Tag Archives: life insurance

How to Turn Around Sluggish Life Sales

Life insurance sales are sluggish, and the struggle to capture the elusive mid-market and millennial demographic persists. Insurers are searching for a way to turn things around.

As tech start-ups have entered the insurance market, shaking up age-old policy servicing and sales models, insurers have been forced to rethink their offerings. Not adapting is not an option anymore.

Some innovative insurers are starting to gamify elements of the customer experience as well as the value chain, like we’ve seen in other industries, from airlines to credit cards, which already have well-received loyalty programs in place that rely largely on game mechanics. Innnovative insurance companies are rewarding users with points and gift cards for completing courses or taking quizzes related to financial wellness and mindfulness, for example. This is their way of incorporating fun, engaging elements into policies as a means of meeting the insured where they are.

But are these new types of policies and customer engagement models taking hold?

Research conducted recently by the Harris Poll on behalf of SE2 and Life.io investigates if factors like rewards and engagement make life insurance more appealing both to those who already have policies and those who don’t. The findings found that the vast majority of the 2,000 respondents would share real-time wellness data with insurance companies through wearable devices in exchange for financial benefits like a lower insurance premium or wellness rewards.

The data also found that U.S. adults want their policies to be more interactive. Roughly two-thirds (68%) say that, if a provider offered a policy that included elements of gamification to reward healthy lifestyle and wellness habits (think: badges for hitting certain milestones, a leaderboard, financial rewards), they would be likely to engage in those elements. Additionally, 43% of U.S. adults say they’d be “much” or “somewhat” more likely to purchase a life insurance policy if the insurer offered an interactive program with wellness benefits (such as coaching and education) rather than just policy payout.

See also: Customer Experience Gets a Major Facelift

In terms of generation, over half (53%) of millennials (ages 23-38) say they’d be “much” or “somewhat” more likely to purchase a life insurance policy if the insurer offered an interactive program with wellness benefits. This is noteworthy given that life insurance sales have historically lagged among this group, according to previous research from SE2. Even baby boomers want in. More than one-third (36%) of boomers (ages 55-73) say an interactive program with wellness benefits would make them “much more” or “somewhat more” likely to purchase a policy.

Will Sweat for Discounts

The survey also found that a majority of U.S. adults seem to like the idea of exchanging wellness and lifestyle data with life insurers for rewards and improved lifestyle. They would share wellness data, such as steps walked daily (79%), blood pressure daily (76%), heart rates daily (74%), calories burned daily (73%) and sleep patterns each night (71%) for financial or health rewards.

In fact, those rewards could lead to lasting lifestyle and health changes. Eighty-six percent of U.S. adults say they’d be “much” or “somewhat” more likely to live a healthier lifestyle if a life insurance company offered cash back as an incentive in exchange for their real-time wellness information. Almost all (94%) of millennials say this would be the case for them.

Other incentives that would urge the insured to improve their lifestyle habits? There are plenty:

  • 86% of U.S. adults say cash back would encourage them to live a healthier lifestyle
  • 85% say a lower life insurance premium would encourage them to live a healthier lifestyle 
  • 82% say additional coverage or benefits would encourage them to live a healthier lifestyle
  • 77% say wellness rewards would encourage them to live a healthier lifestyle 
  • 70% say wellness education/coaching would encourage them to live a healthier lifestyle
  • 66% say financial education/coaching would encourage them to live a healthier lifestyle

See also: 3 Ways to Improve Customer Experience  

More Frequent, Meaningful Touchpoints Matter

The importance of customer engagement cannot be overstated. The research found that policyholders want to hear from insurers more often, not just upon sign-up and for billing purposes. More frequent, personalized touchpoints are crucial for retaining and growing the insurer’s book of business. Higher-quality interactions can help build relationships, which leads to higher sales conversion, reduced lapse rates and referrals.

What to take from all of this?

Policyholders and prospective policyholders will no longer settle for the status quo. They want higher-touch customer service with more frequent touchpoints, policies that are more personalized and engaging and rewards that continue. These approaches can help turn around sluggish sales and lead to lifestyle and health improvements of the insured.

For insurers who aren’t willing to evolve their policies and engagement models to better meet consumers where they are, survey respondents stated that they would be willing to switch insurers. That’s not to be taken lightly.

Tech for Managing Closed Blocks

Life insurance is a dichotomy: decades-long customer relationships, but products in a continual state of change. As companies evolve, active sales of specific products are often discontinued due to underperformance or corporate shifts in strategy, although many of the policyholders are still very much alive.

As a result, insurers have to dedicate time and resources to servicing these discontinued products, also known as closed blocks. This robs focus—and funding—from strategic growth initiatives.

On average, these blocks run off anywhere between 4% and 10% a year, depending on the type of product. Yet, the costs to manage these discontinued products aren’t decreasing at the same rate.

In most cases, closed blocks run on legacy policy administration systems, which typically require more human intervention than newer, more agile platforms. The technology may be so old that it requires specialists to operate, which keeps the servicing costs per policy high.

In the past, the only way life insurance companies could mitigate the high cost of closed block administration was to sunset obsolete systems and migrate to a new solution. However, this approach presented problems of its own.

Data conversion was expensive and complex, at best, often taking years to produce the expected efficiencies. Even then, there was no guarantee of accuracy. Meanwhile, managing the change took time away from the company’s core business.

Today, the emergence of new approaches and disruptive technologies give insurers more options, and new opportunities to reimagine the administration of closed blocks.

Three main factors are driving the changes:

  • Innovative business models can shape more advantageous deal structures with variable costs and reduced risk.
  • Extreme transformation levers like robotics, automation, machine learning and artificial intelligence decrease operating costs and deliver efficiency gains in months, instead of years.
  • The application of automation and big data conversion techniques speed data transfer without the inherent risks of more traditional conversion methods.

This paper explores the impact of these new enablers and how life insurance companies can maximize the benefits of these new closed block strategies.

See also: Selling the Urgency of Life Insurance  

Innovative Business Models Give Insurers More Options

In the past, life insurance companies had limited options for closed block management. Today, insurers have myriad approaches beyond traditional platform migration to consider.

Sell the Closed Blocks

One simple way to lessen the burden of managing closed blocks is to sell these policies to a reinsurer or to another carrier. While this approach does eliminate the challenge of servicing this run-off business, by divesting these policies companies also sever the associated client relationships. Cutting ties with clients who hold these discontinued policies eliminates the ability to cross sell or market new offerings to them (and their families) effectively. The sale could also be negatively received by the client as a disregard for long-term customer relationships, or as a sign of financial instability. Either of these perceptions could cast a negative light on the insurance company’s brand.

Outsource to Best-of-Breed BPO and ITO Provider

Companies that want to maintain client relationships but offload the day-to-day servicing of closed blocks could consider outsourcing policy administration and claims services.

While this option is feasible to mitigate some of the labor costs associated with closed blocks, it does nothing to alleviate the technology overhead or do anything to simplify the complex architecture that adds costly manual steps to the servicing process.

Outsource to Comprehensive Third-Party Administrator

Outsourcing both platform and personnel to a third-party administrator is a viable option for many life insurance companies. They can retire aging systems, redirect the specialized personnel often required to run these older platforms and turn a fixed cost into a variable cost structure. The idea is not only to offload operations as is, but add automation, robotics and other disruptive technologies for continual efficiency gains and cost savings throughout the duration of the contract. The challenge is that very few established third-party administrators with these capabilities and insurance industry experience currently exist. The other challenge is that established third-party administrators are less flexible in their approach, which often leads to nickel-and-diming the insurer or compromising the customer experience.

Develop a Structured Deal With Strategic Partners

Instead of going it alone, some insurance companies are entering into strategic alliances or creating structured or balance-sheet-based deals with trusted partners or other carriers to increase value, leverage economies of scale and manage risks. In this scenario, insurance companies share resources, knowledge, expertise and risk associated with closed block management. A few examples of such options are:

  • Joint ventures, in which vendor and insurer share resources, revenue, expense and profits. These agreements can be very informal or complex. While they work for some insurers, they also have the potential to take focus away from the insurer’s core business.
  • Equity strategic alliances, in which the provider takes over closed block administration, but both provider and insurer share in the block’s costs and profit.
  • Industry consortium, in which two or more life insurance companies jointly invest with a provider to create an industry utility to manage their collective closed blocks.

Extreme Transformation Levers Drive New Levels of Efficiency and Customer Engagement

Technology is advancing at light speed, which is excellent news for life insurance companies. Extreme transformation levers are now in play, enabling companies to improve productivity by as much as 35% to 40%. 

Blending the use of disruptive technologies, like robotics, automation, descriptive analytics and machine learning, with system conversions can transform the economics of closed block management.

A transformed environment operates under a RAPID, SMART, LEARN, VIRTUAL model:


Use automation to make processes run faster.


Use analytics to really look at how to process and who will process.


Fully use machine learning in data extraction and document classifications to learn what information has to be taken from each form, each process and each individual.


Create a straight-through processing environment, where the transactions move seamlessly through the required steps without human intervention.

Robotic Process Automation

Robotic process automation (RPA) is the first phase of the evolution of automation. The “robots” are actually advanced computer software solutions that can interpret existing applications for processing transactions, manipulating data and communicating with other digital systems. The software is not only capable of streamlining repetitive, manual tasks previously handled by humans but, because it requires no coding, is fast and cost-effective to implement and is completely non-disruptive to the existing IT environment.

The number of tasks or hand-offs requiring human intervention is typically very high in legacy systems for closed blocks. The work might require countless hours and investment to fix the administration systems. It can alternatively be addressed through RPA, which can provide 20% to 30% efficiency gains within three to six months and with limited investment.

If life insurance companies work with a provider with skilled RPA technologists on staff, that provider can not only speed ROI by leveraging RPA for lower-complexity tasks but can minimize conversion efforts. Ultimately, this enables the provider to increase efficiency and throughput at lower costs.

Artificial Intelligence (AI) and Machine Learning-Based Utilities

There are multiple technologies within artificial intelligence, like natural language processing (NLP), machine learning and computer vision. There are already uses cases in the industry for leveraging AI to reimagine customer engagement, automate transactional processing or improve claims processing. The results show that employing AI- and machine learning-based utilities to extract structured, semi-structured and unstructured data from documents can improve efficiency by 30%, even if the company makes no other changes.

But this is not to say that machines eliminate human beings from every process.

The ideal model creates a fine balance between technology and human engagement—moving from a model in which people perform the work and machines manage the exception to the polar opposite. The goal is to employ machines to do the basic, passive work and to engage people to handle the less menial, more reason-driven exceptions.


Like RPA, data analytics and advanced machine learning algorithms can greatly enhance the conversion process by reducing the amount of manual coding needed. But prescriptive and predictive analytics are equally effective in reducing operating costs.

For example, providers that apply first contact resolution (FCR) analytics to identify patterns and trends can increase contact center efficiency. By using predictive analytics to quickly segment customers and anticipate need, more callers get the information or resolution they need in one call. Over time, that reduces call center volume, lowers costs and increases customer satisfaction in the process.

Implementing analytics and machine learning techniques can also improve customer satisfaction and retention, as well as reduce the volume of service requests. These tools can be used to create insight by analyzing past customer queries to predict the next actions they may take or issues they may face.  Leveraging this information enables companies to reach out and solve customer issues before they escalate.

Analytics can also be used to identify lapse and retention patterns, which enable insurers to more effectively manage cost and risk.

Omni-Channel Customer Care Technologies

The biggest opportunity lies in reimagining the customer journeys. Different customers want to engage in different ways, and their expectation is to have a seamless experience. In the process, insurers have the opportunity to lower costs by using chat triggers on web sites and deploying analytics to segment customers, deflecting many calls to channels that lower costs while improving customer satisfaction.

Given the option, consumers prefer online communication to making an inbound call, as long as they get the answers they need. The financial impact could be significant, depending on current call volume and customer personas.

See also: Pricing Right in Life Insurance  

Methodologies to Mitigate Conversion Risk

Although modern technologies provide more options than traditional system migration, there will be some conversion involved. This is typically a very resource-intensive process using tools to extract and transform data from legacy systems to make it compatible with a new system.

The good news is the conversion process has significantly evolved in recent years with advanced technology and modernized approaches, bringing more efficiency and accuracy to the process.

The following techniques detail the options:

Conventional Conversion Process

In the past, there was one way to convert legacy data to a new or different system, and it involved a great deal of human intervention. 

The legacy data was mapped to the target system through a conglomerate of extract scripts, transformation scripts and loading scripts, all created by technicians, coders, subject matter experts or a combination.

Because the logic is embedded in the code throughout multiple scripts and systems, changes and defects were difficult to manage. To compound the challenge, data lineage and mapping documents were rarely kept up to date. The testing process involved human beings sorting through the supplied information, with little automation.

Contemporary (Semi-Evolved) Process

The introduction of extract, transform and load (ETL) tools to manage the schemas of legacy and target systems added efficiencies to the traditional conversion process. ETL tools are used by coders and technicians to manage mapping, isolating extract code from transformational code to target systems, as well as managing transformation logic.

Essentially, these tools enable companies to extract data from numerous databases, applications and systems, transform it so it works with the target system and load it into the target database. Although some ETL components can be automated, like scheduling and common management functions, logic mapping still requires manual analysis, design and subject matter expert involvement.

So, there’s an improvement, but, because so much manual intervention and specialized personnel are still involved, ETL tools do not significantly reduce the overhead costs associated with conversions.

Modern Approach to the Process

Today, components of big data architecture can be leveraged to eliminate the need for manual coding. New big data platforms can accommodate new schemas with read functionality of Hadoop architecture, which scales to accommodate large data files more easily.

Spark, Java and Python machine learning libraries can now be built to perform source-to-target mapping, or schema mapping, automatically. Other open source tools can perform testing and analysis, adding efficiency without the need or cost associated with building proprietary tools. Although many ETL functions are still manually coded, automation of these and myriad other functions are currently in development and primed for future deployment.

How Insurers Can Prepare for Change

One message is very clear: the same old ways will not lead to a better future. No question, the supplier maturity and the emergence of disruptive technologies and tools have brought new closed block management options to insurers. But effective closed block management is not a one-way street. To maximize the benefits of these new technologies when working with a strategic partner, life insurance companies should follow these five best practices:

  1. Strategic partner — Today, with the emergence of disruptive technologies and innovative models, life insurance companies have the opportunity to reimagine the administration of closed blocks. With the right partner and approach, insurers can ease the administrative burden and costs associated with managing these closed books of business and focus resources on growing the company for the future.
  2. Active C-suite engagement — A successful transformation requires alignment between the insurer’s COO, CIO and CFO functions, each of whom should be engaged in a closed block initiative.
  3. A business case that is proof of valueIt’s also critical to perform an assessment of the business case, product complexity and capabilities of the third-party administrator or a strategic partner before setting project milestones. The objective is to determine the “proof of value,” which is different from the traditional way of doing a proof of concept. This work upfront not only reduces surprises down the road but enables companies to set realistic timetables for the closed block initiative.
  4. Dedicated teams — Both provider and insurer have to assign dedicated teams of personnel to the project. Skipping this step, or assigning personnel who can’t fully focus on the project at hand, are the most common reasons that conversions fail. It’s also critical to recognize that the ideal platform for efficient closed block management is much different than what’s needed to support business growth. Often, life insurance company leaders blend the agenda and end up investing in expensive, highly configurable technologies that may not be necessary.
  5. A provider that’s a cultural fit and transforms the status quo — Insurers should seek out and work with a provider that understands their business, is aligned with the leadership’s vision and is willing to share risks and rewards. This is not just a technology problem; it’s a business problem and therefore needs to be evaluated as a business strategy. Those characteristics, in combination with a proven track record of success, are key to optimizing outcomes.

Selling the Urgency of Life Insurance

The most essential things are not always the essentials people have or know they need to buy. 

Life insurance is one such thing not enough people have, given that the lives and livelihoods of many depend on the security that insurers can provide. 

To provide for the survivors, to care for a man’s widow and his orphan, is not an act of charity but a declaration of independence; that the living will have the liberty to protect themselves from poverty; that they will have the means to live without fear of eviction or exile; that they will have the freedom to pursue happiness.

To make these promises a reality—to ensure that people have all the insurance they need—requires insurers to better express the urgency of financial safety. 

According to David Albanese of Ameraquest Financial Group

“Insurers need to remind people about the safety life insurance offers. Whether they issue reminders for the second or third time, or for the first time in a long time, what they tell people must be clear and compelling. Anything short of that standard is a loss for everyone.”

As a scientist, I can speak to Albanese’s point about clarity of communication. I do speak to his point, in my own way, whenever I speak to nonscientists about biology or chemistry; which is to say I speak to persuade, I speak to inform, too, so I can get people to join my efforts or support my work.

Before they send a reminder to current or potential clients, insurers need to remind themselves of the importance of clarity of speech.

If people do not know why they need life insurance, if they do not comprehend the value of comprehensive coverage, if they do not know what they should know, then insurers have a duty to explain themselves.

See also: Pricing Right in Life Insurance  

Insurers have a duty to educate us about life insurance. That duty starts with a campaign that has a clear message and a consistent theme, so there is no confusion among those who see or hear the message, so the right people—those who need life insurance—get the point and spread the word, so people may buy all the life insurance they need.

This campaign must include traditional media and social media, because people receive messages through multiple outlets. We send and receive messages by email, voicemail, text, video and chat. 

The conversations we have, the news we share, the comments we post and the posts we publish—all of these things have the power to influence how we act.

If life insurance is to be a topic of conversation, if we are to talk about this subject among our friends and family, if we are to do more than talk, then insurers must campaign to earn our trust.

Transparency is a good way to earn that trust.

Free of ambiguity and devoid of the slightest uncertainty, insurers can improve the world by proving to consumers that life insurance is a necessity.

Need for a Dedicated Coding Language

Insurance is rooted in data innovation. Wide swaths of modern statistics and probability were first devised to accurately price, predict and manage risk. But insurance’s pioneering position has faltered in recent years.

While today’s economy is ablaze with revolutionary advancements in big data and computation, the insurance industry has been uneven in its adoption and application of cutting-edge data technologies. One study found that just 20% of the data collected by insurance companies is usable for strategic analysis. Attempts to incorporate big data and machine learning into insurance products tend to occur on an in-house and ad hoc basis.

High financial stakes and strict regulations already complicate adoption of big data. The lack of a formalized system or computer language for interfacing with the available tools, technologies and data can also obstruct progress. This is why the life insurance industry as a whole, and actuaries, in particular, are in dire need of their own unified, dedicated programming language.

As the CTO of a startup working with big life insurance companies, my team recognized this pressing need and committed ourselves to writing a programming language to help fill the gap.

To understand the distinct challenges of applying technical innovations to the insurance industry, it is essential to first peel back the complex layers behind computer applications in general. Computers have come a long way since their earliest days as room-sized mainframes with punch-card readouts. But, at their core, all modern computers still reflect this hard-wired legacy. Graphical interfaces and polished applications might make today’s computers more user-friendly, but every action and instruction must still be translated and abstracted into binary machine code to be computed on.

See also: A Game Changer for Digital Innovation  

Now, this is not to say that developers sit typing their code as zeroes and ones. Rather, modern programming languages use their own, distinct shorthand, which is then compiled into code readable by hardware. However, the particular output logic required varies by computer architecture. GPUs operate differently than CPUs, which operate differently than cloud computing frameworks. Therefore, the trend has been to write general purpose languages (GPL) that focus on accommodating the widest range of uses to a particular machine or architecture. Instead of optimizing for a specific problem or use-case, GPLs ask the programmer to learn a new language and apply it to their given domain.

The unique contours of the life insurance industry add a layer of difficulty. Regulations governing insurance are among the strictest and most byzantine of any industry. And beyond the issues of compliance come the extraordinary financial and social stakes riding on the integrity of insurance products. Core pillars of the private and public sector are propped up by the accurate, reliable management of risk. Insurance models running on shaky code could turn a tiny software bug into tens of millions of dollars in losses, the eventuality of which is amplified by the enormous complexity of accurately calculating risk five, 10 or even 25 years into the future.

Seeing these issues firsthand inspired development of the Atidot LIA (Language for Insurance and Actuaries). What my team and I realized when approaching this challenge was that what initially looked like one problem was actually three distinct but related issues.

The first issue was the substantial technical demands of carrying out the tasks that actuaries would demand of big data. Cleaning and anonymizing raw data, modeling it properly, testing and executing on a laptop or workstation and ensuring all code passed formal verification – these intricate operations would be a baseline requirement of any function.

After addressing the fundamental complexity of insurance operations, the next issue was simplifying the syntax and optimizing legibility for domain experts who might not be professional developers. By building in insurance-specific entities, data models and analytics models for several use-cases, LIA allows actuaries to speak the language of insurance instead of memorizing the arbitrary variables of Python, Visual Basic or C++.

Lastly, the unification of all necessary functionality into a syntactically legible framework would enable frictionless integration with machine learning models and accelerate time-to-market for new actuarial products. In other words, actuaries could write, debug and deploy big data in terms they could easily understand. Harmonizing function and syntax would help resolve some of the major roadblocks facing data integration.

The current tension between the enormous promise of big data for the life insurance industry and the difficulty of developing dedicated software contribute to a compromise worse than the sum of its downsides. Today, actuaries looking to incorporate big data or machine learning are forced to cobble together homegrown solutions using a patchwork of languages and tools. Otherwise, they must rely on dedicated developers who lack the domain expertise to fluently translate actuarial needs into proper code. This disconnect creates friction and stilts progress.

See also: The Opportunities in Blockchain  

However, by empowering actuaries to translate their domain expertise into instructions usable by cutting-edge technologies, a dedicated programming language will help align the existing talent in the industry with the untapped potential of data innovation. Modeling insurance is increasingly becoming a multi-disciplinary challenge, and a more precise, specialized programming language will help foster collaboration and jump-start innovation. In other words, our vision is to help big data and life insurance finally speak the same language

Rapid Diagnostics for Life Policies

For years, insurance companies have taken steps to improve the life insurance underwriting experience in the hope of removing obstacles and decreasing not-taken ratios. To that end, some have forgone the traditional exam altogether in favor of simplified issue. But the truth is, consumers still aren’t flocking to life insurers, and the results of these efforts have been incremental.

Force Diagnostics has taken a different approach. We’ve developed a consumer-centric process featuring rapid testing that delivers results in 25 minutes. Tests are performed outside of the home in retail clinics and pharmacies, and results are immediately transmitted directly to the carrier’s underwriting engine for immediate processing. Because of the speed to results, innovative insurers and reinsurers could offer an accurate quote for life insurance to their consumers within 24 hours. And with the benefit of testing with fluids (HbA1C for diabetes, cotinine for nicotine, lipids for cardiovascular risk and the presence of the HIV virus, as well as body mass index and blood pressure), insurers may offer the majority of their products quickly and with assurance.

See also: Next Generation of Underwriting Is Here  

The potential results of using this new process can be seen in this underwriting performance calculator.

Once the calculator is downloaded, you may select a typical life insurance policy from a dropdown menu and enter assumptions that reflect an existing underwriting process. The calculator then shows a comparison on underwriting costs, internal rate of return (or IRR) increases, issued policy increases and the potential effects on persistency. At the end, total costs per app are calculated, as are total profits.

There is tremendous value in improving the customer experience throughout the underwriting process.