Enabling Change in Life Insurance: what does it “tech” to get the job done?

As published in InsureTechNews : https://insurtechnews.com/insights/enabling-change-in-life-insurance-what-does-it-tech-to-get-the-job-done

Written by Dustin Yoder, CEO Sureify Dror Katzav, CEO, Atidot Brent Williams, CEO Benekiva Andrei Pop, CEO, Human API | The COVID-19 pandemic has turned up the heat on insurers. Now, more than ever before, they need to provide their business units with digital tools to conduct business, keep agents selling and enable connections to customers in a socially-distanced world. The new business and social climate mandates that customers be able to shop for, buy and manage a policy without a face-to-face interaction for the future. Those carriers that have put the technology and processes in place to meet this increased business and customer expectation will have continued success even while the model of what “work” and “financial security” look like is changing.  Those who haven’t yet modernized are under added pressure to up their digital game. Looking at a cross section of insurtech software and data leaders can provide carriers with a great deal of wisdom on how digital enablement is creating a more robust, more accurate, more cost-effective life insurance model for those willing to mix old and new. The leaders cited here demonstrate how implementing across-the-board technology enhancements can ultimately produce the recipe for success, not just today, but for the long term.LifetimeAcquire

A recent Celent study shows that, in 2019, many of the most basic customer interactions related to the sale and service of life insurance could not be met digitally. More than 50% of insurers were unable to satisfy even basic customer needs (like changing a name, email address or beneficiary) without a face-to-face or fax transaction. It took a global pandemic to wake the industry up given that many needed processes were stopped in its tracks. Today, many carriers who believed time was on their side are scrambling to adapt to this new environment, which arrived overnight and shows no signs of abatement. Frenetic activity is now being undertaken to plug a gap that was apparent even before the virus hit. A permanent digital end-to-end solution is no longer a nicety – it is a necessity.

Sureify began offering the “innovative” end-to-end digital experience that is now an absolute requirement for the life insurance industry long before the virus changed the transactional environment. The company’s platform is built to be flexible and modular.

LifetimeAcquire enables omnichannel sales that drive placement rates via quoting, e-application, automated unAs published in InsureTechNews : https://insurtechnews.com/insights/enabling-change-in-life-insurance-what-does-it-tech-to-get-the-job-done

Written by Dustin Yoder, CEO Sureify Dror Katzav, CEO, Atidot Brent Williams, CEO Benekiva Andrei Pop, CEO, Human API | The COVID-19 pandemic has turned up the heat on insurers. Now, more than ever before, they need to provide their business units with digital tools to conduct business, keep agents selling and enable connections to customers in a socially-distanced world. The new business and social climate mandates that customers be able to shop for, buy and manage a policy without a face-to-face interaction for the future. Those carriers that have put the technology and processes in place to meet this increased business and customer expectation will have continued success even while the model of what “work” and “financial security” look like is changing.  Those who haven’t yet modernized are under added pressure to up their digital game. Looking at a cross section of insurtech software and data leaders can provide carriers with a great deal of wisdom on how digital enablement is creating a more robust, more accurate, more cost-effective life insurance model for those willing to mix old and new. The leaders cited here demonstrate how implementing across-the-board technology enhancements can ultimately produce the recipe for success, not just today, but for the long term.
As different insurtech partners are brought in to fill specific roles for different clients, Sureify completely customizes offerings to fit individual carriers’ needs, even in a constantly-shifting business landscape. With a full complement of like-minded groundbreakers, Sureify orchestrates better, more cost-effective products and processes. The company’s continued growth is further proof that digital transformation is no longer an option, but a necessity. “The idea that digital transformation is innovative has changed overnight, and now many of these digital capabilities are the new normal for doing business post-COVID” noted Sureify CEO, Dustin Yoder. “Life insurers who make digital transformation part of their core strategy in 2020 will be well-positioned, not just throughout this pandemic, but into the future.”


Traditional methods of gathering needed information for underwriting such as in-person paramedical exams and attending physician statement requests are significantly delayed or paused for the moment as the medical field turns its attention to the coronavirus outbreak. As a result, carriers and reinsurers must find a digital method for collecting medical data to continue underwriting cases. Distribution firms are also on the lookout for new ways to assist clients remotely, and to streamline the insurance buying experience to help more consumers secure peace of mind.

Human API allows consumers to digitally connect and share health data from the comfort of their homes — with no IT integration work to get up and running. This “no-touch” approach to medical data retrieval supports business continuity for carriers, reinsurers, and distribution firms while ushering in a new era of digital transformation in insurance. Carriers and reinsurers are finding that an applicant’s electronic medical records often contain valuable information such as recent lab tests, vitals, and social history that can be used to expedite underwriting. The use of EHR data has grown exponentially, especially in recent weeks, and it shows the potential to replace the APS, in-person exams and lab work. This could pave the road to a future with automated rules engines, accelerated underwriting programs, and granular risk stratification. Stakeholders from all across the life insurance industry are fully embracing EHR data to adapt to our new normal and better serve consumers.


Life insurers and annuity writers have more client data than any other industry yet analyzing policyholder behavior has always posed a challenge. The recent rise in unemployment rates, regulatory changes and the unprecedented market volatility have exacerbated that challenge. Over the course of the past six months, we have been confronted with regulatory changes, the COVID-19 pandemic, Shelter-In-PlLifetimeAcquireace orders, a mandatory 90-day premium grace period, interest-rate drops, and a two-billion-dollar relief package. Traditional capabilities and paradigms lack the flexibility to enable insurers to overcome uncertainty, limiting their ability to understand demand elasticity and analyze new market trends in real-time.

Choosing a digital, data-driven approach will empower life insurers to embrace new opportunities and overcome unforeseen risks. Life insurers need the ability to analyze parameters such as lapse, mortality, profitability (and many more) to generate accurate real time predictions that will support their strategy. The fastest, most efficient, and most accurate method to generate insights and predictions dictates the use of Artificial Intelligence and Machine Learning technologies. AI and ML can process large amounts of data from multiple internal and external data sources and then learn and produce new insights as events occur. Atidot offers such real-time data analysis based on clients’ portfolios. The solution provides a 360-degree view of policyholders and producers using insights and recommendations. It also allows strategic scenario modeling on individual policyholders, or on insurers’ overall portfolio and prediction of trends, looking at both individual consumer policyholder behavior and market changes in real time. This allows carriers to monitor, analyze and strategize to improve profitability immediately. “The pandemic has accelerated the digitization process within life carriers however, they have yet to maximize the potential within their data” says Dror Katzav, Atidot CEO.


In today’s more hectic than usual environment, brought on by COVID-19, companies are challenged to keep business continuity effortless. Carriers need to easily move their claims operations to employees working from home, with no drop in productivity or issues with claims processing. Even the best life insurance modernized by technology combined with data means nothing if it does not produce the fulfillment of a customer claim.

Benekiva’s Bene-Claims module has allowed forward-thinking clients to transform their claims processes from intake through payout. The platform automates documentation, intake process, correspondence, workflows, rules, reporting, interest calculations and more. The company’s flexible architecture allows the platform to connect with multiple carriers’ systems across an organization to offer a single claims platform regardless of product line, product riders/rules, or underlying company that the policy was written under. Carriers using Benekiva’s claims module experienced business as usual, or even better than usual. Per Steve Shaffer, Chairman, President, and CEO of Homesteaders Life Company, “With Benekiva’s ability to work anywhere, anytime, and any device, during COVID-19, it has been business as usual for our claims staff and most importantly, we have been able to uphold our superior servicing standard to our beneficiaries.”

Insurers working with Benekiva have reported the following benefits:
– 40% operational efficiency in claims processing, workflows, and payout
– Accurate rider and benefit calculations that saved a carrier $2 to $4 million a year
– Optimized interest calculations which saved a carrier over 40 hours a week
– Reduction of cycle time of 75%

As the industry goes forward, the increased sense of urgency that has resulted from COVID-19 is unlikely to diminish. That means that digital transformation will be essential to any carrier hoping to be viable in this new domain. As the method of doing business transforms to an “all digital” experience, it should be a given that 100% of insurers will be able to complete virtually all business transactions through mobile and web-based applications. Those insurers, and their start-up partners, who come to the table early will be best situated to sell more, manage risk better and operate in a cost-effective manner well into the future. Those who are in the beginning stages of offering such an experience may still be able to make up ground as they accelerate their processes to fit into today’s exclusively-remote business world. Those who haven’t yet started a digital transformation will need the best allies in insurtech to survive into the future.

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.

Leading Insurance Talent Joins Atidot’s Executive Team

What’s the greatest thing since sliced bread? In the insurance industry, it’s insurtech! The “technology” of insurtech is cutting edge and fairly new, and the “insurance” is rooted in tradition.
That’s why the executive team at Atidot, a leader in big data and predictive analytics tools for the life insurance industry, has expanded to include leading insurance experts. An exciting addition is Martin Snow, a former actuarial executive at Prudential, TIAA, and MetLife, who now serves as Atidot’s Vice President and Chief Delivery Officer, and Member of Atidot’s Advisory Board.

Martin is a member of the Big Data Task Force of the American Academy of Actuaries and led the development of the industry’s first ULSG priced with Principle Based Reserves, as well as the conversion of the pricing models to a new platform.

Martin’s CDO appointment complements the founding team of data scientists and actuaries, including the former Chief Actuary at the Israel Ministry of Finance. The expanding team is working with leading insurance providers to enable them to take control of their existing data to strengthen policyholder retention, sales, and in-force management driving both top-line and bottom-line growth.

The Israel-based startup caters to the unique requirements of its customers and harnesses advanced artificial intelligence, machine learning, and predictive analytics to enable life insurers and annuity writers to make data-driven business decisions. Atidot focuses specifically on the life insurance industry (valued at $597 billion in the US alone), offering insurers an easy-to-use and secure SaaS predictive analytics platform. The company utilizes underused and often neglected sources of data as well as open access information, to enhance existing business models.

Martin says, “I am delighted to join the fast-growing team of actuaries, data scientists, developers, and product professionals at Atidot. Together we are building industry-specific predictive analytics tools, providing life insurers with new strategies to meet the opportunities and challenges they are facing.”
Dror Katzav, CEO and Co-Founder of Atidot — and its lead data scientist — says that Atidot was founded to enable insurers to become more client-centric and is very excited that Martin has joined the team. “By using existing policyholder data, combined with open sourced information, we build a complete picture of a policyholder and predict their behavior, allowing carriers to offer the best possible service and develop the best business strategy.”
How exactly will insurers benefit from the Atidot platform? Martin sees it this way: “Life insurers and annuity writers can develop new strategies for their in-force management and new business activities through the insights generated by predictive analytics.”
Martin is a frequent speaker at Society of Actuaries meetings.  Most recently he spoke at the Society of Actuaries Life and Annuity Symposium in Baltimore, Maryland on May 7 and 8. Martin moderated and presented at Session 36 on the Risk Management Process in Product Development” and led a workshop at Session 81 on How AI is being used in Distribution, Product Development, Pricing, and Underwriting.
Keep an eye out for future Atidot executive spottings – coming soon!

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

28 percent of insured U.S. adults ages 19 to 64, an estimated 41 million people, are under-insured (Commonwealth Fund Biennial Health Insurance Survey, 2016).

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 policyholder life events. Any change in policyholder status from marriage, divorce, birth of additional children to house move 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

Let’s take Jessica, Liz, Megan and Sarah, a group of college friends who all purchased the same life insurance policy around the same time. They will all experience multiple life events in the coming years and decades that warrant greater coverage.

Jessica, 30 year old and newly married, rents an apartment with her husband near Milwaukee, and holds an entry level position at a small company. It is likely that her salary will rise, she will have a child, and buy a home in a more affluent community.

Liz also got married recently, but she decided to stay in the city center. She worked in finance and made some successful investments over the years, but when one of her parents was diagnosed with cancer, she had to quit working and take care of them.

Sarah will inherit money from a relative, and Megan will get divorced before having kids.
It is easy to see how the same life insurance policy does not meet each of their needs today. Since they all bought the same life policy when they got married, you can easily see how it cannot fit all of their needs today.

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 under-insured, but have no easy or timely way of knowing which of their policyholders fall into this under-insured 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

With this pain point at the top of many insurance companies’ agendas, 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 under-insured.
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).
The system also attaches trigger information to each lead to give the producer a hook for a conversation with the policyholder. For example, 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 over 41 million under-insured 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.

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.

Atidot CEO Dror Katzav to Speak on Panels and Lead Workshops at InsureTech Connect Conference in Las

Dror Katzav, CEO and Co-Founder of Atidot, an insurance technology company empowering life insurance providers with big data and predictive analytics tools, will speak at several sessions during the upcoming InsureTech Connect conference in Las Vegas. During the Life and Annuity Showcase hosted by Hannover Re and Sureify, Dror will be discussing how Atidot’s tools for under-insurance discovery and the integration with Sureify can help carriers improve their customer engagement.

Atidot, which has been garnering growing attention from analysts, insurance partners, and technology companies, will be exhibiting at the conference for the third consecutive year. The company’s research and development team along with key executives will be offering live demonstrations of the platform at booth 222.

Atidot will also host a workshop with Microsoft on the 2nd of October, where Dror Katzav will be joined by Nick Leimer, Principal Industry Lead Azure for Insurance of Microsoft to discuss ‘How to Successfully Partner with a Startup, and Tips for Cloud and Data Initiatives’. Together they will cover how to overcome differences in issues such as opposing mindsets and cultural differences, and how to navigate the partnership in a productive and effective manner.
In addition, Atidot will be presenting as part of the Startup Bootcamp InsureTech Lounge hosted by Hartford InsurTech Hub as part of the pre-InsureTech Connect events.

Recently noted in reports from Willis Tower Watson- CB Insights, and Novarica, Atidot is solving the ‘big data question’ for the life insurance industry. With advanced analytics, the platform analyzes data already in the possession of the carrier, as well as open source and publicly available data, to generate insights and asses potential underinsurance cases. Armed with more accurate data, carriers can identify policies with a high lapse risk as well as policies providing insufficient coverage for their holders – and remedy the situation. Identifying these policies is a win-win for consumers, who can update their policies to reflect current needs, and the insurance providers, who by providing a higher level of service can retain customers and upgrade policies.

Atidot and Sureify Partner to Provide the Life Insurance Industry With Data Insights to Proactively

Today, Atidot, an insurtech company empowering life insurers with big data and predictive analytics tools, and Sureify, a next-generation life and annuity platform enabling digital sales, service, and engagement, announced a new partnership to bring to their unique tools to the industry. The partnership will enable life insurers to collect new data in order to produce actionable insights, allowing carriers to more effectively engage with their policyholders. With information that is specific and relevant to each policyholder, carriers can improve how they monitor active policies for under-insurance or lapse potential, and proactively identify opportunities for cross and up-sell.

The partnership, with Atidot’s unique advanced AI capabilities and Sureify’s Lifetime platform, provides carriers with a solution that collects and utilizes previously unnoticed or underutilized data to produce new and actionable insights. Sureify’s Lifetime platform will digest most legacy data from a carrier but it also allows policyholders to share their social media accounts, geolocation, IoT data, and engagement analytics. With Atidot’s data analytics tools, insurance providers can combine data with actionable insights inserted into Sureify to effectively engage the customer and drive sales or leads.

“This partnership came about in a very organic way”, said Dror Katzav, CEO of Atidot. “Our complementary technologies are a natural fit, and together they offer an end to end solution for life insurance providers looking to add data powered tools to their offerings, ensuring that they stand out in an increasingly competitive market.”

The integration will enhance existing client-specific insights that facilitate data-driven business decisions. By providing carriers with tools to better identify consumer needs and improve interactions with their clients, providers can rest assured that policies remain as up-to-date and relevant to the customer as possible.


Dustin Yoder, CEO of Sureify believes the partnership can drastically improve the operational capabilities of major insurers. “Sureify’s Lifetime can provide both a great deal of new data but most importantly the engagement capability to go with it, and our partnership with Atidot will further enable us, and thus the life insurance carrier, to make meaningful conclusions and the subsequent action with their policyholders.”

Insurance company – from manufacturer to service provider

Try this, go to an insurance c-level executive and ask “what is your primary line of business?”.

Think about the terminology insurance companies are using when they talk about risk, policies and customers. Insurance companies think of themselves as manufacturers, risk carriers. The broker is the intermediary, managed by a wholesaler and the customer is the policy holder. This terminology sets a mindset. The mindset is that the insurance company is a factory. The factory produces  units of “policy” per month or quarter. Policies are shipped to the Wholesaler in bulk and sold by the retailer. Insurance? Tomatoes? Cosmetics? Who can tell the difference…

Obviously, some companies move to a customer centric models. Unfortunately, even at these companies, they do not perceive themselves as service providers. Having a service provider mindset is a revolution, not a yet-another evolutional re-organization.

In the world of retail Amazon changed the rules. There is one player who controls the entire value chain. Amazon is the factory https://www.ien.com/video/video/20858721/amazon-is-becoming-a-manufacturer, the wholesaler, retailer, producer and delivery mechanism for the product. This is not just an incremental change, this is a revolution. Why? Because now the factory knows who buys the product, what they buy, why they buy, when it is easier to sell and which customers are satisfied. This gives Amazon 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 not even becoming a direct company. It is the change in mindset. Amazon taught us that a book store, that controls the value chain, can understand and analyze the end-customer. Amazon like a SaaS company, focus on customer success, easy on-boarding, self-service, captured value and perceived value for the customer. Think how easy it is to start using Amazon delivery, Alexa, Prime, think about the level of engagement and satisfaction compared to any other retail network. Now, think what could happen if insurance company was no longer a factory that manufactures policies, but is a service provider to a client who needs risk management. Instead of premium, think subscription to a risk management solution.

Insurance companies that focus on providing services would be interested in deep cohort analysis to know what type of customers like the product, what type of customers love the product, and who is the raving fan of the product. Insurance is like any other service. You pay as long as it provides value (or because you forgot to cancel).

Insurance companies that focus on providing services would be eager to change the product to make sure it meets the customers needs in real time. They focus on alignment of incentives and interests to make sure the organization is compensated based on customer happiness. They reduce friction in the on-boarding process, and turn the product from a push product to a pull product because people need insurance. They just really don’t like to buy the service that is currently offered. It is not-good-enough product market fit.

The market fit is not good enough since the insurance company focuses on the wholesaler and distributor, often at the expense of the policyholder, the end customer. As long as the wholesaler is happy, who cares about the customer? It is obviously a broad simplification, but, ask an insurance company who they want to keep happy and wait for the answer. It is not Jane and John doe, it is their top producer, it is their wholesaler and producer networks.

Amazon obviously optimized the assembly line (like AU optimizes the on boarding for insurers), Costco and Walmart did the same. But, they did something different, they focused on customer’s happiness, retention, satisfaction. Amazon do not have better products, in many cases they even sell the same products, but they have a service focused on the customer and not on the intermediary. This makes a difference.

Analysis of Statutory Annual Statements (Part 1)

At Atidot we put a lot of effort into collecting as much data as we can in order to improve our modelling and understanding of the life insurance industry. Our Data Scientists love this approach – it enables us to use hard numbers to support our analyses. One data source that we've been wanting to examine are US life insurers' Statutory Annual Statements. This blog post summarizes a quick research project we just completed to extract and analyze data from these Statements.

What's in a Statutory Annual Statement?

The Statutory Annual Statement contains a wealth of financial and insurance information about an insurer, including, for example, Premiums collected, Reserves, Cash Flows, Reinsurance, etc. From a data perspective, we like to compare it to a thorough "financial blood-test", measuring the vitals and health of a company, shedding light on how it operates, and in some cases – why they take certain actions. For us, this is invaluable – data like this strengthens the calibration of our algorithms, a key step in our journey to further develop the sophisticated Atidot brain that understands and interprets the life insurance industry.

A Google search for statutory annual statements yields some links with downloadable PDF reports, for example:

Example page from a report

PDF Hell

PDF's are great for transmitting documents and other information electronically. But try to convert the pdf into a format in which you can use the numbers – well that's difficult to say the least. Our first step was extracting all the tables we needed of all companies and all years from document files (.PDF) into tabular (.CSV) files. We needed to keep all the numbers in the right order.
This proved to be extremely challenging. We played with the idea of doing this manually, but abandoned the idea pretty quickly. We realized that we needed to develop a fully automatic solution.
One challenge we faced was that in most reports, numbers were encoded with inline PDF Custom Fontsand the standard tools of the trade (e.g. pdftotext) couldn't handle that directly.
We designed several solutions and realized that we need a solution for all extractions of tables with numbers from PDFs and images. This is when we added Image Processing and OCR (Optical Character Recognition) to the mix.

We combined several powerful libraries and tools:

To build an analytics pipeline that: a) cleans the image from small artifacts and noise b) identifies table cells, rows and columns c) does OCR in cells

Online example (using PDF.js + OpenCV.js)

Advertising Efficacy

With the time we had left we decided to do a quick study of the effects of Advertising on "New Business". Measuring the effectiveness of Advertising is tricky and there are numeruous ways to define it, let alone whether is it good enough or not. Accepting that there are no silver-bullets here, we developed a working definition for this blog post that:

  • Is easy enough to understand in this context
  • Uses data from several tables
  • Incorporates business acumen (e.g. "real value" of First Year premiums)
  • Takes the lagging effects of Advertising into account

Illustrated using relevant tables -

The following chart shows original values from Statements of 5.2 - Advertising expenses - Life for several companies during the years 2015-2017.

While this chart shows our calculated ratio -
(Notably, the Colonial Penn Life Insurance Company stands out for consistently spending on Advertising as much as First Year premiums "value" (as per our normalized definition from above)


We set out to analyze Statements with modern Data Science tools, but as we only had 4 weeks for this work, it became clear very early on that we would update our objective and first improve our PDF data extraction capabilities. We're very happy with the results - we're now able to extract virtually any table from a PDF or image format.
Of course, we haven't forgotten our initial goal - we still care about the data and what it says. In a future post we are going to test some advanced Machine Learning techniques (like Deep-Neural-Network) and share the insights we develop.
If you find this interesting and want to learn more, please contact us at: info@atidot.com

Transform your business with predictive analytics

This article aims to demonstrate the utility of predictive analytics models to the life and annuity spaces and how it can be maximised to better retain and engage customers.

Predictive analytics and artificial intelligence (AI) are one of the most transformative developments in the history of life and annuity products. Early adapters are poised to achieve major strategic advantages.We now see many companies with “Chief Analytics Officers” and there is a recent Academy of Actuaries monograph on big data.

Why is predictive analytics important to the life insurance and annuity spaces? It can help us:
. Improve the dynamics of our business; and
. Reduce variability in our financial statements.
Predictive analytics can enable us to better understand the complex causal relationships that affect the performance of our business, in real time, thereby enabling exponential strategic advantages.. In other words, we have the predictive insights in time to act on them, enabling the business to be proactive rather than reactive.
Before elaborating on this, a word of caution. “All models are wrong, but some are useful” said the famous statistician George E.P. Box.  Models for predictive analysis are no exception.

The value of data

In the 1930’s, before the computer age, Eunice Hunton Carter, an Assistant District Attorney in New York, effectively and efficiently used the data available to her – primarily evidence and reports presented by people visiting her office – to build a massive prostitution racketeering case against Lucky Luciano. This case was successfully prosecuted leading to the conviction, imprisonment, and deportation of Luciano, a major organised crime boss. It was the most successful prosection of organised crime up to that time. Data is powerful!

Fast forward to the 21st Century. While browsing an online store like Amazon, we will be told “You might also like …,” “Recommended for you …,” or “Customers who bought this item also bought …” We are consistently surprised and even amazed that the exact product we like or need are displayed.  Online shopping giants have effectively mined their data troves and used data science to identify what their customers are likely to need.

Personalisation in life insurance industry

I own a life insurance policy from one of the largest life insurers. The closest they have come to recommending me a product is to send a list of all the products they offer and suggest that I spend time discussing my needs with the producer. I also own automobile and homeowners insurance from one of the largest P&C writers.  Every few years they recommend that I buy $100,000 of life insurance. But this is hardly personalised! What will it take for our industry to catch up to the likes of Amazon?

Life insurers are using predictive analytics in certain instances – such as with Accelerated (or Automated) Underwriting (AU), post-level term lapsation and premium setting.  However, in other instances (e.g., sales and retention), it would appear that we may not be making as much use of predictive analytics as we could.

Perhaps experts have studied this and determined that additional predictive analytics is unable to help our industry. However, I believe this is simply not true. Suppose that Company A has poor lapse experience and wants to determine what it can do to improve its persistency. They can call everyone who lapses, find out their issues, and try to convince them to reinstate. But, at best, this would be expensive and after the fact. They could call in-force policyholders instead before lapsation happens, but it would be hit or miss on whether they are calling customers at risk, and hence an expensive proposition with dubious results. Worse yet, some policyholders who otherwise would not have lapsed may get the idea from these calls to lapse their policies. So, how can predictive analytics help Company A improve its retention?.


Knowledge is power

Predictive analytics – without human intervention – has demonstrated that some data, such as the premium payment date – previously thought of by many as important only for administrative purposes – can be significant predictors of lapsation risk. Lower and middle income customers who pay their premiums shortly after they receive their paycheck, when they have sufficient funds in their checking account, are more likely to keep their policies in force. Those customers whose premium due dates fall a longer time after they receive their pay checks – by which time they may have spent their most recent paycheck – are more likely to lapse. Armed with this knowledge and other discoveries generated by predictive analytics, insurers and producers can know which policyholders to call and when as well as why these customers are at high risk. The machine makes these connections by itself without anyone needing to know them beforehand.

Using predictive analytics for retention is particularly powerful as the model can be extended to related use cases. Once a predictive model is set up to improve retention, it can be further developed to provide more accurate financials with lower variability. This can be achieved by strengthening the assumption setting process for lapsation.

Strengthening lapse rate assumptions

One big issue in building lapse rate assumptions is the combination of experience from different economic or interest rate environments. For mortality we routinely combine together experience of five years and assume that the year with a particularly harsh winter and a flu epidemic is offset by the year with a particularly mild winter. On the other hand, for lapses this is much more difficult due to the many different combinations of interest rate environments we can have and the fact that they do not necessarily average out.

“How do we use predictive analytics to effectively combine lapse experience of periods in close proximity to each other that have different interest rate environments?”
One fundamental aspect of predictive analytics is feature engineering. Feature engineering uses domain knowledge of the problem being studied (e.g., the setting of lapse assumptions) to create variables that make the algorithms work. These can be tested for significance and incorporated into a model. For example, the magnitude and changes in interest rates can be tested for significance in a lapse model.

Once a sufficiently detailed model is developed, it would then identify the appropriate policyholder segments by which to analyse the lapse experience as well as predict lapse rates for each segment. Segments could have markedly different behaviour, but should be studied only if they have sufficient credibility.

Based on this, the predictive analytics model would use the selected variables to identify the impact that the interest rate environment (as well as other factors) has on lapses, and the model could effectively identify a base lapse rate vector that is independent of the current interest rate environment. This allows separation of some cyclical factors from customer trends.
“We produce more refined policyholder segments that have been newly identified and are using more data and extended study periods to set credible lapse rate assumptions with lower variability.”
The lapse assumptions are more accurate than those produced previously and financial models and results will have lower variability. It also provides a launchpad for reducing lapses in the future. Whether this support has strong incremental effects or exponential strategic advantages depends on the insurer’s implementation.


To achieve exponential strategic advantages, the insurer would automate the predictive analytics. The automation would enable expeditious analysis of additional potentially predictive factors that arise as well as real time learnings on the impact of behavioural, economic, market and other environmental changes. The insurer can then be proactive in improving policyholder retention and understanding its emerging lapse experience.

Returning to the sales process, let us think about how much valuable information we collect that we do not use. For example, when a policyholder notifies us of a change in address, do we treat it purely as an administrative matter or do we analyse it to see whether the move suggests changed economic or family circumstances and hence an increased need for coverage?
Given that many people simply do not buy what a needs analysis says they should buy, perhaps we can start by letting people know how much coverage others in similar circumstances have. This may not solve the entire gap in life insurance coverage, but it is a message that resonates with customers (as Amazon has demonstrated) and it would be a door opener for us to talk to customers and prospects about their needs.

We in the life and annuity spaces have built our businesses by collecting and effectively analysing huge volumes of data. Let us continue to innovate and use the new tools now available to us to revitalise – and indeed revolutionise – our businesses!