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.

When insurtech sprang to prominence in 2015, most startups focused on personal lines disruption. Our August 2016 infographic showed that 75% of insurtechs were targeting personal lines and that 56% were focusing on distribution. Most corporate and specialty insurers concluded that insurtech presented no threat and only limited opportunity and continued with business as usual. That was then, and now is now. Insurtech now matters for corporate and specialty insurers. (Incidentally, we agree with the point Adrian Jones, head of strategy and development at SCOR, makes in this excellent article: it’s a myth that insurtech has been around only since 2015. We do, however, believe that there has been a new thrust since then, harnessing the pace and power of new technologies.) 2015-2017: The first wave of insurtech It is not surprising that insurtech started as a personal lines disruption play. Entrepreneurs, buoyed by what was happening in fintech and other industries, saw huge opportunities to make insurance more customer-centric based on their own experiences. Entrepreneurs wanted to simplify insurance (e.g. Sherpa), offer more tailored propositions (e.g. Bought By Many) or change the whole insurance paradigm (e.g. Guevara). But the truth is that insurance has not been disrupted over the last three years, and it’s hard to see that this is about to change. As Adrian illustrates in another article, even the most prominent disruptors in the U.S. (Lemonade, Metromile and Root) are finding the going tough and burning through a lot of capital, whether directly or via  reinsurance. See also: Digital Playbooks for Insurers (Part 1)   We argue in our insurtech Impact 25 paper (February 2018, page 7) that many distribution insurtechs are not scratching sufficiently major customer itches to be worth the switching cost for those consumers. As a result, the perceived potential is worrying incumbents far more than their actual performance to date. 2018: The second wave of insurtech If we were to update our insurtech landscape infographic, supplier insurtechs would feature much more prominently. These companies are developing technology (or, as in the case of German insurtech Kasko, have repurposed consumer propositions) to help incumbent insurers, reinsurers and brokers operate more effectively. Supplier insurtechs have found getting traction in consumer markets tough and are developing technologies or techniques that they can sell to the established insurers. Many of these companies are targeting corporate and speciality underwriters. This is perhaps not surprising – at least not from the U.K. perspective. U.K. personal lines insurers have been investing in pricing capabilities, efficiency and fraud analytics for years as competition has become cutthroat. They are mostly advanced in many areas. This is in strong contrast to corporate and specialty classes, where much underwriting is still judgment-based, processes are manual and underwriters and risk managers are resigned to poor data quality. As such, we believe that many of the Impact 25 Members can be valuable for corporate and specialty underwriters in 2018. Some examples are below:

• Insurdata was set up by ex-RMS executive Jason Futers and helps (re)insurers obtain more accurate building location information. This is helpful for underwriting (e.g. commercial property, reinsurance portfolios), risk management and portfolio reviews.(website, Impact 25 two-pager)
• Risk Genius uses AI to read policies and understand coverage. Founder Chris Cheatham noted recently. “[My trip to] London was amazing. It took two days for one very big learning to sink in: Underwriters in Europe are empowered to manuscript with little or no formal approval process.” His business allows corporate insurers to get a better understanding of their exposures.(website, two-pager)
• Flock is an analytics platform currently used to price drone flights dynamically, for example taking into account hyper-local weather conditions and locale of flight. The technology’s ability to process big data quickly could be helpful for commercial IoT propositions, for example. (website, two-pager)
• Cape Analytics and Geospatial Insight generate underwriting or claims insight from aerial imagery. This is useful, for example, in natcat losses when (re)insurers need to assess their exposures quickly. (Cape Analytics: website, 2-pager; Geospatial Insight: website, two-pager)

See also: Have Insurers Lost Track of Purpose?   What it means for corporate and specialty insurers Technology is not, of course, a new phenomenon in corporate and speciality insurance. However, the speed of proliferation of new vendors (of both technology solutions and data sources) is arguably unprecedented. It challenges the corporate clock speed of most incumbents and will present opportunities to successful adopters to tilt industry profits in their direction. But identifying the correct response is challenging for incumbents and, as we argue in our Impact 25 paper, there is no single, correct course of action. Choices that need to be made broadly fit into three categories:

• Strategy: Should we focus on customer experience/proposition or efficiency?
• Technology: Do we build or partner or buy? If we partner, how do we create and protect differentiating IP?
• Execution: Should we innovate within the business or in dedicated teams? What structures and processes do we need?

These questions – among others – need to be answered to ensure an effective corporate response.

Another week, another controversy on autonomous vehicles. The latest involves the crash that killed a Tesla driver on March 23 while the car was in Autopilot mode. After being silent for several days, Tesla on Friday wrote a blog post assigning much of the blame, if not all, to the driver. In a blog post, the company said he had received several visual warnings and one audible warning that he needed to retake the wheel but had not done so. The post said the driver had five seconds to react and 150 meters of unobstructed view of the concrete divider that he crashed into.

In the wake of these recent crashes, I think there will be a flight to quality, which doesn't bode well for either Uber or Tesla in the short term. Uber, which has been known as a cowboy, including in driverless cars, has been scrambling since a car in autonomous mode struck and killed a pedestrian in Tempe, AZ, on March 18. It's not clear to me that there's a fundamental flaw in Uber's technology, but the company seems to be moving too fast. It will have to scale back testing and increase security (there was just one safety driver, rather than the usual two, in the car that struck the pedestrian). Uber will also have to pound away at testing a lot more, both on simulators and in the real world, before putting any significant numbers of cars on the road.

Tesla may need to reboot even more. It claims it can get to full autonomy without using lidar, which the rest of the industry is treating as essential. The rotating lidar bulbs on the top of driverless cars surely don't fit the sleek design esthetic at Tesla—maybe lidar becomes acceptable once it can be delivered via just a couple of chips built into bumpers. But solid-state lidar is at least a few years away from being possible in production models, and I don't believe that Tesla can get close to full autonomy in the meantime. Tesla also needs to back off its belief that drivers can easily toggle between autonomous mode and full engagement with driving. Others aren't counting on drivers, who seem to need 10 seconds or more to reengage, and I think Tesla must change its thinking.

Companies taking a more deliberate approach will have to contend with the negative publicity from the recent Uber and Tesla fatalities. But I see no signs that Google's Waymo or GM's Cruise are backing off their plans to launch ride-sharing AV services this year and next in limited, well-mapped environments.

Vivek Wadhwa published an article with us that argues for a moratorium of at least two to three years on any testing around pedestrians. He owns a Tesla and is comfortable using it in Autopilot mode on highways but says local streets are too complicated for now.

Our own Guy Fraker conducted a thought-provoking webinar on the myths and realities of autonomous vehicles that you can listen to here. He provides great perspective both on how far we've come and on how far we still have to go.

Have a great week. (And drive safely.)

Paul Carroll
Editor-in-Chief

Blockchain is poised to change the future of insurance by creating new insurance products, services and business models, as well as promoting new ways of improving control, measuring and pricing risk. It stands to enhance client engagement, while reducing costs, improving efficiency and ultimately expanding insurability. One example of how blockchain can be applied to insurance is found in companies such as Ethereum, a decentralized platform that runs smart contracts. In this scenario, programmable logic can execute actions once pre-determined conditions are met. This all takes place without the intervention of a third party, providing insurers with the ability to digitize processes even further. Insurance industry experts believe that blockchain could herald a new dawn to the industry by introducing improvements and efficiencies related to higher levels of accountability and transparency. Not only will it facilitate new digital currencies, but it may also be used to introduce new products to the insurance market, mitigate risks and fraudulent claims, lower costs, restructure back-office operations and provide easier and enhanced data access to both insurers and the insured. See also: Blockchain Transforms Customer Experience An insurance report by Ernst & Young reveals that blockchain technology could become important in “detecting fraud and eliminating error and negligence by offering a devolved digital depository to help in verifying independently the legitimacy of policies and claims and, of course, customers.” Such a common platform, which offers a mutual record of truth, could assist insurance firms in saving a lot of time and money while improving operational efficiencies. Studies reveal that fraud costs insurers in the U.S. and Europe alone an approximated $60 billion in annual losses. This is because 65% of these fraudulent claims often go unnoticed. But this fraud may become a thing of the past with blockchain technology. This is because it is fraud-proof, indisputable and time-stamped and offers the highest security of identities, thereby offering help in minimizing and easily detecting insurance fraud and providing great value to the industry. Even though blockchain technology is still embryonic, a number of established insurance firms have joined the bandwagon and are exploring the space. For instance, Shenzhen-based Ping An and Hong Kong-based AIA recently partnered with R3, a fast-growing blockchain group based in New York City. The French bank Caisse des Dépôts recently rolled out a blockchain market initiative in partnership with a host of insurance companies such as AXA, Aviva, MAIF and CNP Assurances. The goal is to identify and encourage the development of opportunities for each of the partners by exploring these technologies. See also: Insurtech in 2018: Beyond Blockchain   As more and more of these technologies get up and running in the insurance industry, blockchain’s largely unexplored potential will begin to unfold. This trend, in particular, is set to have substantial impacts on the future of the insurance industry, but it will take time for them to be fully realized. So far, there hasn’t been as much movement in North America, but that’s likely to change soon. If adoption and innovation with blockchain continues at its current rate, there’s no doubt that this technology will play a significant role in shaping the future of the industry sooner rather than later.

A self-driving Uber car fatally crashed into a pedestrian in Tempe, Ariz., last month, tragically illustrating the fears that some of us have long held about the dangers of these technologies. The woman appeared from a darkened area onto a road, and the police said the accident would have been hard to avoid even with a human driver behind the wheel. Yet this is not the way it was supposed to be: Autonomous cars were supposed to be better than humans in exactly such situations. The lidar, radar and cameras that self-driving cars employ are designed to have advanced vision, and their computers have the ability to make instantaneous decisions. Yet the crash suggests that the technology may not be ready for prime time. The race among technology companies to be the first to put these cars on the road is having fatal consequences. Uber’s self-driving vehicle system appeared to have several flaws, according to my colleague Raj Rajkumar, who heads Carnegie Mellon University’s self-driving laboratory. As he explained in an email, “What we saw on the video indicates several trouble spots with the Uber approach, design and software capabilities. There is a serious mismatch between its sensor configuration and actual usage contexts. For example, even though Uber’s self-driving vehicle has multiple cameras, their usefulness at nighttime is extremely limited at best and add no value during those dark hours when they do operate the vehicles.” See also: The Unsettling Issue for Self-Driving Cars   Rajkumar also didn’t let the operator off the hook. “The operator’s role is to act as the safety backup — when the technology fails, (s)he is required to step in. The operator in this case was distracted for a shockingly long duration of time, which culminated in the death of the pedestrian,” he wrote. The reality is that self-driving cars are far from being able to coexist with humans on local roads. Both sides are learning. It is one thing for a human to put the car into autopilot on a highway and another to navigate city streets onto which adults, children and animals may suddenly wander. Autonomous cars need to be relegated to special tracks and highways for at least two or three more years, until they can deal with such contingencies. To be clear, I am not an opponent of the technology. I own a Tesla Model S and am comfortable with letting the car take control of the wheel on highways — despite the fatal Tesla crash that occurred in 2016. But using autopilot on local roads is as dangerous as using cruise control on local roads: You just shouldn’t do it. Toyota did the wise thing by halting testing of its autonomous cars on local roads. All other makers of autonomous cars need to do the same. Or governments may need to call the race off by declaring a moratorium until the vehicles to be road-tested demonstrate certain minimum capabilities. See also: The Evolution in Self-Driving Vehicles   Self-driving cars may bring profound improvements in our lives and slash accident and fatality rates, saving millions of lives. They could reduce the need for ownership, because we would be able to share them, and they could deliver incontrovertible social benefits, offering the disabled on-demand personal drivers. People living in the country could finally gain access to transportation services that put them nearly on par with their city cousins. Crossing or walking next to roads may cease to be a high-risk activity. And, eventually, these autonomous systems could replace humans at the steering wheel, just as horseless carriages replaced the horses. But injudiciously rushing into autonomous driving will lead to unnecessary accidents, justifying calls to outlaw it and halting progress of the technology. It is better to proceed cautiously with it and ensure that the rewards outweigh the risks.

The Dec. 22, 2017, Tax Cuts and Jobs Act is the most sweeping piece of U.S. tax legislation in more than 30 years. It will have wide-ranging impacts on insurers’ business, including corporate structure, regulatory capital and products. We describe in detail elsewhere the act’s technical details and will continue to address what insurers need to do to comply with this new legislation as its implications become clearer. On a broader level, and as we describe below, the act has clear operational impacts that go far beyond compliance. The reduced corporate tax rate benefits most insurers but leads to long-term strategic questions The GOP-sponsored tax reform slashes the corporate tax rate from the headline 35% rate to 21%, bringing the U.S. rate closer to the median when compared with other industrialized nations within the Organization for Economic Cooperation and Development (OECD). A 21% rate on taxable income will increase after-tax profit margins for legacy businesses and capital investment scenario planning considerations. For the life and P&C industries, this should boost margins on legacy books of business and increase premium revenue if job and economic growth prospects materialize as the act’s sponsors claim. In the near term, insurance companies are focusing on financial statement management considerations, such as how to adjust deferred tax assets and liabilities, required changes to loss reserving methodologies and computations of taxable income and reporting any untaxed foreign earnings and profits for the 2017 year-end statement as part of the repatriation “toll tax.” However, insurers shouldn’t ignore longer-term considerations related to scenario and strategic planning in response to the rate cut. The corporate tax rate reduction should motivate insurers to consider how to deploy additional after-tax capital. Scenario planning should first evaluate the overall net impact of tax reform changes and quantify the amount of capital that could be retained by balancing the lower corporate rate against a tightening of deductions and the changes to taxable income computations. Modeling potential implications starting in 2018 and then forecasting mid-term planning and longer-term strategic initiatives can provide management different views to assess the implications of redeploying additional capital. See also: Tax Reform: Effects on Insurance Industry?   With more expected income to deploy, companies should consider the investments they can make to support operations, growth and productivity. Examples include enhancing IT infrastructure, investing in new product lines or capabilities, pursuing opportunities to expand brand presence through acquisition and providing more free cash flow to policyholders and shareholders. U.S.-based insurers may be more globally competitive The sponsors of the Tax Cuts and Jobs Act claim it will make the U.S. tax system more attractive on a global scale. The shift to a territorial tax system, complemented by the reduction in the tax rate on non-U.S. profits, is likely to help at least some U.S.-based insurers be more globally competitive. The previous U.S. tax system put U.S.-parented insurers at a tax liability disadvantage compared with foreign-parented firms because all non-U.S. income was taxed at the higher domestic corporate rate. Going forward, foreign subsidiaries of a U.S. company no longer must pay a 35% U.S. rate, which previously may have deterred companies from expanding outside the U.S. or repatriating earnings. Because U.S. parent companies are no longer taxed on worldwide income, the M&A market could see an uptick with more incentives-based buyers because of the deduction for affiliated inbound transactions from foreign subsidiaries. In effect, global insurers may be more inclined to consider U.S. inbound investment to seek a lower tax domicile. Moreover, with the discontinuance of a higher U.S. tax on all foreign profits, U.S. outbound expansion also could become more common. The base erosion and anti-abuse tax applies to select offshore tax practices In alignment with the territorial tax system theme, the act includes a base erosion and anti-abuse tax (BEAT) that imposes a new tax on certain base erosion payments made by a U.S. taxpayer to a foreign affiliate. A minimum tax of 10% (5% in 2018) will be assessed when base erosion payments exceed a modified taxable income amount. The legislation explicitly mentions reinsurance payments as a base erosion payment, and thus is likely to significantly affect reinsurance arrangements between U.S.-domiciled entities and affiliated entities located outside of the U.S., such as in Bermuda and the Cayman Islands. The classification of reinsurance payments as “base erosion payments” will cause companies to take a look at affiliated reinsurance arrangements in offshore jurisdictions in 2018 and 2019. Companies that use offshore affiliate transactions to manage capital will need to reassess their costs and benefits. The BEAT minimum tax likely will motivate ceding companies to reconsider quota shares with affiliates altogether, reinsure with offshore third party reinsurers instead of affiliated captive reinsurance arrangements, retain or reallocate more risk to the U.S. or elect to be taxed as a U.S. corporation. Critics of the BEAT claim it’s a form of double taxation on non-U.S. insurance and reinsurance companies; domestic U.S. insurers seem to favor the provision, because it will discourage offshoring of profits by non-U.S. companies to tax havens. Regulatory and industry efforts have been beginning to push for changes in this direction, but now the legislative tax impact, which took effect beginning Jan. 1, 2018, creates an immediate deadline and thus a sense of urgency to review existing arrangements to assess for options to manage BEAT liability and reporting requirements in later years. Certain business tax reform changes will affect insurers’ corporate taxes, financing and investment portfolios, namely (i) the repeal of the Alternative Minimum Tax (AMT), (ii) the reduction of the corporate tax rate, (iii) new limiting net interest deductions and (iv) modifications to net operating loss deductions. Insurers expect longer-term after-tax income relief with a lower corporate rate and a repeal of corporate AMT. But, in the near term, they must evaluate what adjustments are necessary in the form of write-downs to deferred tax assets and how the changes to net interest and net operating loss deduction amounts may affect future financial statements. For example, some finance and accounting functions need to consider future business interest deductions, as the lower overall amount available to deduct could modify intragroup financing strategies for U.S. multinationals, such as intragroup loans by the parent to provide capital to U.S. subsidiaries and shifting debt from highly leveraged U.S. subsidiaries to non-U.S. jurisdictions. We expect that the reduction in the corporate rate will offset the increase in taxable income, but the changes to specific insurance provisions will have an impact on longer-term financial statements and ultimately how regulators and rating agencies perceive a company’s financial strength. To better understand the scale of beneficial or adverse effects, companies will need to analyze and project the net effect of expected writedowns and limitations to deductions against the expected after-tax income relief from the reduced corporate rate and use of AMT refund credits. Through these exercises, corporate finance, tax and accounting functions can guide business leaders to help inform business strategy by demonstrating the potential benefits of lower corporate taxes against potential negative effects of tightening limitations on previous deductions. Product pricing will be an area of focus in 2018 We expect product pricing will be an industry focus because the lower corporate tax rate must be balanced against modified provisions that limit deductions and increase taxable income. Higher U.S. corporate tax rates had previously always been a factor in new product pricing for both life and P&C products, and the new reduction in the corporate rate will offer more margin flexibility. Finance and product leads will have to consider impact on product pricing of not just the new lower corporate tax rate but also an increased deferred acquisition cost (DAC) tax capitalization percentage, as well as changes to tax reserving. In addition, the new minimum tax on reinsurance payments to offshore affiliates will cause companies to examine their capital management strategies and determine if the tax leads them to increase prices to address increased capital pressure. See also: U.S. Insurance Deals: Insights on 2H 2017   Given the higher commoditized and shorter-term nature of personal products, price competition is likely to occur sooner than in life, and any decline would subdue some of the bottom line benefits from the lower corporate tax rate. Overall commercial prices probably will continue to result in positive underwriting and favorable returns, but the total benefits will be more favorable for domestic insurers than cross-border ones. Life companies will have to balance the effect of the lower corporate rate on premium pricing with tax reserving changes, principle-based reserving adoption and other state regulatory initiatives. Implications

• With more expected income to deploy, companies should consider the investments they can make to support operations, growth and productivity. This could include enhancing IT infrastructure, investing in new product lines or capabilities, pursuing opportunities to expand brand presence through acquisition and providing more free cash flow to policyholders and shareholders.
• Because U.S. parent companies are no longer taxed on worldwide income, the M&A market could see an uptick.
• BEAT is likely to significantly affect reinsurance arrangements between U.S.-domiciled entities and affiliated entities located outside of the U.S.
• Changes to specific insurance provisions will have an impact on longer-term financial statements and ultimately how regulators and rating agencies perceive a company’s financial strength.
• Finance and product leads will have to consider the impact on product pricing of a lower corporate tax rate, an increased DAC tax capitalization percentage and changes to tax reserving.

This article was written by Mark Smith, Chris Joline, David Schenck, Tom Swoboda and Ed Hirsh. You can find the report here.

I’ve always enjoyed a good magic trick. It gives you a momentary suspension of reality to believe that there is something more. There is also the desire to figure out how they did it, how did they know that your card was the 10 of clubs, how did they walk through a solid wall, how did they violate the laws of physics? But, while the trick is entertaining, you always know that they did not actually saw someone in half and then reverse the process in less than a minute. You know that the rabbit did not dematerialize. You know that the person is not really floating in midair, levitating without strings. You lose yourself in the wonder of it all, but realize that it was just an illusion. There is a somewhat painful parallel with predictive analytics and insurance. On the one hand, there is great promise to change the entire industry, suspending the well-worn rules of insurance and the doctrines taught since the beginning. We look with wonder at the almost messiah-like prophecies of insurtech and how it will kill multibillion-dollar insurance organizations and entire distribution ecosystems at the touch of an icon. But then we start to wonder, how will it actually work? How will companies choose which risks are acceptable and at what price? How will they go from losing money on every policy into a sustainable business model that not only generates revenue for today but adequate reserves and reinsurance for tomorrow? How will the buying public intelligently evaluate different policies and coverages when they do not speak insurance-ese? Just because data may be available, it does not automatically follow that this information is useful, credible and legitimate or can be legally employed. This is the now you see it, now you don’t part of insurance and predictive analytics. But before we delve into it, let’s take a moment to review some fundamentals. First — insurance is the largest single industry on the planet, accounting for 7% of the global economy. It has been called the grease that lubricates the global economy. It is virtually impossible to think about anything where insurance is not at the heart. Nothing gets built, no commerce moves, no innovation or transaction takes place without insurance playing a major role. Second — insurance is the original data-driven industry. Insurance has no converting of raw physical materials into a finished product. At its core, insurance is words, a promise, a contract that lays out rules and responsibilities of the parties. And this is an important lead up to the next fundamental. Third — insurance is NOT a commodity. Of all the things written and bandied about regarding insurance, this is the single most important misconception being spewed by people who have their insurance feet firmly planted in the air, using both social and traditional media for the uncontrolled spread of this false insurance news. You can take out your ruler to measure if the two pieces of lumber are the same height, width and length. Or if the jar of salsa contains 16 ounces or not. Because insurance is the original data-driven industry, there are no physical dimensions to measure or evaluate. Comparing plan premiums and deductibles only scratches the surface. Let me paint a word picture that I hope will make this clear. Imagine that I have five plastic tubes in my hand. To the casual observer, they look similar in size, color and caps. I submit that you would not mindlessly grab a tube and put its contents into your mouth without carefully reading the label and ingredients. You would do this because the tubes, while similar in appearance, could actually contain:

• Toothpaste
• Anti-bacterial soap
• Acrylic paint
• Brass cleaner
• Hemorrhoid cream

For an actual insurance example, an insurer’s “HO3” provides Coverage A – dwelling coverage. Some Coverage A descriptions say, “This coverage does not apply to any dwelling used in whole or in part for ‘business.” Do you ever conduct business from home? Recent studies suggest that half of Americans work from home at least occasionally. If your policy reads like this, then you probably do not have dwelling coverage. Another example comes from Coverage C – personal property. Some policies say it covers personal property “while it is IN” the “residence premises.” This eliminates any personal property that was not directly in the residence. See also: 3 Key Steps for Predictive Analytics   And the worst possible time to discover that your property will not be replaced is when you’ve had a loss. Something less than the holy trinity Fourth — sometimes referred to as the something less than holy trinity, Standards, compliance and regulation play important roles within the insurance industry.

• Standards — with very rare exceptions, there basically are no meaningful standards within the insurance industry when it comes to policy language, pricing or data. Yes, there are forms and rates published by organizations, but carriers are very creative in creating unique coverages, wording, pricing and underwriting selection criteria. We know this to be true for at least three reasons. When an insurance agent moves a policy from carrier A to carrier B, the agent is strongly encouraged to review differences with clients or face a potentially painful and expensive E&O claim for policy differences that were not explained and signed off. Another reason that we know this to be true is that you cannot take data from a policy download from carrier A and upload it to carrier B. And thirdly, if there were meaningful standards, then there would be no need for comparative rating software.
• Compliance — across the country, there is an unbelievably complex web of compliance requirements. Not only are there state insurance commissioners, but some states split out workers' compensation from other forms of insurance. Insurance products must comply with individual jurisdictional requirements, or substantial fines can be levied. As an example, check out the press releases on the fines charged to Zenefits for its part in selling insurance without the use of authorized, licensed insurance agents.
• Regulation — In 12 states, insurance commissioners are elected, providing additional political pressure to lower rates while raising coverage, especially in an election year. On top of the state requirements, some cities have their own insurance requirements which are layered on top of, and sometimes conflict with, the state requirements when it comes to required coverage, price, covered claims and taxes.

Data sources Because insurance is the original data-driven industry, insurance organizations are a treasure trove of information that they have been collecting since their inception. Perhaps this is why both artificial intelligence and predictive analytics zealots salivate at the prospects of getting access to this information. There are three potential data sources available to insurance organizations that could feed predictive analytics; traditional, obvious and hidden. Traditional — these are data sources employed today within the insurance industry. It all starts with information collected on an application. From there it spreads out to include vehicle/property data, employment and payroll data, replacement cost estimators, credit scores, actuarial tables, claims history, location/ZIP codes and beyond. But even with all this information, there is no uniform application of it throughout the industry, among companies or even within a state. For example, there is ample statistical evidence proving that insurance credit scores are solid predictors of claims severity. Some carriers use insurance credit scores extensively while others ignore it altogether, even in the same competing markets. In personal auto and homeowners, premiums can more than double based solely on the insurance credit score of two identical insureds in the same state. In California and Massachusetts, you are not permitted to use insurance credit scores in either personal auto or homeowners, while in Maryland you can use it in personal auto but not in homeowners. This is a classic example of the “now you see it, now you don’t” aspect of insurance data and predictive analytics. Data is available, but not everyone elects to use it. Also, there are regulatory restrictions on when and where the data can be employed. It's important to understand that, unlike traditional credit scores, which are used by lenders such as credit card issuers, consumers don't have access to their credit-based insurance reports. This helps fuel the fires of mistrust that the American people feel toward insurance. One study concluded that, in most states, “auto insurance premiums are driven in large measure by economic factors that are unrelated to driving safety” — namely, education level, occupation, homeownership status, prior purchase of insurance and marital status. It also found that a substantial majority of Americans believe it is unfair for insurance companies to use economic characteristics – specifically, education level, occupation, not having insurance because of not having a car, homeownership status, marital status, and credit score – in setting auto insurance premiums. For example, good drivers pay 59% more, or $681 annually, on average for auto insurance due to personal characteristics associated with lower economic status. Obvious — there are a number of new and exciting data sources that have clear and observable usage for insurance and predictive analytics. They may be obvious to the casual observer, but privacy and political concerns may move them into the questionable category. While telematics has been used by some insurance companies, I don’t think of it as a traditional data source because of the emergence of your vehicle not only as a means of transportation, but also as a data hub. In addition to vehicle and driver performance, data from vehicle hands-free cell phone usage, entertainment and vehicle hotspot connections are available. This allows non-driving data and patterns to be compared and scored, potentially altering premiums and whether you are a desirable insurance customer. Social media is another obvious data source, but how should it be employed within the insurance marketplace? For example, should your social media posts be used to select whether an insurance company is willing to insure you, and at what price? Also, should social media information be criteria to deny a claim? Recently, a couple applied for personal umbrella liability insurance to be added to their homeowners policy. As part of the application, they had to list the number of dogs they owned and their breeds, which they truthfully answered. They were shocked to find that they were not only rejected for the extra insurance but that their homeowners policy was being canceled as well because the company claimed they had a Rottweiler mix, a dog breed the company considers dangerous. The most intriguing part of the story was that the insurance carrier used pictures from Facebook as proof that the coupled had “lied” on the application. The couple followed up with the insurance company to let it know it was wrong about the dog’s breed. Instead of standing down, the insurance company said it would need a written letter from a veterinarian. This was no problem, because the wife was a veterinarian. The insurance company eventually offered to reinstate the policy, but the couple took their business elsewhere and lamented, “Be careful about what you post on Facebook. It’s sad that you can’t post pictures of your beloved pet on your own Facebook page." There are mountains of data available on individuals and corporations that are in the public domain. The federal government collects and publishes reams of data, and there is precious little that you can do about it. Some examples include census data, name and addresses of all licensed pilots, whether your address is on a flood plain. The surveillance, epidemiology and end results (SEER) program of the National Cancer Institute Program provides information on cancer statistics in an effort to reduce the cancer burden among the U.S. population. By keeping this data, SEER details when/where cancers are breaking out. Local governmental data sources include information on physicians and attorneys, and your water bill is also in the public domain. One of the more interesting non-governmental sources is information for all loans issued through the Lending Club, including the current loan status (current, late, fully paid, etc.) and latest payment information. And we won’t even go into amount of free data that is available via Facebook. The Internet of Things, IoT, is providing unparalleled additional information into the details of our lives. Just about any type of device for your home, office or health is collecting data to learn more about out preferences and habits, which is potentially available to affect your insurance acceptability and pricing. Even iRobot’s Roomba is collecting data about dimensions of a room as well as distances between sofas, tables, lamps and other home furnishings. Phone companies can track which cell tower your call is pinging. Credit/debit card companies are tracking payment details. Some of this may/may not be available today, but the shifting sands of privacy certainly will alter the status quo. Hidden — there are a growing number of non-obvious data sources that contain potentially valuable or questionable information about our lives that may be applied to insurance. We will discuss the sources and then whether they should be used within insurance. One insurance company uses the customer’s email domain name in pricing personal auto policies: “Certain domain names are associated with more accidents than others. We use a variety of pieces of information to accurately produce a competitive price for our customers.” This hidden piece of information leads to different pricing for Hotmail email account users instead of a Gmail one. It was also found that this insurance company charged significantly more for insureds with foreign-sounding names. Intelligent personal assistants, IPAs, are becoming more and more popular each and every day. Organizations from every industry, including insurance companies, are tapping into this easy-to-use technology that is only a spoken word away. Amazon and Google are leading the pack, with Microsoft and Apple in hot pursuit. The good news is that all you have to do is talk, and the IPA is there to grant your every wish. The bad news is that for IPAs to answer your request, they have to be listening. And not just listening but listening all the time. It is unclear how much of the conversation is actually saved and how it is being used. But as we know all too well, once something is saved, it is almost never fully erased; there are digital cookie crumbs to be followed. There was a case where police subpoenaed the recordings made by an IPA in a house where a murder was committed. Should this information be made available to the police? Should it be made available to insurance companies? You can be certain that everybody, and I mean everybody, including smart phone apps, fitness monitors, retail and grocery stores, rental car companies, cable companies, etc. are collecting lots of data about you, your life choices, lifestyles and buying habits. For many of the obvious and hidden data sources, the public is giving away their data and rights to restrict how it might be used. Why do you think everyone offers free memberships, convenient apps and customer loyalty rewards? It’s not that they enjoy giving things away. While you cannot be certain about a great many things in this life, one thing that you can go to the bank about is that they think it’s worth the time, energy and money to collect this data to monetize it. I know it’s even less exciting than watching paint dry, but stop and try to read through the entire user agreement before mindlessly clicking on the “I Agree” EULA (End-User License Agreement) button. If you read these agreements in detail, you will find that you are providing the right to collect just about everything you can imagine and then some, and then letting the company do with it whatever it thinks best. One car rental user agreement I reviewed said the company had the right not only to the vehicle telematics data, but also to your Facebook information. Now I really cannot connect the dots between a car rental and my Facebook data, but the company apparently think it’s worth it. My favorite example of hidden information is mapping data. I love how I can enter a destination and see the best route, with/without tolls, and even see changes in the route in case of changing traffic patterns caused by roadwork or accidents, and at no cost. This is great technology that I am thankful for each and every day. However, in case you were not aware, smart phones with mapping tools are also recording your movement as long as the phone is on, even when you are not using the map app. They have my detailed movements on file since December 2012. I can look up, by day, where I went, including the places of business I visited. Do you think an insurance company might be interested if you:

• Changed your normal travel patterns?
• Visited a local watering hole after work?
• Started going to a cancer treatment center?
• Went to an AIDS clinic?
• Stopped in a medical or recreational marijuana store?
• Drove to a psychiatrist three times a week?

As I said, this is great technology, and it doesn’t cost me a thing. Or does it? A personal example If you were to look at my available data during the early summer of 2016, you would see a change in my travel patterns, mapping, spending and social media information. You could easily determine that something had changed, triggering a more detailed look in my traditional, obvious and hidden data. You would have found that I started visiting a dermatologist quite frequently, and stayed for long periods. You would have seen pictures and posts on social media about multiple visits and procedures to identify, remove and then remove again more tissue associated with melanoma. While I am thankful that the doctors were able to remove it after multiple surgeries, would an insurance company be interested in this kind of data? Or perhaps the more daunting question is whether an insurance company should be interested in this kind of data? There is no question that the data is there (now you see it). But the more difficult question is whether or not the insurer should be allowed to employ it (now you don’t). See also: What Comes After Predictive Analytics   Bill Hartnett, sometimes called the “godfather” of Microsoft financial services, puts it succinctly; “Predictive analytics has a dark side. Price and claim optimization should have no place in insurance. Determining that you are willing to pay a price higher than your underwriting risk indicates based on past buying behavior, or accept a claim settlement less than the actual cost based on your financial situation is not insurance.” Bill Wilson, CPCU, ARM, AIM, AAM, founder and CEO of InsuranceCommentary.com, puts it more bluntly, “All insurance regulators who have considered the issue of price optimization have concluded that it has no place in underwriting and pricing insurance. Most state insurance laws expressly require that rates and premiums be risk-based and not unfairly discriminatory. The insurance industry is one founded, like few others, on the overriding principle of utmost good faith. Price optimization is about as far as you can get from good faith.” Application These are some formidable questions, requiring both discussion and research. Intel, IBM, Workday and the Washington, D.C.-based Information Technology Industry Council—whose members include Facebook, Apple and Google—all issued principles on the ethical use of artificial intelligence. Microsoft put out an entire book on “Artificial Intelligence and its Role in Society.” Some of the biggest tech companies founded an ethics-setting organization called the Partnership on Artificial Intelligence to Benefit People and Society, based in San Francisco. There are many others including Open AI, the AI Now Institute, doteveryone and the Center for Democracy and Technology. It would seem reasonable that because insurance is the single largest industry on the planet, and consumes nothing but data, representatives from the insurance industry participate in these cross-industry forums. So, where do we go from here? I’d like to suggest two ways to move forward, but their source and inspiration are miles apart from insurance. First — predictive analytics and insurance is a journey, not a destination. The inspiration for this is Heraclitus of Ephesus (535 BC – 475 BC.) Now I admit that insurance, technology and ancient Greek philosophers don’t have a whole lot in common, at least on the surface. Heraclitus wrote, “Everything changes, and nothing remains still. You cannot step twice into the same stream.” He was saying that if you step into a stream, step out and then right back in, you are stepping into a different stream. The water around your feet during the first step will be different than the second. The air around you is different. The birds in the air are no longer in the same place. And so it is with predictive analytics and insurance. What was commonplace and known last year is way out of date. The assumptions and tools are better and different now than just months ago. This requires continuous investment, evaluation and adjustment of your predictive analytics plans and application. There needs to be a continuing collection and evaluation of data sources, quality and efficacy. Second — predictive analytics and insurance requires that we treat people well. The works of Robert Neelly Bellah (Feb. 23, 1927 – July 30, 2013) are the inspiration for this application. He was an American sociologist, and the Elliott Professor of Sociology at the University of California, Berkeley. Robert wrote about something he called “expressive individualism,” where each person has a unique core of feeling and intuition that should unfold or be expressed if individuality is to be realized. Each person has the need to express needs, wants and desires. Throughout the predictive analytics implementation process within insurance, we need to treat people individually and always lead with respect. Today’s insurance marketplace needs to move way beyond the current mania of mail-merge marketing memos. Another thing to avoid is acting creepy, using so much information that you make the customer feel uncomfortable. Winners within the insurance marketplace will be those organizations that aggressively and systemically leverage data and predictive analytics technology while providing personalized products and services. The road to successful employment of predictive analytics within the insurance marketplace will, by necessity, require many starts and adjustments along the way. Members of the “something less than the holy trinity” will create unique challenges felt in no other industry. But great potential awaits those who start and continually move forward. Note — this article is based on a presentation for the Global Predictive Analytics Conference, April 2-4, 2018, Santa Clara, CA.

Here’s a quick experiment: Think of what you ate for dinner last night. Not too difficult, right? Now think of what you ate for dinner exactly 15 days ago. Chances are you have a harder time coming up with it, if you can remember at all. Unfortunately, the same concept applies for a lot of on-the-job training, especially if it isn’t challenging enough. Employees know the material when they learn it but struggle to recall it when it comes time to use it on the job. The issue, researchers say, is that there are two primary components to how we learn and remember. There’s “storage strength” – how well we learned something. Then there is “retrieval strength” – how easy it is for us to access that information later. Robert Bjork, professor of cognitive psychology at UCLA, says the interplay of these two elements creates challenges in training for learning and development pros. So much of the focus of today’s workplace training is on delivery methods and ease of access. Increasingly, online modules and self-directed training are replacing sessions where the trainer and students have to be in the same room together. These advances make it easier for agencies and other employers to offer workers the training they need to improve. But they don’t guarantee training is effective or that workers retain information for when they need it on the job. In fact, make training too easy, and it can be detrimental to long-term retention. Just because someone can retrieve information during training or in a follow-up evaluation doesn’t mean they’ve learned the material enough to retrieve it when they need it on the job. Researchers point to two effective solutions based on practically opposite ends of the spectrum. Selecting the right approach depends on the kind of training you’re conducting. See also: Training Millennials: Just Add Toppings   Testing improves memory Training should include more tests — and those tests should be more difficult. Most employees won’t like this solution, but testing does more than determine how well the test-taker knows the information, Bjork argues. Every time information is successfully retrieved, the memory of the information changes, making it easier to recall in the future. Tests that challenge students’ understanding of the knowledge in different ways make those connections even stronger. Questions should be nuanced and presented in many different forms (think a mixture of fill-in-the-blank, short answer and longer responses). The more difficult the test, the more storage strength the material will have in students’ minds. There is one time when basic evaluations like multiple choice tests are still preferred – during pre-testing. A multiple choice test offered before training can help prime students for the material they’ll soon learn. In these situations, Bjork’s research has determined that even though learners will score poorly on the pre-tests, they’re more likely to pay attention to concepts offered as multiple choice answers during the actual training. For example, if the training topic is commercial property risk management, a multiple choice pre-test should cover common terms in business income insurance, equipment breakdown, builders risk and causes of loss forms, etc. The evaluation following the training session should use different formats to keep trainees on their toes. And the test should be hard -- every minute employees spend struggling to come up with an answer boosts storage strength. The case for the case method If you’re fortunate enough to have a group of trainees in a classroom with a subject matter expert leading the training, tougher testing may not be the most effective way to get the material to sink in. This research also makes a strong argument for teaching styles like the case method, according to the Harvard Business Review. It points to Harvard Business School (HBS) as a shining example of the case method in practice. These classes downplay testing, and professors are “choreographers of discussion” who don’t provide answers, but rather a pathway to discussion. Lectures become in-depth discussions where students debate the best course of action and are constantly forced to reassess their ideas. The school offers these tips for preparing and leading the discussions:

• Have a complete set of objectives. Don’t confuse discussion for vagueness. Preparation should include specific information student must learn and some questions or discussion points that will get students there.
• Let trainees take ownership. Students should guide the discussion and offer new perspectives based on what they feel will be most useful to them. Trainers should ask questions to keep the discussion relevant to the training topic at hand and focused on outcomes that will benefit the organization.
• Listen. In traditional training models, instructors speak at least 80% of the time, and workers speak 20% of the time. The case method flips that, putting the onus on students to keep the discussion going.

Say you’re leading a session on handling auto claims. Rather than a traditional classroom session on analyzing liability, assessing damage or dispute resolution, create a fictional scenario based on an actual auto claim and let employees hash out how they would approach the situation with targeted guidance from the trainer. See also: Security Training Gets Much-Needed Reboot   This kind of in-depth preparation and delivery isn’t ideal for every on-the-job training session. It requires more preparation for instructors to lead a discussion of a real-life dilemma. But these discussions are great during onboarding and especially during scenarios involving complex concepts or customer service techniques. If you present the material the right way and make it challenging enough, trainees will understand the information better and be able to access it when they need it on the job.

Part 1 – Your claims process and systems are a customer-experience issue. What is the most important task of a life insurance company? There are many answers to this question. It all depends on your role or paradigm of the company. When you break it all down, I believe there is one task that is most important within a life insurance company. It is to pay claims! Think about it. If a life insurance company is not good at paying claims, then what is its purpose? A recent article in A.M. Best’s journal shared a survey that indicated the top two items that needed technology improvement are:

1. Improving the customer (and agent) experience.
2. Legacy administrative and claims systems.

Let’s discuss #1 first. If you have never read the book called "Delivering Happiness," by Tony Hsieh, I recommend it to everyone in your organization. Stop reading this article and go buy the book or audio version. The idea is, if you focus your attention on customers and their experiences, the business will thrive. Of course, you have to start with a strong culture and hire the right people. You have to give them the atmosphere, tools and freedom to tackle the work. Happy employees will lead to happy customers! We believe that 96% of carriers have decided that claims transformation and digitization is imperative. We believe the 85% of carriers’ executive teams are dedicated to claims excellence and good customer experiences. See also: Finding Efficiencies in Claims Process   You must design your claims process with the customer in mind. Here are some questions to keep in mind as you and your team evaluates the claims process:

• Do customers want to go through an old process of jumping through hoops?
• Do customers want to go to your website, download a PDF claim form that isn’t even fillable, print this form, fill it out and then mail it in, only to receive a five- to eight-page claim packet two weeks later that they feel will need an attorney to complete? YUCK!!
• What kind of experience are you presenting with a process like this? Would you want to go through this experience?
• More importantly, do you think you will ever have a chance to sell this customer any of your products in the future?
• What is your customer acquisition cost if you could retain this client with a great customer experience?

The point is, you must be on a mission to create a better customer experience. Your customers demand it! Today’s consumers expect to have an easy experience. They expect multiple resources to enhance that experience. They want to be able to complete these processes on their time, not the company's. Updating and maintaining technologies are crucial to an insurer's ability to capture and retain these technology-savvy customers. See also: Global Trend Map No. 10: Claims   Stay tuned for next issue – Part 2 – You can reduce your claims expenses and improve the customer experience.