Life insurance is a dichotomy of decades-long customer relationships and 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%, depending on type of product. Yet, the costs to manage these discontinued products aren’t decreasing in kind.

In most cases, closed blocks run on legacy policy administration systems, which typically require a higher degree of 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 complex at best, oftentimes taking years to accomplish. Even then, there was no guarantee of accuracy. Not only was the system investment a substantial capital budget drain, but, the expected efficiencies took years to achieve. 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.

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.


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 crosssell 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.


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.


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


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 have been summarized below

  • 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, machine learning and other techniques with system conversions can fundamentally 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 utilize machine learning in data extraction and document classifications to “learn” what information has to be taken from each form, each process and each individual.


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


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, it is fast and costeffective 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. This might require countless hours and investment to fix the administration systems. It can alternatively be addressed through RPA which can provide ~20%-30% efficiency gains within a period of 3-6 months and 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.


There are multiple technologies within Artificial Intelligence umbrella 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, automating transactional processing, or for claims processing. The results show that employing AI and Machine Learningbased utilities to extract structured, semi-structured and unstructured data from documents can improve efficiency by 30 percent, 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 ultimate 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, reduces 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 proactively 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.


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. This also serves as an opportunity for insurers deploying elimination or deflection strategies to lower cost channels and proactive chat triggers on web sites, in addition to using analytics to segment customers most likely to use web chat or other lower-cost channels to deflect call volumes and improve customer satisfaction. Given the option, consumers prefer online communication to making an inbound call, as long as they get the answers they need. While it sounds like a little change, the financial impact could be significant, depending on current call volume and customer personas

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 resourceintensive 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:


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 of these professions.

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 ETL tools do not significantly reduce the overhead costs associated with conversions.


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.Need for a 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.Create a business case that is proof of value It’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 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 To be successful, 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 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 new 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.Select a provider that’s a cultural fit and transforms the status quo Insurers should seek out and work with a provider who understands their business, is aligned with the leadership’s vision, and 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.


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