Life Insurance Providers are

by Dror Katzav, the CEO and Co-founder, Atidot

min read

Life Insurance Providers are Missing Opportunities Presented by Raw and Unstructured Data

“Water, water, everywhere, nor any drop to drink;” Replace Coleridge’s memorable line with “data” and you have an accurate surmisal of the life insurance industry over the last three decades. Dating back to 1700’s London and even, some suggest, ancient Rome, life insurance companies accrue vast sums of data on policyholders’ health, family circumstances, living arrangements, employment, and beneficiaries. The sector might be considered the original “Big Data” business but, until now, unlocking the full potential of that data was a task befitting Hercules.

Traditionally, life insurance companies store their data in different formats and in different systems, none of them compatible, none of them talking to each other. But the old legacy systems of green and orange screens do not provide a means of strategically using data and insurers are subsequently losing out in an increasingly competitive market.

Advances in artificial intelligence, machine learning, and predictive analytics have opened a new world of opportunity for life insurance companies. Digitalization in the 1990’s created an explosion of available data, but for a long time, this surge was not matched by corresponding technological developments that allowed the data to be processed, manipulated, and transformed into actionable insights

One of the greatest challenges currently facing actuaries in the life insurance industry is that while senior management is eager to put the abundance of data to practical gain—to develop a better understanding of policyholders and apply this in marketing, pricing and reserving—actuaries are often not equipped with the know-how to apply the latest technologies to their own data.

Many companies who work with brokers struggle to form a full picture of their clients. Even those who work directly with clients are unable to forecast what they will do and often fail to accurately predict future behavior because companies are unable to unlock the data’s hidden value with the range of tools available to them.

Life insurers are realizing that outmoded ways of doing business are not only sub-optimal but may even no longer be viable. For decades, the sector was slow to adapt to new technologies that other industries were responding to, and entrenched IT departments coupled with insufficient pressure to adapt were the enemies of innovation. Today, insurers are working in a very different climate. In a 2016 PwC survey, some three-quarters of insurance companies acknowledged that their business was going to be affected by technology disruption and feared that their traditional operations might lose to new contenders. A similar percentage of insurers surveyed in 2015 said that they expected to use big data in pricing, underwriting, and risk selection within two years. Competition is greater, premiums are lower and industry disruptors such as Lemonade are trying to upend the industry from a distribution channel point of view.

To address this, many insurers are looking to data scientists to extract value from their data. But most data teams at insurtech companies expect to receive normalized data from the insurers to create a structured data set. Not only is this frequently beyond the reach of smaller companies who don’t count data scientists among their staff – and therefore cannot devote the necessary time and resources to unpack the data, but even among those firms big enough to have their own innovation departments, the time —and money— required to cleanse and normalize the data can be burdensome.

Some companies are changing this reality by harnessing new data analysis tools to enable life insurance companies to monetize their in-force data and customer base. These platforms allow insurers to assess the potential for under-insurance, high lapse risk and profitability to improve up-sale and cross-sale efforts, as well as optimizing distribution channels to develop proactive retention programs. By taking advantage of artificial intelligence, machine learning, and predictive analytics, these systems augment the data, both internal and public, held by life insurance providers, grouping cohorts of policyholders together for meaningful analysis, to find the embedded value in a book of business.

Despite the potential value, the life insurance market, which has not yet made the technological jumps that have revolutionized other sectors such as banking and finance, has been slow to appreciate the value of raw and unstructured data.

Structuring unstructured data is a big headache for insurers, yet it is also a necessity if companies only have standard actuarial techniques at their disposal. Using advanced methods of artificial intelligence and machine-learning, data bypasses these first steps of insurer manipulation, allowing the modeling process to start straight away—a process that is immediately quicker and more efficient.

Typically, after data has been normalized, unstructured data is left out of the final analysis, wiping vast quantities of relevant information. In many cases, the lack of data is itself indicative of a behavioral pattern. Unstructured and manipulated raw data grants insurers the freedom to utilize more features—and the more features, the more insurers can understand why individuals make the choices they do, helping them to build up a more realistic image of their behavior. From a quantitative point of view, the model is improved by ever larger data sets.

The benefits of unstructured data can be illustrated through the example of a free text box that may accompany an insurer’s request for policyholders’ work emails and occupations. When you have free text cells, swaths of data can be lost unless insurers understand how to analyze it and link it to external information sources. For example, the data can be divided into three sets: those that enter an accurate work email, those who enter an email that is incorrect and those who leave the space blank. Through advanced modeling, it can be ascertained that each group behaves in behaviorally distinct ways. From this data—and even, significantly, from the absence of data—insurers gain greater insights into the policy-holder. Those who put in incorrect emails may have lost their jobs, for example, and those who don’t enter a work email may either be unemployed or employed in a field—such as construction or cleaning—where they don’t require an email.

Similarly, if people are asked to enter their occupations manually, there will be tens of thousands of variations—teacher, math teacher, French tutor—that are not statistically significant until the techniques of machine learning are applied: different occupations can then be clustered according to different statistical groups, such as pensioners, teachers, managers, housewives, to extract potentially lucrative data.

Knowing where a policyholder is paying their premiums from, whether from an individual or company account or, for example, the Teachers Federal Credit Unit or the Navy Federal Credit Union is advantageous for the insurer.

Unlocking the structure within unstructured data is the key to further insights, which are enriched by publicly available external data. Advanced models can apply those features most relevant to insurance, for example, U.S. census questions on monthly insurance expenditure and assets in pension savings. Moreover, every time data is entered into these machine-learning models, the process is quicker than before, giving insurers greater insights in a significantly shorter space of time.

Raw data is the key to insurers staying ahead of the competition. Life insurers can continue to do what they do best—but now with the tools to irrigate their data and watch the profits bloom.

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Life Insurers Could Be Sitting

by Dror Katzav, the CEO and Co-founder, Atidot

min read

Life Insurers Could Be Sitting On A Massive Goldmine

Insurance companies have a lot of information about their policyholders – a lot! But just because they possess the information doesn’t mean they really know their policyholders very well.

Let’s be honest – insurers don’t have the ability to access all this information, and even if they did, they struggle to interpret what it all means. Why? Much of an insurance company’s data is siphoned off to different systems within the organization and cannot be leveraged for strategic business decision-making. In fact, according to Willis Towers Watson, less than 20% of the data that insurers have is being used for this purpose.

So, just because insurers have lots of data about their policyholders does not necessarily mean they know their policyholders very well.

 

But what if life insurers did know their policyholders well?

Knowledge is power, and the more insurers know about their policyholders, the more accurate and targeted they can be in their approach. What does this mean?

Let’s say a certain policyholder has been missing premium payments for the last few months. Information such as this could suggest that the customer might be nearing lapsation.

Now, the finance department has this information, but the marketing and sales departments are likely kept in the dark with no access to these important records.

Furthermore, any other information about this particular policyholder such as knowing he moved to a low income neighborhood 3 months ago and that he is a mechanic, is not available to these departments in a clear or timely fashion.

If the organization had access to the whole story, insurers would be able to act preemptively, and to strategize regarding the value of a specific policyholder.

Should they attempt to contact the customer to prevent lapse? Should they attempt a cross-sale? Should they allow him to lapse his policy? How old is his policy? What is his premium? What makes more sense for the business? These are questions that insurers ask all time, and the answers are in their data.

 

A whole pile of gold right there and no one even knew about it

Insurers are sitting on tons of data, but it doesn’t mean much if they can’t comprehend it, draw connections or gain insights.With more information and a clear understanding of who policyholders are, insurers can potentially increase profits and grow the business.

Atidot’s technology uses predictive analytics and AI to help insurance companies do just this. By augmenting customer data with public, open source information, Atidot can predict insurance-specific behavior based on big data analysis and trends. The Atidot algorithm automatically scans all data resources and pinpoints relevant insights about policyholders that insurers can turn into business opportunities.

The solution enables insurance companies to know their customers better – and to know precisely which of its policyholders is likely to lapse, be under-insured or more likely to be cross-sold. It enables insurers to employ a more customer centric approach, thereby increasing customer satisfaction and loyalty.

With Atidot, insurance companies can uncover embedded value in their existing books of business which they never knew was there – as if they’ve been sitting on a gold mine all this time. 

 

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Elementor #234

By: Dror Katzav, Co-Founder & CEO, Atidot

min read

Next Generation Life Insurance for Next-Gen Customers

The Current State

The U.S. life insurance industry’s average annual growth over the past 10 years has been less than 2% in nominal terms and negative in real terms. Meanwhile, the average face value amount of individual life insurance policies purchased in the US has steadily increased from $110,000 to over $170,000, (McKinsey Research) indicating that life insurers are failing to reach the middle market.

According to McKinsey’s mass affluent research in 2015, only 65% percent of Americans who are married with dependents have a life or an annuity policy, while 97% own an investment account. Different research, made by LIMRA, shows that only two-thirds of Gen Y consumers have any kind of life insurance compared with three-quarters of Gen X and Boomers. In addition, fewer Gen Y consumers own individual life insurance (34 percent) than Gen X consumers (45 percent). More than half of Baby Boomers report owning individual life insurance (52 percent).

 

Why is the Life Insurance Industry struggling with getting those Next Gen’s onboard?

Failure to adopt new technologies is a prominent factor in this struggle. In 2019, some 68% of insurance agents under 40 said that the insurance industry is too

slow to adapt to change. On the other hand, there are 310 Insurtech startups in the US alone, so why is it so slow? The answer is complex and has to do with business culture, slow financial process, and low-level digitization but is also connected to low consumer engagement.

Jeff Bezos used to say that the biggest impact on e-commerce would be to reduce the friction between an intent to buy and the time it takes for your computer to reboot and connect to the Internet. Bezos said it in the `90s when it was still a decent 5 minutes process. Purchasing a life insurance policy takes 55 days on average.

 

55 days.

Customers today expect their service providers to provide service. From the get-go and all along the journey, for life. Amazon offers you products that people like you buy, Spotify learns your music preference. Similarly, life insurance companies have the opportunity to be a long-term partners. Unfortunately, the existing client base, the in-force policies, representing over 80% of the business, captures less than 20% of the managerial attention.

However, we have seen that by utilizing Amazon/Netflix/Spotify like Engines for providing service, targeted Agent Communications, and recommendations, carriers can more than double the premium received over the term of the policy.

This is where new and advanced technologies play a role in bridging the gap between the ‘Old’ and the ‘New’ to increase traction with next-generation customers.

Clearly, with the outbreak of the COVID19 pandemic, things are ripe for a change. Digitalization turned from an interesting trend to a necessity as companies, and more importantly, brokers and agents transitioned to working on a remote basis. Digital adoption with the insurance industry globally grew by 20% in 2020 and is expected to accelerate even further in 2021. Companies jump on this train in order to reduce costs through automation but they should embrace the opportunity to turn to big Data’, enabling technologies such as AI and Machine Learning and bringing significant value across the value chain.

From the top 200 insurers that were surveyed in a Deloitte research, it was stated that 23% of their premium volume was a result of new initiatives and that they expect it to grow by 33% in the next 5 years. The number one trend is data innovation.

 

Next-Generation Technologies

Data has become the currency of the future. The insurance companies that will successfully utilize AI and Machine Learning as their underlying technologies to power their strategy and provide a customer-centric experience shall prevail.

The barrier to applying new technologies to Life Insurance is not only a lack of digital data but also the low quality of the available data. The ability to produce intelligent insights via AI algorithms is totally dependent upon these two factors.

Therefore, enriching insurance data with external resources that are qualitative is of great importance.

Traditional Life Insurers need to become much more proactive in preparing themselves for the fierce competition they will soon be facing from fully digital, agile Insurtech companies offering friendly, personalized, easy-to-understand life policies. NextGen customers expect no less – 88% of insurance consumers demand more personalization from insurers, but until now most carriers haven’t implemented a reliable means of providing that personalized service.

By adopting advanced technologies, insurers now have the means to fully understand their customers and offer them the right policy and the right time at the right price, tailored to the exact profile and risk factors. They have the means to know not only which of their brokers or agents are currently excelling, but by utilizing Predictive Analytics are also able to predict which will lose traction in the next quarter or year.

AI, Machine Learning, and Predictive Analytics are the underlying engines that power the new product and distribution strategies that carriers form to enable them to better understand their customers and distribution channels.

New technologies can transform data into actionable insights, thus enabling providers to empower their agents to address the unmet challenges and to optimize their books of business.

This new approach is slowly revolutionizing Life Insurer’s business conduct, and together with other advanced front-facing systems, next-generation customers can learn to expect and receive better products and services from Life Insurance providers.

Even the ancient Greeks knew, if you stand still the Amazons will kill you. Go data.

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The Impact of a Well-Defined Business Goal on Churn Model Persistency

By Alexia Jami Data Science Team Lead, Atidot

min read

The Impact of a Well-Defined Business Goal on Churn Model Persistency

How do you achieve 98% accuracy on your churn model? That’s easy – predict that NO ONE will ever churn and…tada! 2% of the population will churn in the coming period. By predicting that everybody will stay, you will reach 98% accuracy. However, you will also completely mess up your goal. This is why, in any classification model, accuracy is not what matters.

Gaining a competitive edge by limiting the churn of your valuable customers is a holy grail for businesses everywhere. It’s vital to use the right tools that offer you the precision you need to target the right people at the right time and avoid loss of business.

Churn propensity models aim to predict the churn probability for each customer in a defined period of time. The challenge is to carefully define the business goals so that they impact the model and vice versa.

This is a rare opportunity where data science impacts business outcomes directly. To achieve this, data scientists need to understand the business model and business impact. By doing so, you can fine-tune your models, carefully built over massive data and smart techniques, to address the exact targets the business people need and affect customer engagement.

 

How do we achieve this? 

The data scientist must have a clear goal of the score they want to achieve, which needs to reflect the goal of the project.

When developing a churn model, the first question is simply how do you want to communicate with your potential churning customers. For example, with the same budget, you can reach many more customers by email than by phone.

Next, every churn model, and every classification model, must achieve the right balance between what is known in the data science world as precision and recall.

Precision is the proportion of actual churners in the predicted churner population. A precision score of 60% means that out of 100 predicted churners, 60 actually churn.

Recall is the proportion of actual churners that we predicted with our model. A recall of 30% means that out of 100 churners, our model caught 30 of them.

So for a phone campaign, we need high precision because we want every call, which can be costly in terms of resources allocated, to be effective in achieving its result. A less expensive email campaign allows us to be less precise in identifying a larger number of churners.

For instance, when you have a small group of well-paid financial advisors who are compensated based on retention success, you want to make sure that every call they make is effective in the sense that they’re targeting customers who are actually in need of financial advice. You want to avoid a situation where they call people randomly, wasting their valuable time and reducing their enthusiasm and focus.

We also have to ensure that our outreach to potential customers doesn’t result in contacting those who are not at risk and have the opposite effect of causing them to churn as a result of the call.

So the strategy must define the balance between precision and recall based on clear business goals while ensuring that the score reflects the model based on specific business needs. Ultimately, every customer that is “saved” by implementing a churn model is valuable revenue for your business and increases your competitiveness in the market. 

Good data science practice is to balance precision and recall based on the needed business outcome. In this case, various business requirements might lead to very different data science models with different accuracy measures.

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Reach An Untapped Market With AI & Predictive Analytics

Dror Katzav, Co-Founder & CEO of Atidot

min read

Reach An Untapped Market With AI & Predictive Analytics

According to the US Census Bureau, women make up 50.8% of the US population. That is more than half of the population, yet women still remain an untapped target market for life insurance companies.

Consider these 5 interesting data points based on LIMRA’s life insurance consumer studies:

  • 47% of women own life insurance.
  • Approximately 14% of women — more than 18 million — lost their life insurance coverage in 2020.
  • While women express more concern about COVID-19 overall, women were less likely than men to say they planned to buy life insurance due to the pandemic (29% versus 33%).
  • Only 22% of women feel very knowledgeable about life insurance. In contrast, 39% of men say they are very knowledgeable about life insurance.
  • Women were more likely than men to say the major reason they have life insurance is to pay for burial expenses (53% versus 44%). Women were much less likely than men to consider it as a way to supplement their retirement income (33% versus 24%).
  • 44% of uninsured and underinsured women say they need (or need more) life insurance

Based on these numbers, you can see that there is still a very large gender gap when it comes to life insurance. With the right data, you can tap into the female market and start closing that gap.

How can you find this data?

Predictive models, enriched by public external data sources, can quickly detect which women have orphaned policies, are on the verge of lapsation, or are in need of a different policy.

AI based models can produce highly accurate results by detecting up to 70% of customers that are about to lapse, saving you time by pinpointing which customers in your existing in-force need to be contacted. These models can tell you which policies are unassigned or potentially underinsured.

Those potential prospects can be ranked by their revenue potential to ensure optimization of resources. Moreover, some platforms can have the inherent capabilities to trigger marketing reach out to those customers, offering an immediate remedy to your customer’s problems.

With AI technology and predictive models, you can generate new revenues from untapped sources while simultaneously providing valuable service to your customers, increasing loyalty and customer satisfaction.

What percent of your in-force is women?

And how many of those women are underinsured or at risk of lapsing?

As International Women’s Day approaches, there is no better time to tap into this untapped market. Take advantage of existing technologies to close the life insurance gender gap and ensure your customers are getting the coverage they need.

About Atidot

Atidot is a nimble growing InsurTech start-up building an innovative AI, Machine Learning platform aiming to change the life insurance industry. We are helping insurers to become data-driven and produce insights to inform decision making and drive new business strategies at the corporate level.

Atidot’s cloud-based, SaaS platform is tailored specifically to the needs of the life insurance industry. We can monetize your data, augment it with external data, and build accurate projections of your in-force book of business. We work with tier one life insurance companies in the US and in the EU and were awarded ‘Cool Vendor’ by Gartner in 2019.

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Life Insurers- It’s Time To Optimize Your Data

Dror Katzav, Co-Founder & CEO of Atidot

min read

Life Insurers- It’s Time To Optimize Your Data

Life insurance margins continue to be squeezed due to the plunging interest rates, as well as the precipitous drop in the stock market and global economic crises. The much-awaited rise in interest rates has not materialized, and recent events such as COVID 19 and high unemployment rates make it clear that they will continue to plunge. As a result, it is crucial for life insurance providers to find new ways to maximize the revenue opportunities available to them, such as locating current policyholders who possess the potential for substantial upsell, and next-gen customers that are looking for personalization, all are likely to purchase better life insurance and become a Very Important Policy (VIPs). A tailored approach for these VIP customers will be exponentially valuable in the current turmoil. While insurance companies are aware of who their current important policyholders are, how can they better understand and identify the policies with the most potential? Once identified, how can they optimize methods for best engaging and converting them? The answer lies in optimizing the data they already have to generate insights on the life cycle of their policyholders. Understanding and predicting policyholder behaviors and alerting the agent at the right time to a potential change or discrepancy in the customer’s profile could be a saving grace for life insurance providers in times of economic uncertainty.

 

Looking Inward for New Revenue 

Now more than ever, insurance companies must pay more attention to the untapped potential in their book of business. Interest rates have dropped significantly, drastically affecting the assets and liabilities of providers. As lower interest rates make the insurance company’s products less attractive, sales continue to drop, further reducing the revenue available for investments. 

While insurance companies may choose to address problems in the market by looking for new opportunities for growth to ensure efficiency and profitability, they should be focusing on optimizing what they already have – starting with their book of business.

 

Finding Gold in a Mine of Data 

How can carriers find untapped segments? The answer already lies at their fingertips. The vast amount of data they already have can assist them in clearly identifying the opportunities for upsell. 

New technologies can transform the data into actionable insights and prediction data, enabling providers to empower their agents to address the unmet areas and optimize their books of business. 

Why is it still so hard to identify the most important users? Many agents are still largely relying on subjective memory and personal experience rather than basing actions on statistics and data. 

By having amassed massive quantities of data, life insurance providers have the ultimate big picture of their customers. Consumers and providers, however, lose out when carriers don’t zoom in on segments and nano-segments and therefore fail to identify opportunities for upsell. Even when potential upsell is identified, if agents are not informed with actionable 

insights, none of it matters. 

Insurance carriers know a lot about every policyholder based on the data they hold. Utilizing predictive analytics to dig into the data and identify the opportunities for upsell ( i.e.,the underinsured policyholders) can create a win-win-win situation for policyholders, agents, and carriers alike. The segments that can be revealed include those that answer to the following questions:

  • Has a policyholder just moved into an affluent suburb with excellent schools? Successful predictive analytics could indicate that this family should belong in another segment, as predictive analytics calculates that they will likely move into a higher income neighborhood, buy a more expensive car, etc.
  • What if a married father of 3 starts paying rent for a 1 bedroom home. Could this mean that a divorce is at play? If so, his policy would need to be fixed and redirected to benefit kids, and no longer his wife.
  • Does the policyholder enjoy skiing? If a policyholder has a preference for extreme sports and has taken out a policy to cover skiing holidays, they would be segmented into a specific group. If within that group, 90% of them have a certain plan, but 10% still don’t, this is a prime and untouched opportunity for agents to target that 10%.

Empowering their agents with information that would identify their underinsured policyholders who have a great potential for upsell can maximize revenue on their book of business in the long run. To achieve this goal, they need to turn inwards and draw on the raw data they already have. 

 

The Right Touch at the Right Time 

Tapping into the life-cycle of a policyholder and alerting the agent at the right time to a potential change or discrepancy in the customer’s profile could be a saving grace for life insurance providers in economic uncertainty. 

On top of this, the client receives more personalized attention: 88% of insurance consumers demand more personalization from providers, but until now providers haven’t had a reliable way to provide personalized service. Any attempts to provide this service, such as frequent check-ins, annoy rather than provide value to the customer. 

Once the customer’s growth potential is identified, agents must also know how to maintain them. 

This critical interaction, which sometimes lasts no longer than 5 minutes, could make or break an agreement. Knowing when to contact the customer is crucial. Insurers should be careful not to offer the wrong policy to the wrong person at the wrong time. This is not just to avoid wasting effort and resources,rather, they could be risking the lifetime value of a customer, as the customer may be reminded that they don’t necessarily need the policy and thus cancel it. 

Some insurance companies are still unaware of the most accessible and lucrative solution: finding the potential within their book of business through accurate data collection and processing the data they already have. Using predictive analytics to identify the core policyholders who are underinsured, is a win-win for both agents and clients.

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