Next Gen Personalization Methodology for Life Insurance

Consumers have come to expect personalized, multichannel user experience 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 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 segmentations, providing a full and complete insight into your Book of Business?

Atidot leverages on these powerful segments with its AI and Machine Learning capabilities and augment 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 creating a platform for new marketing strategies that are more accurate, potentially enabling marketing campaigns based on real time customer data. Occupation, 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 potentially 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 instance, via bloggers associated with style, targeted ads in fashion publications, direct email, etc. The possibilities are endless.

So, since AI and ML capabilities can be trained to translate real time events into real time data, this newfound segmentation becomes the platform for tailored marketing targeting that can factor 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:
– Tens of thousands and up to millions of new segmentations.
– A dynamic software platform vs. static commonly used tools.
– Life insurance-specific software, catering to the life insurance segmentation
– An accurate platform for new marketing strategies
– Behavioral-based on top of customary data driven
– providing a competitive advantage
– Maximizing the value for your existing customers. Resulting in deep understanding of the customer base and full optimization of the Book of Business, as well as new revenue streams generated from a specific target audience. next-gen-personalization-methodology-for-life-insurance

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

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: 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

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. ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":795,"style":"{\"FG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"clast","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"2jf0d","text":"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. ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":260,"style":"{\"FG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"41j2h","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"fa8vj","text":"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","type":"unordered-list-item","depth":0,"inlineStyleRanges":[{"offset":0,"length":15,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"akc00","text":"","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"709sm","text":"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. ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"fh0p6","text":"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.”","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"d132q","text":"","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"cfbv1","text":" HUMAN API","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":1,"length":9,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":1,"length":9,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"6cjio","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"c0bhm","text":"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. ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":572,"style":"{\"FG\":\"#222222\"}"}],"entityRanges":[],"data":{}},{"key":"er4g4","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"4q3fe","text":"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.","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":994,"style":"{\"FG\":\"#222222\"}"}],"entityRanges":[],"data":{}},{"key":"ejmtm","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"eng9q","text":"ATIDOT","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":6,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":6,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"5ctko","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"dsvfk","text":"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.","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":277,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":277,"length":125,"style":"{\"FG\":\"#1d1c1d\"}"},{"offset":417,"length":317,"style":"{\"FG\":\"#1d1c1d\"}"},{"offset":277,"length":125,"style":"{\"BG\":\"#NaNNaNNaN\"}"},{"offset":417,"length":317,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"98m2s","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"5prs8","text":"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. ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":731,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":783,"length":46,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":856,"length":449,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":673,"style":"{\"BG\":\"#NaNNaNNaN\"}"},{"offset":731,"length":52,"style":"{\"FG\":\"#222222\"}"},{"offset":829,"length":27,"style":"{\"FG\":\"#222222\"}"}],"entityRanges":[],"data":{}},{"key":"9pq16","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"4td7h","text":"BENEKIVA","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":8,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":8,"style":"BOLD"}],"entityRanges":[],"data":{}},{"key":"2819b","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"6o5m8","text":"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.","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":430,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":430,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"3dlcg","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"56aag","text":"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.\" ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":945,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":528,"length":417,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"bm1bb","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"5nbjr","text":"Insurers working with Benekiva have reported the following benefits: ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":69,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":69,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"8nsn4","text":"- 40% operational efficiency in claims processing, workflows, and payout","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":72,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":72,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"blghk","text":"- Accurate rider and benefit calculations that saved a carrier $2 to $4 million a year","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":86,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":86,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"1s314","text":"- Optimized interest calculations which saved a carrier over 40 hours a week","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":76,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":76,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"2j2cj","text":"- Reduction of cycle time of 75%","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":32,"style":"{\"FG\":\"#NaNNaNNaN\"}"},{"offset":0,"length":32,"style":"{\"BG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}},{"key":"4aoi0","text":" ","type":"unstyled","depth":0,"inlineStyleRanges":[],"entityRanges":[],"data":{}},{"key":"1st7m","text":"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. ","type":"unstyled","depth":0,"inlineStyleRanges":[{"offset":0,"length":960,"style":"{\"FG\":\"#NaNNaNNaN\"}"}],"entityRanges":[],"data":{}}],"entityMap":{},"VERSION":"8.63.6"}”>

 
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
SUREIFY®
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 manLifetimeAcquiredates 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.”
 
HUMAN API
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.
ATIDOT
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.
BENEKIVA
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
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.

Next Generation 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. A 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 on board?

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 partner. 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 the automation but they should embrace the opportunity to turn to‘Big Data’, enable technologies such as AI and Machine Learning andbring 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 the AI algorithms is totally dependent upon on 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 towards 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.

Better with Age /The Actuary Magazine

As featured at the Actuary Magazine : https://theactuarymagazine.org/better-with-age/

FEATURED ARTICLES

Better With Age

Predicting mortality for post-level term insurance

MARTIN SNOW AND ADAM HABER SPRING 2020

Photo: Getty Images/Dimitri Otis

Actuaries have a long and storied history of providing the joint mathematical and business foundation for the insurance industry. Yet, advanced predictive analytics techniques with machine learning (ML) and artificial intelligence (AI) have not made it into the standard toolkit of the typical actuary. Insurers and actuaries could reap major strategic benefits if they were to significantly increase their use of these advanced predictive techniques. In this article, we focus on mortality and lapse studies as one example.

Post-level term (PLT) insurance presents a unique set of challenges when it comes to predicting mortality and lapse experience. After a set period of, say, 10 or 20 years when the policyowner paid level premiums, the premium will rise annually. Customers will be highly motivated to identify all of their other options. Healthier individuals will have good alternatives and lapse their policies; the less healthy ones will remain. The greater the premium increase, the greater this effect will be—resulting in the classic mortality spiral.

How can we get a good quantification of the interrelationship between premium increases and lapse and mortality experience? By building a predictive analytics model—more advanced than those previously developed1,2—to set lapse and mortality assumptions, and price and value PLT insurance. Our model will statistically integrate heterogeneous customer cohorts,3 improve credibility in cohorts with sparse claims data, and provide a more complete understanding of the impact of premium changes on mortality rates. We can only imagine the additional improvements to insurer pricing and financial reporting that could be achieved with broader applicability of these techniques beyond PLT.

OUR PLT MODEL

Our PLT model comprises three advanced predictive methods:

1. An innovative application of a statistical multivariate framework to model PLT lapse and mortality. This multivariate model reflects the causal structure (and almost immediate impact) of PLT lapsation and premium changes on mortality (PLT causal structure4) and provides better guidance for setting PLT premiums. Taking the causal structure into consideration is especially important when answering predictive “what if” questions (e.g., what happens to mortality if we change premiums by X percent).Consistent with our plan to model the lapse rate as a major driver of the dependence of mortality rates on premium level, we make assumptions in our model about the underlying data-generating processes:
· Whether a policyholder lapses at the end of the level term period is a stochastic function of various characteristics such as age, gender, risk class, face amount and the change in premium.
· This function may include complex dependencies among variables. For example, the effect of different face amounts on lapsation may vary by age, gender and so on.
· The differences in both base and shock lapse among cohorts cause perceptible differences in mortality levels.

2. The statistical technique of “partial pooling” to increase the credibility of sparsely populated cohorts. This is especially important when the volume of available data (especially mortality data) differs substantially by cohort, leading to differences in credibility—including cohorts with very limited credibility.

Partial pooling is a principled middle ground between complete pooling, which fits a single model for the entire population and ignores variations, and no pooling, which fits a single model for each cohort and ignores similarities shared among cohorts. Partial pooling is also known as hierarchical partial pooling.

Partial pooling enables us to share information (borrowing strength) among cohorts, regularize6 our model and account for different cohort sizes without incorporating ad hoc solutions. The data for each observed cohort informs and adds credibility to the probability estimates for all of the other cohorts. The extreme estimates are driven toward the population mean (“shrinkage” in Bayesian statistics) with significant lessening of variability that may have been created by noise in the data. This phenomenon is closely related to the concept of bias-variance trade-off,7 in which the tightness of fit to the observed data is reduced, so the derived estimates serve as better predictors. Partial pooling leaves us with better estimates, reduced variability and improved credibility.

Partial pooling smooths mortality estimates, which by itself is not new in actuarial science—different graduation techniques have been developed and implemented over the years. The distinct advantage of partial pooling is that it achieves the same goal by explicitly sharing information among cohorts in a principled way (guided by domain knowledge and analysis of the data), and it can improve credibility in sparsely populated cohorts.

3. The integrative statistical approach of Bayesian inference 8,9 to quantify differences in experience among cohorts with different exposure levels. The generative nature10 of Bayesian modeling enables the incorporation of expert knowledge into the models in the form of model structure and informed priors.11,12 Bayesian models produce crucial uncertainty estimates (unlike the point estimates supplied by more traditional maximum likelihood approaches) needed for informed decision-making—especially with sparse mortality data. We use Bayesian multivariate modeling of lapse and mortality, but we do not include a numerical comparison of the Bayesian and non-Bayesian approaches in this article due to space considerations.

There are two key elements of our mortality-lapse model. The first is a nonlinear regression lapse model inspired by previous Society of Actuaries (SOA) studies.13,14 We added partial pooling of parameters across cohorts to increase accuracy, credibility and predictability. We changed the link function of the model from log to logit to ensure per-cohort lapsation is bounded by the exposure (previously it was possible for the model to predict more lapses than exposures, i.e., an actual-to-expected ratio > 1).

The second key element of our model is that it is a Bayesian version of the Dukes MacDonald (DM) mortality model.15,16 In this version, we model the effectiveness parameter as a nonlinear function of the cohort characteristics (e.g., age, risk class, gender, etc.), use priors that reflect actuarial knowledge regarding plausible parameter values of G (e.g., a reasonable prior might put more weight on values of G closer to 1 than 0),17 and infer the posterior distribution of G from the data (the distributions over model parameters after conditioning on the data). We use the nonlinear regression lapse model previously described to estimate a distribution of lapse rates by cohort. Mortality is estimated by integrating over two variables: the joint distribution of base/shock lapse rates and the effectiveness parameter, thereby completing the mortality-lapse model.

OUR MODEL IN ACTION

To implement the model, parameters for both the lapse and mortality models were estimated using Stan, a state-of-the-art platform for statistical modeling and high-performance statistical computation.18 We validated the estimates Stan provided with both Bayesian model comparison methods, such as leave-one-out (LOO) and Watanabe–Akaike information criterion (WAIC),19 and actual-to-expected (A/E) ratios.

The SOA data20 we used for our modeling, consisting of 8,066 different customer cohorts, is summarized in Figure 1.

Figure 1: Experience Used in the Model

Source: Society of Actuaries. Lapse and Mortality Experience of Post-Level Premium Period Term Plans. Society of Actuaries, 2009 (accessed January 27, 2020).
To quantify and validate the impact of the new Bayesian tools presented, we conducted an analysis. First, for the multivariate modeling of lapse and mortality, we examined three variants of DM mortality estimates:

1. Assume fixed base lapse rates before the PLT period, fixed total lapse rates at the end of the level term period, and fixed effectiveness parameters. Optimal values for base and total lapse rates and the effectiveness parameter were found by using a standard gradient descent optimization algorithm. The lapse and effectiveness parameters do not vary by cohort though the select and point-in-scale mortality do vary by cohort.

2. Empirically assess from the data both the base and total lapse rates by cohort. The effectiveness parameter was fixed. It was optimized using grid search.21

3. Use a partially pooled model to estimate both base and total lapse rates that vary by cohort.

The distribution of the effectiveness parameter was inferred from the data itself using NUTS,22 an adaptive extension of the Hamiltonian Monte Carlo Markov Chain algorithm.23 In each of these variants, expected mortality is computed based on the five input parameters to DM: effectiveness, base lapsation, shock lapsation, select mortality and point-in-scale mortality. The select and point-in-scale mortality used in the computation of expected mortality were selected from standard tables. We compared the actual deaths for each method in each cohort to the expected, and we then computed a weighted error as the mean absolute deviation of the predicted A/E ratio from an A/E ratio of 1, weighted by exposure. Figure 2 shows the results.24

Figure 2: Mean Absolute Deviation of Actual/Expected Ratios

A model such as this can be continually improved. For example, we know mortality is often a bit higher for lower socioeconomic classes. Building in this knowledge may result in an A/E ratio closer to 1. Similarly, upper-income policyholders may have the ability to anti-select, which also could be built into the next model iteration. The Bayesian framework used is especially well-suited to the incorporation of this type of expert knowledge.

For partial pooling when measuring mortality rates, we fit a nonlinear regression model to publicly available mortality data25 with and without partial pooling of model parameters and held all else (e.g., the data and the characteristics being analyzed) constant. We compared the partially pooled model to both regularized and nonregularized nonlinear regression models using R’s glmnet package.

We ran the models with different characteristic subsets to validate that our results are not characteristic-dependent. Almost always, the models without partial pooling of parameters yielded implausible estimates for cohorts with especially low exposures or claims, sometimes deviating from the population mean by more than four orders of magnitude. On the other hand, the mortality rates in the partially pooled model were much closer to the population mean on an exposure-controlled basis. Outlier behavior of the magnitude seen when partial pooling was not used was not observed.

When comparing models using Bayesian selection methods,26 the partially pooled model had significantly better LOO cross validation and WAIC scores, as shown in Figure 3.27

Figure 3: Model Validation Comparison

*For this row, we show values for the regularized (nonpartial pooling) model that gives the best results.

When predicting mortality rates for cohorts with relatively small exposures (~5 percent of the mean per-cohort exposure, 153 cohorts out of 8,000), the nonpooled models yielded mortality estimates that are less than 0.01 percent of the mean mortality rate (interestingly enough, over-estimation was not observed). This under-estimation resulted from improper handling of small sample sizes. These results held even with the regularized models, which are very similar to models with graduation.28
On the other hand, models with partial pooling did not produce such extreme estimates because of the beneficial impacts of shrinkage. Proper handling of mortality estimates in cohorts with small exposures is critical, as such cohorts will almost certainly exist when modeling data at high granularity.

CONCLUSION

This article explored innovative approaches to modeling PLT lapse and mortality. A multivariate PLT lapse and mortality model improves mortality estimates and sheds new light on the interactions among changes in premium, persistency and mortality. Because management would have the information it needs in real time, this transforms pricing, reserving and “what if” analysis.

Partial pooling shares information among cohorts, accounts for different cohort sizes, regularizes estimates and improves credibility. When there are multidimensional cohorts with sparse data, partial pooling can provide unique insights into policyholder behavior, which is very valuable for insurers looking to manage risks and finances and optimize top-line growth.

The Bayesian model allows us to capture our prior knowledge of the data-generating process, such as the reasonable values of the effectiveness parameter. Such a model will be practical and implementable—and not just a nice theoretical toy.

The methods discussed in this article are valuable for answering a widedent, chief delivery officer and chief actuary at Atidot.

Adam Haber is a data scientist at Atidot in Tel Aviv. range of sophisticated actuarial questions. Actuaries and insurers will want to consider how advanced methodologies such as the innovative lapse-mortality model, causal inference and Bayesian decision theory could be used to address crucial challenges. Now that the availability of computational resources facilitates the implementation of these advanced methodologies, insurers face a new imperative. These techniques can be extended to general lapse and mortality studies along with other aspects of the insurer experience. We look forward to seeing the improvements in pricing and reserving (such as for principles-based reserving) and the increases in credibility that will emerge from greater use of these techniques.

Martin Snow, FSA, MAAA, is vice president, chief delivery officer and chief actuary at Atidot.

Adam Haber is a data scientist at Atidot in Tel Aviv.

Need for a Dedicated Coding Language

Why Actuaries Need a Unified, Dedicated Programming Language
By Barak Bercovitz, Atidot Co-Founder & CTO

Insurance is rooted in data innovation. Wide swaths of modern statistics and probability were first devised to accurately price, predict and manage risk. But insurance’s pioneering position has faltered in recent years.

While today’s economy is ablaze with revolutionary advancements in big data and computation, the insurance industry has been uneven in its adoption and application of cutting-edge data technologies. One study found that just 20 percent of the data collected by insurance companies is usable for strategic analysis. Current attempts to incorporate big data and machine learning into insurance products tend to occur on an in-house and ad-hoc basis.

High financial stakes and strict regulations already complicate big data adoption, but beyond that, the lack of a formalized system or computer language for interfacing with the available tools, technologies and data can prove one of the biggest obstacles to progress. This is why the life insurance industry as a whole, and actuaries, in particular, are in dire need of their own unified, dedicated programming language. As the CTO of a startup working with big life insurance companies, my team recognized this pressing need and committed ourselves to authoring an insurance and actuary focused programming language to help fill the gap.

To understand the distinct challenges of applying technical innovations to the insurance industry, it is essential to first peel back the complex layers behind computer applications in general. Computers have come a long way since their earliest days as room-sized mainframes with punch-card readouts. But at their core, all modern computers still reflect this legacy, hard-wired roots. Graphical interfaces and polished applications might make today’s computers more user-friendly, but every action and instruction must still be translated and abstracted into binary machine code in order to be computed on.
Now, this is not to say that developers sit typing their code as zeroes and ones. Rather, modern programming languages use their own, distinct shorthand, which is then compiled into code readable by hardware. However, the particular output logic required varies by computer architecture. GPUs operate differently than CPUs, which operate differently than cloud computing frameworks. Therefore, the trend has been to author general purpose languages (GPL) that focus on accommodating the widest range of uses to a particular machine or architecture. Instead of optimizing for a specific problem or use-case, GPLs ask the programmer to learn a new language and apply it to their given domain.
While this complicates developing specialty applications of any kind, the unique contours of the life insurance industry add an additional layer of difficulty. Regulations governing insurance are among the strictest and most byzantine of any industry. And beyond the issues of compliance come the extraordinary financial and social stakes riding on the integrity of insurance products. Core pillars of the private and public sector are propped up by the accurate, reliable management of risk. Insurance models running on shaky code could turn a tiny software bug into tens of millions in losses, the eventuality of which is only amplified by the enormous complexity of accurately calculating risk five, 10 or even 25 years into the future.

Seeing these issues firsthand inspired development of the Atidot LIA (Language for Insurance and Actuaries). What my team and I realized when approaching this challenge was that what initially looked like one problem was actually three distinct but interrelated issues.

The first issue was the substantial technical demands of carrying out the tasks actuaries would demand of big data. Cleaning and anonymizing raw data, modeling it properly, testing and executing on a laptop or workstation and ensuring all code passed formal verification – these intricate operations would be a baseline requirement of any function.

After addressing the fundamental complexity of insurance operations, the next issue was simplifying the syntax and optimizing legibility for domain experts who might not be professional developers. By building in insurance-specific entities, data models and analytics models for several use-cases, LIA allows actuaries to speak the language of insurance instead of memorizing the arbitrary variables of Python, Visual Basic, or C++.

Lastly, the unification of all necessary functionality into a syntactically legible framework would enable frictionless integration with machine learning models and accelerate time-to-market for new actuarial products. In other words, it would allow actuaries to write, debug and deploy big data in terms they could easily understand. Harmonizing function and syntax would help resolve some of the major roadblocks facing data integration.

The current tension between the enormous promise of big data for the life insurance industry and the difficulty of developing dedicated software contribute to a compromise worse than the sum of its downsides. Today, actuaries looking to incorporate big data or machine learning are forced to cobble together homegrown solutions using a patchwork of languages and tools. Otherwise, they must rely on dedicated developers who lack the domain expertise to fluently translate actuarial needs into proper code. This disconnect creates friction and stilts progress.

However, by empowering actuaries to translate their domain expertise into instructions usable by cutting-edge technologies, a dedicated programming language will help align the existing talent in the industry with the untapped potential of data innovation. Modeling insurance is increasingly becoming a multi-disciplinary challenge, and a more precise, specialized programming language will help foster collaboration and jump-start innovation. In other words, our vision is to help big data and life insurance finally speak the same language,

An Interview with Barak Bercovitz: Co-Founder, CTO & Professional Problem-Solver

Barak Bercovitz, Co-Founder and CTO of Atidot, has applied nearly a decade’s worth of IDF intelligence training to the niche space of long-term insurance and the results have proved fascinating. Meeting his fellow Co-Founder, Dror Katzav, during his army service, Barak explains, “What we learned to do in a very systematic way in our unit is solve ‘seemingly impossible problems’.”
Together, Barak and Dror continued seeking out other unique industries facing “insolvable challenges,” believing that where there is challenge, there is also phenomenal opportunity. Rather than moving into more predictable spaces such as Cyber or Telco, the two became intent on disrupting an industry to create long-lasting change and efficiency.

“We really wanted to make a change. In a way, we wanted to build teams like we used to build in the army to tackle big challenges. Eventually we decided that the insurance industry was one that really needed many changes in how it’s working and operating.”

Detailing a picture of how insurance looked 10 years ago, Barak says “There are all the advances in tech, cloud computing, elastic computing powers and all of that, but insurance professionals are not able enjoy those benefits because they are locked Makes Insurance Smarter
down to legacy systems and older data. The processes are a mess! We realized we wanted to bring our knowledge of how to build very reliable products and solutions that also can be easily operated by insurance people and actuaries.”

Doing so would mean spurring a groundbreaking shift away from traditional insurance practices and moving towards data-driven methods. This would not be an easy task, as insurance companies have become accustomed to tedious manual work as well as spending “hundreds of thousands if not millions of dollars” on consultancy companies to come up with reports.

“Insurance professionals never received a user interface, a dashboard, a tool that is easy enough for them to use—without becoming software developers themselves—to help solve challenges, which include optimizing the book of business, new business, in-force business and basically becoming a data driven business.”

Coming together with their third partner, Assaf Mizan, who at the time was Chief Actuary for Israel’s Ministry of Finance, gave a big push for Atidot to move forward. “Basically, he completed the picture and focused us further on the long-term insurance business, which has many nuances and challenges that are specific to that subfield. Predicting and knowing what’s going to happen in 10 or 15 or 20 years’ time is challenging enough, but the ways it’s been done so far using classical statistics or classical actuarial sciences is really not good enough and can’t be compared to the new solutions of predictive analytics and machine learning that all companies use today.”

Looking with Hindsight

challenges facing the long-term insurance industry. To build their platform, they began researching how insurers used existing technologies and data, as well as the accompanying roadblocks insurers kept meeting along the way.
Barak recalls, “A lot of trial and error from a research perspective but also from an engagement perspective: What are we offering our clients? Are they going to like it or not? Are they willing to pay for it or not? How to work with them? There are big cultural differences with companies from all around the globe and technical issues even with things like privacy regulations, which in a way makes engagement more challenging.”

We realized we wanted to bring our knowledge of how to build very reliable products and solutions that also can be easily operated by insurance people and actuaries.
Barak made sure to add, “But again, when anything is challenging, there is a big opportunity to win by using a combination of an understanding of the insurance business and software development, and by bringing in our machine learning expertise. If we find a way to solve those problems with automation or computer science, then it’s a great win for us.”
Recalling in hindsight some of the Company’s earliest achievements, Barak noted the significance of aligning with some of the biggest insurance companies in the US, which is not trivial for a small Israeli startup. Other important junctures during Atidot’s early stages included the recruitment of a strong development team, marketing and sales personnel, and actuaries who understood the ins and outs of the insurance business. Although today the Insurtech field is exploding, pitching to VC’s in the Company’s earlier stages meant proving the potential of an entirely new category.

“When we began, we just wanted small Israeli companies to talk to us and give us access to their data,” Barak says. Then, “we started developing more of our intellectual property that includes tools that analyze data sources automatically, and solutions that model insurance in better ways than it’s been done before. We started marketing to and approaching American companies, and we managed to get some big lines to talk to us, which was amazing! We really leveraged our connections to do more of that while also developing our understanding of the specific challenges per geography, per company, per scale, for big and small companies alike.”

From here on, the technology itself seemed to take the lead, cutting insurer’s costs and offering deliverables in record times, something that the insurance world had never seen before. The holistic approach of Atidot’s technology helps insurance experts “scope their problems and distill and focus the questions and solutions”. The results are then in the insurer’s hands, tangible and usable in real-time, for actuaries, marketing teams and management alike.

Problem-Solving in the Future

“As a startup we are in a constant street fighting mode to understand what is the best direction and the best way to help as many companies as we can.”

As the world is changing, so are the challenges insurance companies are facing. Currently, long-term contract insurers suffer from high percentages of lapsation and churn, and
The holistic approach of Atidot’s technology helps insurance experts “scope their problems and distill and focus the questions and solutions”.

specifically in the US, there is the ominous question of under-insurance. “Something like 40% of the US is under-insured, meaning their insurance won’t cover their real needs, which is potentially a big problem for a country like the US if something like 2008 were to happens again.” For statistics and information on under-insurance in the US, read Atidot’s most recent Annual Insurance Report.
“We have to adapt ourselves all the time,” Barak explains, “but we’ve built the organization in such a way that we can support those changes and be agile. We try to be agile and answer our clients’ needs as much as we can. From a technological perspective, we developed our solution with an understanding that things are going to change, and if we want to stay competitive and support those changes, we have to model insurance from the ground up.”

As CTO and Chief Visionary Officer, Barak has to stay focused on the company’s long-term vision of becoming the “Bloomberg for insurance policies,” or in other words, becoming the single point of knowledge for universal values of insurance policies. Barak believes there is a growing market for insurance policies, reinsurance and the hedging of insurance data. He explains, “Insurance policies are a financial instrument like a bond or a stock, so our vision is to become a company that has the most powerful and accurate understanding of quality life insurance companies and their strategies.”
“From a tech perspective, I envision regulators and companies using our software libraries and our programming language for insurance because it is such a good language, and lets them express their ideas and scenarios in the insurance domain very easily”. Reaching this goal means identifying which technologies Atidot should be using, learning and developing today to lead the way for success in years to come. To reach these goals, Barak’s top priority is keeping a strong team around him, one that grows better together and continues to find the best insurance solutions out there.
When asked which key skills a company leader needs to succeed, Barak again embraced what he found to be a challenging question. Out of countless qualities he felt helped him along the way, he named patience, ambition and having the right people around as the building blocks of his journey.
“There are so many hurdles when you start a company. You can never win it all. You keep falling and getting up again and again. One important thing is being patient and understanding that no one, really no one, has succeeded without failing. That’s why we chose a tech solution that from the beginning assumes that there will be mistakes and that we can fix them as we go.”
He continues, “What I mean when I say patience and ambition, is that it’s really all about people and their personalities. It’s not about someone’s personality specifically; it is about the collective personality.

When you start a company like ours, you have to believe in co-workers and trust them and give them space to perform. This is something very common in Israeli culture for everyone to come up with ideas, and managers here usually don’t dismiss them, which I’m not sure happens elsewhere in the corporate world.”

Average US Life Insurance Policyholder 74% Under-Insured According to New Study by Atidot

A report published today by Atidot, an insurance technology company providing AI, big data, and predictive analytics tools to the life insurance industry, exposes the widespread problem of under-insurance in the US life insurance industry. According to the report, only 26% of the total coverage needed for life insurance is currently met, leaving 74% of unmet potential coverage. The report also found that insurance companies are missing out on an average of $785 USD in annual life insurance premium payments per person who requires insurance coverage in the U.S. resulting in a total missed potential of almost $70 billion USD in annual premiums.

“Policyholders are generally unaware that they are underinsured, and the onus must be on the insurance industry to remedy that,” said Dror Katzav, CEO of Atidot. “Life-insurance companies need to be able to utilize the troves of data at their disposal to better engage with their customers. New solutions enable insurers to harness this data efficiently and know when to contact clients to update their coverage and prevent lapsation.”

The report analyzes the levels of insurance on a state-by-state basis, uncovering the rate of under-insurance for individual states and the US as a whole. The State with the greatest percentage of under-insurance is West Virginia with an average of 85%, while Oklahoma, the least under-insured State, still recorded a staggering 51%. The findings clearly show how widespread the problem is, demonstrating an alarming disconnect between providers and policyholders.

The report reveals that companies are forfeiting enormous profit potential and placing their most valuable asset, a loyal customer base, at risk by failing to capitalize on the data they possess. The failure to strategically interact with their clients comes at a substantial cost for insurers and customers alike.

The full report can be found here: https://www.atidot.com/under-insurance-report-2018