or are planning to adopt AI as a customer service solution by 2020. There are two primary ways organizations are augmenting their customer service experiences with AI:

• Front-end, AI-powered bots, or conversational computer programs that interact directly with a customer without human interaction.
• AI-assisted human agents, or human customer service representatives who are supported by AI technology.

For at least the foreseeable future, chatbots won’t replace humans in customer service centers. They will, however, replace some of the tasks traditionally handled by people and, ultimately, enhance the experience for consumers. Customer service and experience expert Shep Hyken shares four reasons AI and chatbots are improving customer service in big ways:

• Chatbots never sleep.
• Chatbots won’t make you wait.
• Chatbots personalize the customer experience.
• Chatbots make friends and build relationships.

See also: Group Insurance: No Longer Overlooked   While AI’s value proposition may be clearer in some areas than others, it’s not hard to imagine a future in which nearly everything we do — in both business and in life — is somehow affected by AI.

Insurers move cautiously when embracing automation and other tech tools, and for good reason. As technology changes the insurance industry, challenges arise that appear in few other verticals.

Nonetheless, business automation is set to change nearly every industry, Deloitte argues, and insurance companies can certainly benefit from the efficiencies that automation will introduce. Data collection, analysis and decision-making, once the sole domain of humans, can now be improved by automation and AI. These tools range from simple time-savers, like auto-completion during data entry, to complex pattern recognition and data mining, which is transforming the way we analyze risk.

Further, automation offers numerous opportunities to improve efficiency, retain customers and reduce errors — but only when automation is enacted thoughtfully, as Corrine Jones notes at Property Casualty 360. Here, we look at five obstacles to automating agency operations, plus ways to overcome them.

1. Departments Aren’t on the Same (Digital) Page

Most insurers’ systems have difficulty talking to one another. Property and casualty insurance companies tend to operate like federations: departments that fall under the same umbrella, but that operate independently most of the time. This means their customer data gets hidden away in silos, and data-driven intel therefore cannot be shared among departments.

Insurers that continue to federalize this way miss key connections that can lead to improved coverage and more satisfied customers, says Dan Reynolds, editor in chief of Risk & Insurance. Breaking down silos can be a daunting task, but the reward can be well worth the effort — and automation can help.

When data can flow across departments, machine-learning algorithms can perform analyses across departmental lines. This lets the technology spot patterns and recommend solutions more easily, says Forbes contributor Bernard Marr. With access to a single cohesive system and its data, machine learning algorithms can handle a wide range of tasks, from spotting potential fraud to providing an interactive FAQ for customers.

2. Current IT Infrastructure Might Not Support an Ambitious Implementation

McKinsey partners Tanguy Catlin, Johannes-Tobias Lorenz, and Shannon Varney stress that one of the big lessons the insurance industry learned in 2017 was that tech-driven strategies aren’t a goal in and of themselves. Rather, executives need to think about what strategies make sense in a tech-driven world.

As such, organizations must ensure the capabilities of their IT teams are keeping pace with plans to implement automation. IT cannot take on a supporting role when implementing automation technology. IT must help lead the implementation, the McKinsey partners argue, and it’s up to the organization to position IT in a leadership role.

See also: How to Solve the Data Problem  

Here is how company leaders can position IT teams to assume that role:

• Hire tech leaders. IT teams leading changes need project leaders, agility coaches and scrum masters to guide their work.
• Promote a tech-friendly environment. Demand for tech talent is quickly outstripping supply in many industries, and insurance companies today must establish “an environment that attracts talent, promotes personal growth and offers a desirable and interconnected work environment and flexibility.”

3. Existing Processes Might Not Scale Quickly Enough

Many tech professionals who focus on insurance solutions, like EZLynx project manager Derek Armentrout, caution insurers to “start small” when considering the switch to automation. Starting small can benefit some companies.

But a small start can derail an entire automation project when “small” isn’t combined with “scalable.” Not only must the system be able to grow into the existing insurance company structure, but it must also be able to grow with that structure as the company expands. It must handle not only additional users but also more intensive calculations, recommends Richard Seroter.

Prasad Jogalekar and Murray Woodside in the IEEE Transactions on Parallel and Distributed Systems, provide a definition of scalability that is particularly apt for insurers: “Scalability means not just the ability to operate, but to operate efficiently and with adequate quality of service, over the given range of configurations.” A system that fails customers when overloaded is not scaling adequately to meet either the insurer’s or the customers’ needs.

As Seroter notes, working with a Software as a Service (SaaS) provider is one way to ensure scalability that meets both insurer and customer demand. That means choosing a provider that understands the connection among scalable platforms, automated activities and customer experience to maximize the value of automation in customer retention.

4. Workforce Obsolescence

McKinsey principal Sylvain Johansson and senior expert Ulrike Vogelgesang predict that automation will render 25% of all insurance industry jobs obsolete by 2025. Operations were hardest hit, with a 13% predicted drop in human employees, caused largely by automating everything from report generation to answering customer queries.

“That’s neither a negligible amount of job loss nor an unimaginably distant time frame,” Johansson and Vogelgesang wrote. “On the contrary, given the magnitude of these changes and the looming future, it’s important that insurers begin to rethink their priorities right now.”

Among the rethinking steps the McKinsey report recommends are:

• Retraining existing staff,
• Identifying imminent skills gaps and hiring to fill them, and
• Crafting employment value propositions that reflect a tech-heavy world.

Despite McKinsey’s predictions, the insurance industry will need to retain human workers for a number of key positions, Sabah Karimi writes at Great Insurance Jobs. Digital analysts, online marketers and other tech-minded positions will still demand the human touch. McKinsey explains that some insurance jobs are relatively safe from automation for the time being: Actuaries, for instance, are unlikely to see their jobs automated in the near future.

5. Existing Interfaces That Fail to Attract, Inspire and Retain Customers

Customer loyalty to their property and casualty insurer is a unique relationship. Because customers rarely interact with their insurers except in a crisis, building a relationship over time poses particular challenges.

See also: 3 Keys to Success for Automation  

Raising the difficulty level is the fact that today’s customers expect their product and service purchases to be easier than ever before. Web-based business has created an expectation of a seamless omni-channel experience and instantaneous results.

How can automation help?

• Improving self-service. Increasingly, customers who use the Internet to contact businesses do so with the expectation of self-service, Steve Wiser writes in an article at P&C 360. Automated systems streamline the collection of customer data. When incorporated with machine learning, they can automatically recommend the best additional coverage or next steps for the user.
• Better analytics. In the age of big data, Wiser notes, insurance companies that don’t gather and analyze customer information are missing an extraordinary opportunity — not only to manage their own risk, but to better connect with customers, as well. A personalized customer experience boosts customer ownership, and it’s a process that can be automated with the right tools.
• Improved ownership by packaging product lines. When Allstate first tried to switch to a commercial offering, the company found itself stalled by agents who needed to search out information before offering recommendations to customers — and a system that turned this process into a major stall, Kumba Senaar says. An automated system responded to these information requests more quickly, intuited what agents would need next and recommended additional coverages based on available data.

The result? Happier customers, larger purchases and more efficient agents. A win-win(-win) for Allstate.

The insurance industry has a long history of reclassifying “obstacles” as “opportunities.” When insurers partner with SaaS providers, they gain an ally that understands the connections between these major challenges and that can implement systems that address multiple challenges simultaneously.

Can you imagine what the insurance industry would be like without the internet? Many of us remember a time before email and online price comparisons, but we can no longer picture doing our jobs without the web. Hands down, the internet created a fundamental shift in how business gets done in the insurance industry. The Big Data Revolution There’s another revolution on the way in the form of big data. In fact, nine out of 10 companies told Accenture that big data will change how they conduct business on a scale comparable to how the internet changed the world from the 1990s on. Think about that—less than 20 years after the internet upended business, another movement is coming along that enterprises say will have a similar impact. What a revolution, right? Big data is already shaking things up in a big way. As a result, insurance organizations are racing competitors to employ analytical techniques and develop predictive models that will give them a competitive edge in everything from pricing risk and detecting claims fraud to developing products. But as companies develop these tools, many overlook the significant role that frontline employees will play in optimizing and adopting new big data techniques. Companies put as much as 95% of their big data budgets toward employing analytic techniques and developing models, ignoring employee training and knowledge building in the process. See also: What Does ‘Data-Driven’ Really Mean?   A Crucial Role for the Front Lines Frontline employees and managers working in traditional insurance fields like claims and underwriting will play a crucial role in working with data scientists to develop and implement effective solutions based on big data. Data scientists are not insurance experts and don’t necessarily have a firm grasp on how the industry operates. Without key intelligence from employees who know the insurance world, even the most elegant predictive modeling tools won’t have sufficient business impact. If data analytics is going to have a positive impact on insurers’ bottom lines, it stands to reason that a basic understanding of it will be good for your career, too. Insurance organizations across the country are forming teams to figure out the best way to integrate big data into their daily operations. Employees with big data know-how who can act as connectors between data scientists and traditional insurance operations will quickly find themselves in high demand. So just how can frontline employees and managers become data-driven decision makers? By following these three steps: 1. Focus on data literacy Data literacy means getting a handle on the terms and concepts behind data science and how they’re being used in the insurance world. So even if you can’t segment data into a classification tree, you should at least understand what a classification tree is and the basics of how it works. Big data is a fast-growing field with a lot of lingo and jargon. Check in with data scientists at your company to get a better idea of how your organization talks about data and where it is prioritizing using new data collection and analytical techniques. Focus your big data learning in these areas. 2. Sharpen your data mindset Once you understand what’s possible with data science and where your company wants to go, start thinking in terms of big data. When a problem comes up, think about what data you have at your disposal and how it could be analyzed to solve that problem. For example, can the data be analyzed in a new way to create different insights? Should additional data be collected from government agencies, customers, agents or adjusters? If some data point isn’t at your disposal but would help you do your job faster or better, chances are your boss wants to hear about it and your company’s data experts can help find a way to pull it. 3. Hone your data skillset After you’ve got the lingo down and think of data first when trying to solve problems, the third step is to position yourself as a data steward who can bridge the gap between data scientists and the insurance processes they’re working to optimize. You can still leave most of the modeling (and the crazy math!) to the data scientists, but you should understand big data to the point where you can advocate for specific analytical strategies and point out when processes can be improved. If you can fill this role, you’ll be an indispensable resource to your department and your organization as a whole. See also: Digital Playbooks for Insurers (Part 4)   This progression of developing a data mindset can be found on an organizational level as well. As big data expands and touches on more aspects of operations, entire organizations will have to think in terms of data analytics. Individuals who can develop their skills to satisfy their organization's growing appetite for data-based solutions will help those organizations find and implement strategic improvements while also growing their own expertise in the process. Interested in becoming a data-driven decision maker? Learn more about the Associate in Insurance in Data Analytics.

In many quarters, the above title could be fighting words! Because I can’t even watch an Olympic boxing match, much less an all-out fight, let me explain. For as long as I have been in the insurance industry, life and annuity insurers have been thought of as being significantly behind P&C insurers in terms of technology adoption and innovation. L&A insurers hung on to “build, not buy” strategies long after P&C insurers were advancing “buy” strategies. Technology providers with potentially cross-segment capabilities frequently did not even have a road map for selling to L&A insurers because, in their view, the front door was nailed shut! When STP (straight-through processing) started to become table stakes in personal lines operations, many life insurers seemed to feel that STP was simply a good motor oil. Every application for life insurance needed manual review. Let me enthusiastically state that those days are gone. L&A has most certainly caught up – and sometimes surpassed P&C – on many fronts. One of the measures of being “smart” is learning from prior mistakes. L&A insurers have had opportunities to learn, and not necessarily from their own mistakes. They have learned from P&C insurer mistakes, as well. See also: How to Insure the Gig Economy   SMA recently issued two research documents based on L&A insurer surveys:

• 2018 L&A BUSINESS AND TECHNOLOGY INVESTMENTS: Balancing Producer and Customer Requirements
• 2018 STRATEGIC INITIATIVES: Life & Annuities

Many exciting insights are revealed through the survey data. Not the least of which is that, in 2018, 43% of L&A insurers indicate they are transforming. Eight short years ago, only 13% indicated this to be the case. While this blog cannot hope to recap all the findings contained in the two SMA research papers, what did jump out quickly was the differences between how L&A has approached some things versus P&C – lessons learned:

• Digital isn’t all about fancy front ends and apps. When it became apparent that insurers needed to respond to the reality of a digital world, many P&C insurers ran headlong into introducing apps – most usually first notice of loss (FNOL) apps. Click to pay with a credit card on websites was another common feature. More examples could be cited. But to cut to the chase, the problem was that these digital capabilities stopped right at corporate walls, dropping into legacy technology and manual processes. They ceased to be digital. P&C insurers learned the hard way – through disconnected customer experience – that core modernization was necessary! L&A insurers have seemingly learned that lesson, with 55% having policy admin projects in 2018 – the No. 1 project overall.
• Love the hand that feeds you. The mantra across most all insurer segments, and strongly for P&C insurers, is customer experience. “We Love Our Customers” T-shirts are on every desk. This is absolutely critical, but for many P&C insurers this focus went to the exclusion of distributors. Agent and broker technology fell to the bottom of the top priority lists at many insurers. Given that agents and brokers have not disappeared, and, in fact, are critical as advisers for many consumers, this created a gap. L&A insurers do want to show some “love” to the distributors who play a critical role in customer acquisition and service. 55% of L&A insurers are executing distributor portal projects for both sales/submissions and service.
• It’s not all about BI. SMA research shows a historical trend among P&C insurers to invest in BI technology. In fact, in relation to other components of data and analytics such as dashboards, data and text mining and predictive analytics, 71% of P&C insurers indicate they are advanced users of BI tools. This is certainly good, but for many years P&C insurers have invested in BI and have not invested to the same degree – or at all – in other capabilities, which stalls advanced execution in this area. L&A insurers are investing in BI, as well, but, in 2018, 22% are investing in behavioral analytics, big data and AI. Getting into the game in these advanced areas is imperative, and L&A insurers understand that.

And there is one lesson that L&A insurers have learned from themselves:

• Building it yourself is a long and painful road. Over time, L&A insurers have attempted to tweak internally developed technology to step up to new market requirements. Given the rapidly shifting technology landscape, most insurers are not positioned to keep up, both from an IT capacity perspective and in terms of general skills levels. When it comes to emerging technology, 43% of L&A insurers are partnering with others that have emerging technology solutions. Only 29% continue to leverage their own capabilities.

See also: 3 Ways to Keep Training Fresh   Many exciting things are happening at L&A insurers in 2018. Both of the SMA research reports, which can be found here and here, provide insight into strategic initiatives and projects. Clearly, there are opportunity areas that are challenges, but there is little evidence that L&A insurers are content to support the status quo. Smart L&A insurers are looking over the fence to see what they can learn from P&C insurers. Over time, the opposite may be the trend!

We are seeing a flurry of advances in the insurtech space, be it product innovations, reimagined service experience or reduced premiums for customers. A question I often get asked is why personal lines in insurance is blazing ahead of commercial lines when it comes to innovation. The easy answer is to just follow the money, specifically the funding trail. Venture capitalists whose metric for early-stage startups is growth have rushed to personal lines as it is easier to show the volumes. Personal lines insurtech startups have focused on the distribution side of the problem – lowering the premium to increase the volume of transactions. Their lever for this rapid growth is a slick UI and a digital broker; betting on increasing throughput, consequently the adoption. In the subsequent rounds of funding, when the motive of the investors shifts from growth to profit, insurtech companies will realize that distribution is only one part of the equation, and not the core of the problem. Insurtech companies are amazing, and they all solve a part of the problem. However, to solve systemic problems, companies need to attack improvements in the loss ratio (i.e. the product problem, not the distribution problem). More than the profitability of the insurer, the ripple effect across the insurance industry and other adjacent industries is massive (for example, think of the impact on workplace safety as opposed to underwriting workers’ compensation). So, for systemic industrial change, I think the commercial industry is better-placed than personal, even though it will take longer. What Does the Anatomy of a Commercial Insurtech Unicorn Look Like? Like all quick analyses, I look at this in two dimensions:

• The opportunity
• The execution needed  to deliver on the opportunity

Both of these point to commercial as a better option. Opportunity Driven by Sharing Economy The sharing economy is drastically reducing asset ownership, with car ownership in urban areas the most-cited example. This is a loss for personal auto and a gain for commercial auto (the car is going to be a computer, and cyber risk from the manufacturer will likely become the highest coverage). This trend exists in other areas, including home ownership, renting of equipment, physical storage, cloud computing etc., but it is not talked about as much. The second shift I see happening is a fundamental change in the product structure from static to dynamic. Across all lines, the change in what you need to know upfront and what you will know throughout the life of the policy will change. The usage-based policy (sort of pioneered in parts in personal auto) will start to become the norm in commercial, despite having only a minuscule footprint currently (remember, these are exponential changes, and the initial doublings are not noticeable – think of the 0.01 megapixel camera becoming a 0.02 megapixel camera). See also: 3 C’s for Commercial Brokers in 2018   Executed With IoE and Machine Learning Let’s have a look at how you can execute on these trends: First, the current 1.5 to two touch points a year with the carrier become 365 touch points at least. The key touches in this sense are not human touches but data-driven touches. Both the upfront and post-bind data, the certainty and access of data on commercial is better, with access to personal lines data prone to consent due to privacy reasons (at least until DNA sequencers take privacy out of the equation). Meanwhile, in commercial, even if you were to replicate the existing forms (which you should NOT!) you can probably find 50% to 60% of the data -- general company, financials, locations and parts of workers’ compensation, commercial auto, general liability and the directors and officers -- to be as little as their names and addresses. However, under a usage-based policy, even knowing 100% of the upfront static data is not enough; it is the dynamic IoE (Internet of Everything) data that shifts the paradigm. These IoE solutions that I talk about have already reached a level of maturity in industries such as mining, manufacturing and construction. They have been deployed in cutting machines, heating/cooling equipment, cranes, thermal cameras, traditional cameras, forklifts, trains and guided vehicles for years. This has enabled a level of sophistication in IoE solutions, which has data from running mission-critical systems (PLCs, data loggers, historians, etc.) But why would a manufacturer or a construction company give a carrier this data? Come to think of it, the true financial incentives to increase safety and decrease risk have never existed before! This has to come in to create a win-win scenario between the insured, its employees and the carrier. Despite the commercial insurtech not taking as much premium upfront, it will get to unlock many other opportunities, simply due to the data and touch points it has. As you may have realized by now, other than driving loss prevention, what a commercial insurtech really does is switch the insurance from someone/something like the insured (“broad risk pools”) to someone exactly you (i.e. “pool of one”). One can argue this can be achieved on the wellness side with device data, but the industrial automation data has been collected and proven across many industries for 20 years now. The wellness data is just starting. Disintermediation – Stating The Obvious So far, we have got to the shape of this active, personal commercial insurtech unicorn. However, it would be remiss of me to not briefly talk about its distribution structure. A traditional carrier spends around 30%-plus of direct written premium (DWP) between expenses and commissions to “touch” an insured 1.5 to two times a year. Now, if you want to be able to continuously “touch” an insured, both the acquisition, retention and renewal structure has to be re-imagined bottom up for it to scale. One thing is for sure that in a world of IoE and machines, “human” intervention is minimal; people simply will not be able to handle the volumes and variety of data. So, there is no chance a commercial insurtech unicorn will be intermediated. None of this is just gleeful optimism; I will admit to there being regulatory hurdles. Despite having regulatory “sandboxes” setup, it is a massive step up for traditional regulators who are grounded in easy-to-regulate forms and structured data to switch to on-the-fly decisions, price adjustments made by machine learning algorithms and data flowing from the IoE devices. I see the legal and regulatory skills needed to maneuver the commercial insurtech company to being a unicorn to be as big, if not bigger, than the technology and algorithmic skills. This cannot be underestimated. My hope here is that ultimately any regulatory body remembers who they are regulating for: the insured. See also: New Era of Commercial Insurance   To Sum It Up You can see an outline of what a potential commercial insurtech unicorn would look like. Instead of being reactive, impersonal and intermediated, the successful company will likely target loss ratio improvement with active, personal service, powered by a large network of data partners, commercial IoE partners and machine learning partners. To operate at a global scale, this unicorn will have to have low cost per digital touch, and hence it will likely be disintermediated. There is already a large (and growing) opportunity for an insurtech to target major commercial segments in commercial packages, commercial auto and workers’ compensation. The solution options are massive, but the problem space is even bigger. As a word of caution, it isn’t just about technology here; the ability to carefully guide the company through the many regulatory hurdles is also essential. I look forward to seeing the first commercial insurtech unicorn. I wonder who it will be?

Around two years ago, the term "chatbot" shot into our vocabulary and onto the agendas of CIOs and CMOs everywhere. The idea that a customer could simply "chat" with a robot any time, anywhere made so much sense — or did it? Any technology solution or product implemented without a clear problem in mind is just wasteful. And it is this lack of planning that put chatbots on a fast path to nowhere in many companies. Two and a half years ago, there were only a handful of chatbot providers. A year ago, there were thousands. Any remotely adept coder could whip a bot together in a few hours and, surprise, surprise, VCs went in hot pursuit of companies to fund. Fast forward to today, and we’re constantly hearing the phrases “our chatbot proof of concept was not what we hoped,” or “we tried chatbots, and they didn’t work” rolling off the tongues of those same CIOs and CMOs. But we’re not surprised. In fact, we welcome the demise of pointless technology. When we last checked, Facebook Messenger had more than 100,000 chatbots. Many of them are failing to impress, leaving users underwhelmed and frustrated. See also: Chatbots and the Future of Interaction   Automation needs a purpose So, is this the end of chatbots? It certainly is the end of companies creating chatbots for the sake of having a chatbot. But it is the beginning of a major technology shift, a quasi-revolution called AI-based automation, and chatbots certainly have an important role to play. Companies waste resources when they implement new technologies without first establishing an actual problem to solve. The same theory applies to automation, AI and chatbots. For chatbots to survive, they have to solve a business problem. Period. Executives must clearly define this problem and distill it into real use cases that have true ROI or Net Promoter Score implications — meaningful implications. As soon as a team clearly maps out the use cases, the case for automation comes next. Can the company solve this problem by removing the human element in the back end? If so, there will undoubtedly be a cost benefit to the company. A smart design here will allow for escalation to human agent in the (let's hope) shrinking contact center. Once the higher-ups give automation the green light, the company must spin up myriad other technologies to create an effective system that solves the problem in the long term. As an example, if the business problem were around customer service and the use case were automating bill pay, then payment gateways, an asynchronous messaging channel, an authentication system, encryption and privacy layer, feedback loop, API bridge into the billing system and others would need to work in unison to provide a complete solution. Rethinking the word ‘chatbot’ You’re now probably wondering where the chatbot comes in. Well, therein lies the point of this article: A chatbot only has a role to play if it delivers utility to the customer. In the case of bill pay, the visual experience the bot presents to a consumer is in the form of a chat. Developers program this conversation inside the chatbot using either decision trees or natural language understanding. See also: How Chatbots Change Open Enrollment   If I had one wish for this industry, it would be that we get rid of the term “chatbot” and instead call this user interface built around conversations a CI, or conversational interface. CIs done properly, with a true business problem in mind, will reach deep into the back end through a persistent and secure messaging channel, allowing the customer to do business — any time, anywhere and, most importantly, happily.

Our survey of leading carriers shows that insurers are increasingly looking to integrate data scientists into their organizations. This is one of the most compelling and natural opportunities within the analytics function. This document provides a summary of our observations on what insurers’ analytics function will look like in the future, the challenges carriers are currently facing to make this transition and how they can address them. We base our observations on our experience serving a large portion of U.S. carriers. We supplemented our findings through conversations with executives at a representative sample of these carriers, including life, commercial P&C, health and specialty risk. We also specifically address the issue of recruitment and retention of data scientists within the confines of the traditional insurance company structure. The roles of actuaries and data scientists will be very different in 2030 than they are today Actuaries have traditionally been responsible for defining risk classes and setting premiums. Recently, data scientists have started getting involved in building predictive analytics models for underwriting, in place of traditional intrusive procedures such as blood tests. By 2030, automated underwriting will become the norm, and new sources of data may be incorporated into underwriting. Mortality prediction will become ever more accurate, leading to more granular (possibly at individual level) premium setting. Data scientists will likely be in charge of assessing mortality risks, while actuaries will be the ones setting premiums, or “putting a price tag on risk” – the very definition of what actuaries do. Risk and capital management requires extensive knowledge of the insurance business and risks, and the ability to model the company’s products and balance sheet under various economic scenarios and policyholder assumptions. Actuaries’ deep understanding and skills in these areas will make them indispensable. We do not expect this to change in the future, but by 2030, data scientists will likely play an increased role in setting assumptions underlying the risk and capital models. These assumptions will likely become more granular, based more on real-time data, and more plausible. Actuaries have traditionally been responsible for performing experience studies and updating assumptions for in-force business. The data used for the experience studies are based on structured data in the admin system. Assumptions are typically set at a high level, varying by a few variables. By 2030, we expect data scientists to play a leading role, and incorporate non-traditional data source such as call center or wearable devices to analyze and manage the business. Assumptions will be set at a more granular level – instead of a 2% overall lapse rate, new assumptions will identify which 2% of the policies are most likely to lapse. See also: Wave of Change About to Hit Life Insurers Actuaries are currently entirely responsible for development and certification of reserves per regulatory and accounting guidelines, and we expect signing off on reserves to remain the remit of actuaries. Data scientists will likely have an increased role in certain aspects of the reserving process, such as assumptions setting. Some factor-based reserves such as IBNR may also increasingly be established based on data-driven and sophisticated techniques, which data scientists will likely play a role in. Comparing actuarial and data science skills Although actuaries and data scientists share many skills, there are distinct differences between their competencies and working approaches. PwC sees three main ways to accelerate integration and improve combined value 1. Define and implement a combined operating model. Clearly defining where data scientists fit within your organizational structure and how they will interact with actuaries and other key functions will reduce friction with traditional roles, enhance change management and enable clearer delineation of duties. In our view, developing a combined analytics center of excellence is the most effective structure to maximize analytics’ value. 2. Develop a career path and hiring strategy for data scientists. The demand for advanced analytical capabilities currently far eclipses the supply of available data scientists. Having a clearly defined career path is the only way for carriers to attract and retain top data science (and actuarial) talent in an industry that is considered less cutting-edge than many others. Carriers should consider the potential structure of their future workforce, where to locate the analytics function to ensure adequate talent is locally available and how to establish remote working arrangements. 3. Encourage cross-training and cross-pollination of skills. As big data continues to drive change in the industry, actuaries and data scientists will need to step into each others’ shoes to keep pace with analytical demands. Enabling knowledge sharing will reduce dependency on certain key individuals and allow insurers to better pivot toward analytical needs. It is essential that senior leadership make appropriate training and knowledge-sharing resources available to the analytics function. Options for integrating data scientists Depending on the type of carrier, there are three main approaches for integrating data scientists into the operating model. Talent acquisition: Growing data science acumen Data science talent acquisition strategies are top of mind at the carriers with whom we spoke. See also: Digital Playbooks for Insurers (Part 3)   Data science career path challenges The following can help carriers overcome common data science career path challenges. Case study: Integration of data science and actuarial skills PwC integrated data science skills into actuarial in-force analytics for a leading life insurer so the company could gain significant analytical value and generate meaningful insights. Issue This insurer had a relatively new variable annuity line without much long-term experience gauging its risk. Uncertainty about excess withdrawals and rise in future surrender rates had major implications for the company’s reserve requirements and strategic product decisions. Traditional actuarial modeling approaches were limited to six to 12 months of confidence at a high level, with only a few variables. They were not adequate for major changes in the economy or policyholder behavior at a more granular level. Solution After engaging PwC’s support, in-force analytics expanded to use data science skills such as statistical and simulation modeling to explore possible outcomes across a wide range of economic, strategic and behavioral scenarios at the individual household-level. Examples of data science solutions include:

• Applying various machine learning algorithms to 10 years of policyholder data to better identify most predictive variables.
• Using statistical matching techniques to enrich the client data with various external datasets and thereby create an accurate household-level view.
• Developing a simulation model to simulate policyholder behavior in a competitive environment as a sandbox to run scenario analysis over a 30-year period.

Benefit The enriched data factored in non-traditional information, such as household employment status, expenses, health status and assets. The integrated model that simulated policyholder behavior allowed for more informed estimates of withdrawals, surrenders and annuitizations. Modeling “what if” scenarios helped in reducing the liquidity risk stemming from uncertainty regarding excess withdrawals and increase in surrender rates. All of these allowed the client to better manage its in-force, reserve requirements and strategic product decisions. This report was written by Anand Rao, Pia Ramchandani, Shaio-Tien Pan, Rich de Haan, Mark Jones and Graham Hall. You can download the full report here.

I don’t think any of us would dispute that the insurance industry is facing an inflection point, with changing market conditions, emerging technologies and startup insurtech companies. In an environment in which both personal and commercial lines of business are affected by a digitally empowered consumer, many insurers are rethinking their IT infrastructures, distribution networks and communication strategies. Yet recent coverage in the media paints a different picture for smaller insurers trying to achieve transformative change.

I was struck by a comment made by Tom Benton, VP of research and consulting at Novarica, in an article called “Smaller insurers lean on partners to navigate disruption.” Benton argues that, while all insurers continue to struggle with limited IT resources, capabilities and access to specialized skills while facing increased demand for operating efficiency, smaller insurers are at a disadvantage.

“Most insurers are focused on three things,” he said, “running IT for the organization, projects that help the organization grow and transformational projects. Most small carriers don’t have the budget or resources (including talent) to apply to transformative projects.”

See also: How Small Insurers Can Grow  

While it’s true that small property and casualty and commercial workers’ comp carriers, municipal risk pools, captives and self-insured groups may be vulnerable to more rigid budgetary concerns than their larger Tier 1 and Tier 2 counterparts, I’m not convinced that transformation is unattainable to them.

Although smaller carriers may not have the desired resources, they can move faster than larger organizations on certain initiatives. Smaller carriers don't have to jump through all the organizational hoops usually present in a larger company. Plus, smaller carriers usually have a culture that embodies taking risks, getting faster approvals and moving into a pilot much quicker than larger insurers.

Let’s look at the agility of a smaller carrier and add the notion that these employees tend to “wear many hats” (often running IT operations while functioning in another capacity within the organization). Here, choosing the right technology partner is critical, and long-term issues must be considered when making decisions on platform, systems and applications.

For example, Maine School Management Association (MSMA), a state-wide non-profit federation that administers various insurance programs to the state’s school systems, replaced a decades-old process that involved spreadsheets and manual entry, with cloud-based insurance management software. The decision, made to provide secure and efficient online renewals of property and casualty (P&C) coverage for its 98 member school districts, is transforming the entire renewal process, reducing renewal process time and streamlining members’ ability to respond. With just 23 employees, MSMA is an example of an insurance organization that has achieved transformational change due to its commitment to successful risk-taking, a calculated plan to work exclusively with “best in class” vendors that specialize in serving public entities and a culture that is committed to innovation-fueled growth.

Even less successful experiences serve to inform future operations. We aren’t perfect, and firms that proclaim, “not us,” or “we won’t have those issues,” are either disingenuous or naïve.

See also: Have Insurers Lost Track of Purpose?  

The call for transformational improvements is upon us, with pressure to innovate using technologies such as cognitive computing tools, machine learning, predictive analytics, robotics processing automation, chatbots and natural language processing. Rather than be at a disadvantage, smaller insurers are embracing a new level of confidence that maintains that transformation is not only possible, it’s realistically attainable.