How does the SECURE Act influence the life insurance industry?

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.




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.


Converting Orphaned Policies into Revenue Opportunities

One of the biggest challenges facing today’s life insurance industry 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 that 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 customers’ 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!

Insurance company – from manufacturer to service provider

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.

Your policyholders could be significantly under-insured. What can you do about it?

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 very 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 that 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 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 who 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.

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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Atidot: A Leading Cloud-based Platform for the Life Insurance Industry

Dror Katzav, Co-Founder & CEO of Atidot

min read

Atidot: A Leading Cloud-based Platform for the Life Insurance Industry

With the emergence of modern technologies like artificial intelligence (AI) and machine learning (ML), many industries got disrupted. Exceptional transformations took place in the way industry processes were run, making them more streamlined, efficient, and cost-effective. As industries gradually started adopting these modern technologies, more accurate and precise outcomes resulted in organizations understanding their client’s needs better and delivering them more personalized solutions.

One name among the companies that are leveraging technology to provide customized solutions to their clients is Atidot. Spearheaded by Dror Katzav, the CEO and Co-founder of Atidot, the company is a leading AI cloud-based platform tailored specifically to suit the needs of the life insurance industry, enabling insurers to grow their customer’s Lifetime Value and create new revenues. In the following interview, Dror talks about the exemplary personalized solutions that the company has been delivering since its inception.

Please brief our audience about your company, its USPs, and how it is currently positioned as a leading player in the financial services space.

Atidot is an Insurtech company providing AI and ML solutions for life insurance companies. Atidot offers a cloud-based platform that provides data-driven insights to decision-makers, drives new business strategies, and creates new revenue streams using AI, machine learning, and predictive analytics technology resulting in optimization and monetization of insurers’ books of business. We are currently one of the few companies with a tailored solution designed by a team of actuaries and data scientists that match the life insurance industry’s needs.

Atidot works with leading life insurers, such as Guardian Life and Pacific Life, mainly in North America and Europe. Because of our unique positioning as an AI solution provider that works with carriers to disrupt their organization, Gartner named us ‘Cool Vendor 2019’, in insurance. The company also was selected as one of the ‘Top 10 Insurtech’ companies by CIO Magazine and is included in ‘Insurtech100’ most innovative Insurtech companies by FinTech Global.

What other services/solutions does your company offer, and how are these impacting the industry and your clients?

Because we view AI solutions as such that have a ripple effect throughout the value chain, we offer many AI-based solutions for life insurers. Today, we focus on providing insurers with insights and analytics about policyholders to help them understand their needs and unlock the potential these customers hold. In addition, we offer the ability to operationalize these insights with a complete marketing and operation package to drive new business.

In essence, we can manage the whole digital communication with an existing book of business and help customers make the transition from offline to online, generating upsell and cross-sell opportunities.

We also focus on solutions that enable accelerated and automated underwriting, detect inefficiencies in the underwriting process, produce insights on distribution channels and other solutions that will help an insurance company become more nimble, efficient, and most importantly, more profitable.

Being an experienced leader, share your opinion on the impact of the adoption of modern technologies such as AI, big data, and machine learning on the finance niche, and what more could be expected in the future?

Life insurance used to be simpler a decade ago. Insurance was bought, not sold, and insurers became very wealthy. But the world changed during the last few years, specifically post-COVID. Interest rates are lower and people expect a different level of service today, which they are struggling to get from their insurance provider. Companies don’t really know how to provide the expected level of service where everything is friendly, self-explanatory, and delivered at the speed of light.

AI and machine learning technologies are the power behind product and marketing strategies that can provide better life insurance to customers. These technologies can ensure that people are provided with the right product or service at the right time and the right price. Advanced technologies can ensure that more people can be eligible for life insurance, and those who get it are provided with much better service.

‘One size’ doesn’t really fit all in today’s world, and there are many variations and grey areas when insuring people. What if you could tailor a bespoke suit to people instead of providing one quote? What if they could be insured at the exact amount that fits their needs? Right now, there is a huge uninsured or underinsured population. If you could provide a more personalized solution to each of them, you could potentially serve better as a company.

If you think like Amazon, the policyholders can choose from a pool of products and services. They can pick and choose according to their needs and situation in life. It is a shift in paradigm in the way insurers view their customers. The customer is placed in the middle, and the data governs the interaction insurers have with them. That data is being managed and extracted with technologies such as AI and ML.

Taking into consideration the current pandemic, what initial challenges did you face, and how did you drive your company to sustain operations while ensuring the safety of your employees at the same time?

Atidot, as a nimble start-up, maintained its ability to respond to the impact the pandemic had on insurance companies. For a while, we stopped recruiting and shifted into a hybrid model of work where we met at the office twice a week and worked from home

the rest of the time. That helped our employees stay safe and cope with that difficult time where the whole family stayed at home, and the children had no school.

Luckily, the life insurance industry didn’t really suffer from the pandemic, and most insurers experienced a record year. In fact, COVID accelerated many of the processes that started before the pandemic, such as digitization and moving to a cloud infrastructure. So, once we realized that the market was opening up to us, we started growing significantly in terms of adding resources and expanding our product offering.

That said, during COVID, there were many things that insurers could do to help their customers, like providing cash value loans and other financial products that could provide relief. But it needed an immediate response from the insurers that usually rely on their channels and have a long response time due to culture and complicated internal processes.

What if you could send the right offer to the right customer with a personalized message that will give them the kind of financial security they were looking for with just the push of a button? That would surely do wonders, not just in terms of loyalty to the company but also in terms of creating new revenue streams that would benefit the top line of companies. This is where Atidot fits in, and insurers understand that.

How do you envision scaling your company’s operations and offerings in 2021?

Right now, we see a huge interest in the market for our end-to-end packaged solution that provides analytics and operations combined. Insurers provide us with access to

their data (anonymized), and we tailor the right product and marketing strategy that translates into new revenues.

This is often provided in a rev share model where we contribute our resources and infrastructure, and the carrier provides access to the data. It’s a win-win strategic model where all parties benefit from growing their business with minimal risk.

Exhibiting Brilliance

  • “Given the current state and rapidly shifting buying trends, we were seeking a predictive analytics solution to help us drive future product performance in the marketplace with a high level of confidence,” comments Mary Bahna-Nolan, SVP, Head of Product Innovation and Strategy Life Insurance Division, Pacific Life.
  • “The iPipeline/Atidot, predictive analytics models, cover multiple dimensions of our business, and we expect this to provide valuable guidance immediately after implementation. We are excited about enriching our analytics with new, actionable insights to guide our decisions, minimize risk, and optimize efforts to grow our business while better serving the needs of our customers.” (Insurance Innovation Report, June 2020) Mary Bahna – Nolan, Head of Product Innovation and Strategy, Life Insurance Division, Pacific Life

About the Leader

Dror Katzav is Atidot CEO and Co-Founder. Prior to co-founding, Atidot Dror completed an 11-year career as a team leader and project manager with an elite IDF (Israeli Defense Force) Unit, leading state-of-the-art technology development in intelligence. Dror led teams in artificial intelligence (AI), data solutions, and information sciences in that unit. He obtained a Bachelor of Science degree in physics and mathematics and a Master of Science degree in management sciences and information technology.

When Dror finished his army service, he partnered with Barak Bercowitz, an army veteran and computer science wizard. Together, they realized that the life insurance industry could benefit immensely from data-driven solutions that would accelerate their transition into the new digital era. The life insurance industry was not speaking the language of data the same way other industries were at the time. They both saw it as their life mission to transform the industry to become data-driven focused and help insurers understand the policyholder’s needs.

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Next-Gen Personalization Methodology for Life Insurance

Dror Katzav, Co-Founder & CEO of Atidot

min read

Next-Gen Personalization Methodology for Life Insurance

Consumers have come to expect personalized, multichannel user experiences from companies that they interact with. Most industries are now able to 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 that is commonly used throughout B2C campaigns.

Most life insurers currently use traditional segmentation tools such as Tapestry (Esri), Mosaic (Experian), and even Facebook, as the basis for their marketing strategy. This allows insurers to personalize their marketing activities and products in the life insurance vertical. In short, Tapestry, and the like, classify people into over 60 groups and types based mainly on zip code data. This cohort is 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 in the form of recurring behavioral patterns to create hundreds of thousands of additional granular segmentation, providing a full and complete insight into your book of business?

Atidot leverages these powerful segments with its AI and machine learning capabilities and augments a behavioral pattern to the data. We use external information from public databases as well as internal sources such as CRM systems. This information will enrich your database and

provide additional insight into your book of business or create a platform for new marketing strategies that are more accurate, potentially enabling marketing campaigns based on real-time customer data. Occupation, the proximity of hospitals, day of premium payment, investment patterns etc., 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 product offering for 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. This is the basis of our nano-segmentation methodology.

For example, one behavioral pattern is the client’s payment date. Different dates may have different meanings. For instance, if someone is repeatedly late in their premium payments, the machine can find 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. Here, it might suggest that they are busy successful people who don’t have enough time to attend to their finances. Yet they may match the most profitable policyholder groups that insurers have.

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. AI and ML technology tie such behavior patterns into different groups, such as the trendsetters segment (a traditional segmented group) that tends to invest when the bond index is up (based on the correlation between two sets of behaviors) indicating that they have a financial orientation and can be treated in two different ways. 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. When the bond index is down, insurers might consider selling more protections to that segment.

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 example, bloggers associated with style can receive 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 market targeting that can factor any real-time relevant data. For instance, changes in the COVID 19 geographic spread 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.

Key Benefits:

  • Up to millions of new segmentations
  • A dynamic software platform vs. static commonly used tools
  • Life insurance-specific software that caters to the life insurance segmentation
  • An accurate platform for new marketing strategies
  • Behavioral-based insights that provide a competitive advantage

With newfound segmentation, you can gain a deep understanding of your customer base and full optimization of your book of business, as well as new revenue streams generated from a specific target audience. This next-generation approach will set the basis for marching your company into the challenges of the next decade that are all about customer experience and profitability.

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