Insurance companies have a lot of information about their policyholders, but just because they possess the information doesn’t mean they really know their policyholders.

Let’s be honest – insurers don’t have the ability to access all this data, 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. have is being used for this purpose.

But what if life insurers used this data?

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

Let’s say a certain policyholder has been missing premium payments for the last few months, which could suggest that the customer is nearing lapsation. 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. Any other information about this particular policyholder, such as knowing he moved to a low-income neighborhood three 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, it would be able to act preemptively and 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?

The answers to all of these pressing insurance questions can be revealed by the data.

A buried treasure, just waiting to be discovered

Insurers are sitting on tons of data, but it doesn’t mean much if they can’t comprehend it, draw connections or gain insights.

Atidot’s technology uses predictive analytics and AI to help insurance companies do just this. By augmenting customer data with 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 underinsured, 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 invisible value in their existing books of business. They’ve been sitting on a goldmine all this time.

Amazon changed the rules of retail. It is the factory, wholesaler, retailer, producer, and delivery mechanism for the product. It controls the entire value chain. This is not just an incremental change, it is a revolution. The factory knows who buys the product, what they buy, why they buy it, when it is easier to sell, and which customers are satisfied. This provides Amazon with more power and influence than Walmart, Target, and Bloomingdale’s combined.

The revolution is not a change in technology from one stack to another. It is a change in mindset. Amazon taught us that a bookstore that controls the value chain can understand and analyze the end customer. Amazon focuses on customer success, easy onboarding, self-service, captured value, and perceived value for the customer. Think how easy it is to start using Amazon delivery, Alexa, and Prime, and think about the level of engagement and customer satisfaction compared to any other retail network. Consider what could happen if an insurance company was no longer a factory that manufactures policies but was instead a service provider to a client who needs risk management. Instead of a premium, think of a subscription to a risk management solution.

Amazon optimized the assembly line. Unlike their competitors, they focused on customers’ happiness, retention, and satisfaction. They do not have better products, but they do have a service focused on the customer and not on the intermediary. This makes a profound difference.

Insurance companies that focus on improving their services would be eager to personalize their products to make sure they meet the customers’ needs throughout their life journey.

They focus on the alignment of incentives and interests to make sure growth is based on customer happiness. They aim to reduce friction in the onboarding process and turn the product from a ‘push’ product to a ‘pull’ product because people need life insurance. The current situation is that insurance companies focus on the wholesaler and distributor, often at the expense of the customer’s best interest. That is the main reason for policyholders’ dissatisfaction, which eventually results in resistance to buy insurance or lapse.

New technologies are aiming to change the status quo. They provide insurers with the tools they need to offer their customers the right product at the right time throughout their life journey, and predict their needs in a frictionless way. This is the revolution.

These technologies are AI and machine learning based. They use existing and flowing data to constantly improve customer engagement and services. As a result, they optimize the Life Time Value of each policyholder, growing insurers’ revenues and adding to the customers’ happiness and retention.

“While uncertainty continues to abound in equity markets, clients will favor the principal protection and asset growth that fixed annuities provide.”-  Phil Michalowski, Head of Annuities with MassMutual

The world economy is slowing down. Inflation is higher than it has been in several decades, leading to an increased cost of living and financial uncertainty for many people and companies. While many businesses are suffering from this uncertainty, annuity companies are well-positioned to weather the storm and emerge stronger on the other side. Here’s a closer look at how the current economy affects the annuity market.

The central bank’s series of aggressive interest rate hikes this year have contributed to the increase in annuity sales. Sales of annuities are growing significantly faster than other fintech sectors, such as life insurance. LIMRA’s preliminary estimates indicate that annuity sales rose 27% from a year earlier to $79.6 billion. 70% of the total sales were fixed annuities. Fixed-rate volume was $29.8 billion, almost 160% higher than a year earlier. Volume was more than twice as high as for traditional variable annuities, once the dominant product in the industry.

Why is this?

According to Todd Giesing, assistant vice president of LIMRA annuity research, “Continued equity market declines and rising interest rates drove investors to continue seeking protection.” Insurers are offering good terms and guarantees on all types of annuities amid rising interest rates, making annuities a more attractive product for investors fleeing volatility in the stock market. With rising interest rates and market volatility, consumers are searching for safer investments. Additionally, instead of buying a single $100,000 fixed-rate annuity, people are spreading the money over several products.

So if sales are on the rise, what are the challenges that annuity providers face?

Due to the short duration of annuity products, companies tend not to engage with existing customers. So while getting annuitants might be easy, keeping them is more challenging, and the industry suffers from a conservation problem and a high surrender rate.

When it comes to annuitants, most are happy with their products and won’t consider surrendering and looking elsewhere for a new product. However, some customers will constantly look at the market to see if better products are available and would surrender if they found a more attractive product. 

How do you keep annuitants from leaving your company?

Implementing the right modernized technology is critical for decreasing the surrender rate and increasing retention, driving higher top-line growth. 

AI and predictive solutions can provide insights into existing customer data and automatically increase engagement with at-risk annuitants. AI technology can use internal and external data (issuer’s data) to help engage, retain, and upsell their existing customers. Additionally, AI insights can help issuers sell directly to customers by using product and customer channel data to tweak pricing and commissions based on customer elasticities.

This is the new era of optimizing and monetizing the potential of existing customers. It is not only the level of service expected from the industry but also a new way to grow profits and increase customer satisfaction.

21.3 percent of insured U.S. adults ages 19 to 64 are under-insured (Commonwealth Fund Biennial Health Insurance Survey, 2020).

To solve this problem, we need to understand what is causing it. One of the most common reasons for this problem is that insurers are not always aware of changes in changes in the lives of their policyholders. Any change in policyholder status from marriage, to divorce, to the birth of additional children is likely to have an impact on the amount of insurance needed.

While insurers possess a massive amount of data about their policyholders, they often don’t have the tools to access it, and are therefore unable to use it to guide business decisions.

Atidot helps carriers ensure that their policyholders have the correct coverage by identifying pockets of untapped opportunities for up-sell.

Just think about the potential opportunity

More and more insurance companies understand the under-insurance issue and the great opportunity it represents for the industry. They realize that a significant percentage of their book of business is underinsured but have no easy or timely way of knowing which of their policyholders fall into this underinsured category. They have masses of data but are unable to access it or make sense of it and, therefore, cannot use it to gain insights about their policyholders to ensure that their policies are up-to-date and accurate.

Know your customer

Since this issue haunts many insurance companies, there is significant excitement around Atidot’s insurtech solution that uses artificial intelligence (AI), machine learning (ML), and predictive analytics to help life-insurers optimize profits within existing books of business.

What does this mean exactly? Well, Atidot actually augments customer data with public, open-source information as well as its own proprietary data and can predict insurance-specific behavior based on trends. The solution enables insurance companies to know their customers better – and to know precisely which of them is potentially underinsured.

That’s not all…

Atidot works closely with carriers to ensure that the solution is tailored to each carrier’s needs. Qualified leads are generated based on the company’s business plan and distribution channels to ensure that the under-insurance leads they are looking for are from the right target group.

The system can also group the leads as per the carrier’s request. For example, we can provide each carrier with different lists of leads depending on their level of under-insurance, geography, distributor, or any other factor, along with their likelihood to convert (high, medium, low).

A salesperson would be provided with a reason why the lead had been chosen, be it a house move, change in marital status, policy conversion points, preferred products, etc, which helps to focus the call and provide the right bundle of insurance.

Precision pays off

With Atidot’s technology, insurance companies can predict policyholders’ behavior and can offer policy adjustments accordingly in order to ensure that they are receiving the proper coverage.

Being able to know precisely who among your policyholders is a likely candidate for under-insurance is a huge game changer for insurance companies that for generations have been drowning in their data and unable to leverage it to guide business decisions.

With millions of underinsured Americans out there today, the opportunity to grow the business is immense. Insurance companies just need the right tool to be able to reach it.

Hailed as one of the most significant pieces of legislation affecting the ways Americans save for retirement, the SECURE (Setting Every Community Up for Retirement Enhancement) Act of 2019 made much-needed changes to retirement plans and provisions relating to life insurance products.

While the bill added plenty of provisions and changed standard pieces of tax planning, many felt it didn’t go far enough. They needed further reform, hence the introduction of The SECURE Act 2.0, the latest bill waiting to pass legislation that will make more changes to retirement plans and annuities.

The bill was passed in March by The House of Representatives and is expected to be ready to be signed by President Biden by the end of the year.

In anticipation of this, we will take a closer look at how the SECURE Act 2.0 influences the life insurance industry with regard to annuities.

Annuities

When purchasing an annuity, one can specify when they want the income stream to start. However, the maximum that can go into a qualified longevity annuity contract is the lesser of $135,000 or 25% of your retirement account. The Act would remove the 25% cap and potentially increase the maximum amount allowed in the annuity contract to $200,000.

Currently, the IRS mandates a minimum annual withdrawal amount from tax-deferred retirement accounts, such as annuities, from age 72. That means, at age 72, one must start taking money out of their retirement accounts and paying taxes. If passed, the Act would raise that age to 75, allowing people more years to grow their investments before paying taxes on them. This age increase can potentially increase revenue for carriers as well, as a few more years of premiums would be paid before withdrawals are made.

For Employers

Employers can choose a group annuity to include within a 401(k) or similar plan. If passed, the Act would shift liability risk from the business owner to the insurance company, relieving businesses of any reliability risk they might be prone to from before.

For Employees

The Act would require yearly disclosures illustrating how much the participant can receive if the account balance is used to provide a lifetime income stream during retirement. In addition, the Act would allow employees who purchase an annuity in their 401(k) to transfer it to a different employer 401(k) or IRA plan without paying surrender charges or penalties.

For Life and Annuity Providers

The world is changing and so are people’s finances. Life and Annuity providers need to understand the SECURE Act to stay ahead of the game and cater to their customers’ changing financial needs. With its ability to predict trends in customer behavior, companies that adopt AI and ML technologies will be able to design new products for people whose financial situation has changed due to the economic situation. Increasing retention and providing better customer service will put companies using AI and ML technologies at an advantage over their competitors.

The SECURE Act is a sweeping piece of legislation that makes changes to retirement plans and provisions relating to life insurance and annuities. While some of the changes are welcome, others have been met with criticism from industry professionals. It will be interesting to see how the industry adapts to the new law and its impact on American workers’ retirement planning.

In this world of instant communication, consumers have come to expect personalized user experiences from their service providers. Recognizing this, most industries can now offer their customers products that match their immediate and long-term needs, wrapped in tailored messaging that speaks their language and caters to their lifestyle, behavior, attitude, and preferences.

This is the basis for the data-driven, ‘People Like You’ marketing strategy commonly used in B2C campaigns.

There is so much untapped potential for personalization in the life insurance world. Most life insurers currently use traditional segmentation tools such as Tapestry, Mosaic, and even Facebook, as the basis for personalizing their marketing activities in the Life Insurance Next Gen Personalization Methodology vertical.

In short, segmentation programs classify people into over 60 groups and types based mainly on zip code data, creating unique lifestyle segments relating to demographics and socioeconomic characteristics. Tapestry, for example, describes US neighborhoods in easy-to-visualize terms, ranging from “American Royalty” to “Heartland Communities.”

But what if you could add a totally new dimension to traditional classification? Tracking recurring behavioral patterns can create hundreds of thousands of additional granular segmentations, providing a full and complete insight into your Book of Business!

Atidot leverages these segments with its machine-learning capabilities. We use external information from public databases as well as internal sources such as CRM systems.

Additionally, you can create a platform for new marketing strategies that are more accurate, enabling marketing campaigns based on real-time customer data. Occupation, the proximity of hospitals, the day of premium payment, investment patterns, and more can impact the Lifetime Value of your policyholders and can help create additional revenue sources.

This is the next-generation platform for product personalization and tailored marketing campaigns, new risk modeling, lapse strategy, and more. Moreover, machine learning technology can keep learning as policyholders’ actions are recorded to create more accurate and additional profiles, thus detecting the most profitable potential customers.

Real-time data is the basis of our Nano-Segmentation Methodology. One example of a behavioral pattern is the client’s payment date. Different dates have different meanings: if someone is repeatedly late in their premium payments, the machine can identify a pattern such as “these people, if their age is between 40-50, tend to lapse within 5 years”.

This behavioral pattern could have a totally different meaning if they are between the ages of 20-30. In that case, it might suggest that they are busy, successful people who don’t have enough time to attend to their finances.

Another example of a Behavioral Pattern relates to sensitivity to financial market trends. For instance, when the Bond Index is on the rise, some people tend to invest more in their own pension funds.

Atidot’s technology ties such behavior patterns into different groups, such as the “Trendsetters” segment that tends to invest when the Bond Index is up, indicating that they have a financial orientation and can be treated in two different ways. For instance, if the client owns a policy with a 4% guaranteed premium that was issued years ago, they should be encouraged not to lapse that policy.

On a strategic level, if you are catering to this specific group, you might launch a marketing campaign in channels that cater to this specific group of trendsetters with a financial orientation, for instance, via bloggers associated with style, targeted ads in fashion publications, direct email, etc. The possibilities are endless.

So, since AI and ML capabilities can be trained to translate real-time events into real-time data, this newfound segmentation becomes the platform for tailored marketing targeting that can factor in any real-time relevant data. For instance, changes in global food and oil security can impact the demand for life insurance, generating real-time targeting on a weekly or monthly basis through traditional channels such as email campaigns, social networks, etc.

This next-generation approach will set the basis for marching your company into the challenges of the next era.

Traditionally, life insurance companies store their data in different formats and in different systems, isolating it from real analysis. But the legacy systems 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 use, actuaries are often not equipped with the know-how to apply the latest technologies to their own data and to accurately predict the future behavior of their clients.

Life insurers are realizing that outdated ways of doing business are no longer 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, 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.

Some companies are adapting to this new landscape by harnessing new data analysis tools to enable life insurance companies to monetize their in-force data and customer base. 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.

Unlocking the structure within unstructured data is the key to further insights, which are enriched by publicly available external data. Advanced models can apply the features most relevant to insurance. Every time data is entered into these machine-learning models, the process is quicker than before, giving insurers greater insights in a significantly shorter length 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.

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 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 on board?

Failure to adopt new technologies is a prominent factor. In 2019, some 68% of insurance agents under 40 said that the insurance industry is too slow to adapt to change. There are 310 Insurtech startups in the US alone, so why is it so slow? The answer has to do with business culture, slow financial process, and low-level digitization but is also connected to low consumer engagement.

In the 90s, Jeff Bezos said 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. This was when booting up still took a good five minutes. Purchasing a life insurance policy, on the other hand, takes 55 days on average.

Customers today expect their service providers to provide service. From the get-go and 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 long-term partners. Unfortunately, the existing client base, representing over 80% of the business, captures less than 20% of the managerial attention.

However, we have seen that by utilizing Amazon-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.

With the outbreak of the Covid-19 pandemic, things are ripe for a change. Digitization 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 in the insurance industry globally grew by 20% in 2020 and is expected to accelerate even further.

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 is the currency of the future. The insurance companies that successfully utilize AI and Machine Learning to power their strategy and provide a customer-centric experience will 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 on these two factors. Therefore, enriching insurance data with qualitative external resources is of great importance.

Traditional life insurers need to become much more proactive in preparing themselves for the fierce competition they will soon face from fully digital, agile Insurtech companies offering friendly, personalized, easy-to-understand 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.

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

This new approach is revolutionizing the life insurance industry, 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.

One of the biggest challenges facing the life insurance industry today is that due to a high burnout rate, a staggering 90% of young agents are leaving the business for other ventures and professions within the first year (source: Investopedia). This creates a huge impact on insurance companies, leaving them with about 30%-40% of their book of business unserviced, or what is commonly referred to as “orphaned policies.”

An orphaned policyholder is far less likely to renew or expand their coverage when they have been abandoned by their agent and no longer have a personal connection to the insurance company. With a serviced policy being 2X more profitable, imagine the additional premium potential hidden within the book!

There is therefore a desperate need to fill this increasingly growing gaping hole by converting orphan policies into revenue opportunities.

Provide a better service 

From a customer service perspective, it has become increasingly beneficial for insurance companies to provide better services to their orphan policies. Personalization of products together with a more personable, friendlier approach from the insurance agents increases loyalty and customer satisfaction. While previously customers were just interested in the policy itself, nowadays they are in search of a good customer experience where they feel valued and cared for. It is, therefore, crucial for insurance companies to move with the times and embrace a more welcoming customer service approach.

Identify policyholders who are likely to lapse

Customers with orphaned policies are often not even aware that they have been abandoned by their agent and are not being adequately served. At the same time, they are in a prime position to receive advice and re-engage with their insurers since many of these orphaned policyholders experience life changes that may require expanded insurance coverage or investment products offered by the carrier.

Finding these policies and ensuring they don’t lapse is vital to an insurer’s financial health. To prevent potentially disastrous relationships, insurers must have the tools to catch policyholders who are likely to lapse. Once identified, they can reach out to them to provide better service, create loyalty, and renew and upsell policies.

Enter predictive analytics and AI-based solutions

Insurers can identify and prioritize orphaned policyholders using predictive analytics and AI-based solutions to analyze the data in real time. Once accurately identified and analyzed to predict customer behavior, insurers can match them to the appropriate advisors based on their segments and attributes.

These technological capabilities can predict these customers’ purchasing potential based on internally and externally sourced public data. This helps life insurance companies better cater to their customer needs, which leads to increased customer satisfaction and can ultimately lead to more profitable sales. Real-time analysis can be provided to enable life insurance operations to reach out to orphaned policyholders, delivering a tailored product experience at the right time to the right person for the right reasons.

Once insurers re-establish these relationships with the customers, they can be more profitable policyholders, rather than non-paying lapsers, and ensure premiums stay on the books.

Properly handling orphan policyholders has been an issue in the insurance world for decades. Leading technological tools can help solve this age-old problem and make both the insurance companies and the customers happy and satisfied – a win-win for everyone!

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.