Data Science Meets InsurTech Startup: An Illustrative Profile

Data Science Meets InsurTech Startup: An Illustrative Profile

by: Dror Katzav, CEO and Founder

September 11th, 2023

As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important.

At a recent SOA conference, I had the good fortune to learn more about one particular InsurTech startup, Atidot, who focus on predictive models and insurance data analytics. Atidot brings together a team of data scientists and actuaries to develop models that are run with machine learning techniques.

In an interview with the Atidot Data Science Team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples where the innovation is having an impact. This article explores what collaboration can look like and how the role of a data scientist and actuary may evolve in the InsurTech industry in the future.

Culture and Vision

Questions focused on working in an innovative InsurTech startup.

Mark Spong (MS): What is it like to work at an InsurTech?

Atidot Data Science Team (ADST): Working for an InsurTech startup means we place innovation at the top of our minds. We balance our approach by considering that the insurance industry, specifically life insurance, is heavily regulated and has to follow strict compliance guidelines. — Alexia Bettan Jami, data scientist team lead

InsurTech often has a diverse international team; talents recruited from different parts of the world create a mixture of cultures, languages, and even work days. At Atidot, people attend meetings from Tel Aviv, London, New York, and Palo Alto daily. This generates a hectic agenda, but the flip side is that someone is always awake at the company 24/7. Working with an innovative InsurTech company means its projects are always strategic for the life carriers we work with. This drives encounters and the exchange of ideas and approaches with top executives, from the insurer CEO to the chief actuaries and chief data officers across the industry. It is thrilling to see the impact of AI models on forward-thinkers, and it’s even more exciting to be part of the transformation happening, especially in this economy that is driving new and inventive ways of generating more revenues from existing assets. — Sherry Chan, FSA, EA, MAAA, CSO, and an actuary

MS: What challenges do you face as an InsurTech startup that others may not expect?

ADST: The industry is known for being slow and needing help adapting to new techniques and has a lot of know-how built over the years. Part of the challenge is finding creative ways to incorporate this into our models. For example, you can learn from data that certain policies have a high lapse rate. But, when speaking to people in the industry, you understand that the new agents, in their first or second year, sometimes buy a few policies from the carrier to ensure they meet their quota. If they don’t survive in the industry, they lapse these policies, hence the high lapse rate. Examples like these are where we incorporate the know-how from actuaries to enhance the model. — Shimon Malka, data scientist

MS: You have both data scientists and actuaries. Who does what? How are the roles similar and different?

ADST: Actuaries and data scientists both work with data and try to predict the future. However, actuaries focus on the big numbers to identify the trends, where the game is heading, and what behaviors are trending. In contrast, data scientists focus on more nuanced numbers and examples to spot emerging new trends. It’s big numbers versus small examples. To accurately analyze small numbers, data scientists benefit from large quantities of data and sophisticated algorithms. If you give them superpowers with AI and Machine Learning capabilities, they can create better and much more advanced models that will detect and predict big and subtle patterns. This is why both roles are necessary and complementary. Actuaries, with their studies, identify the primary current that impacts how everything flows; data scientists identify the small currents to see where they are heading. — Shaked Markovitch, data scientist

Data science involves understanding statistics, domain data, and computer science. You need to use sophisticated computer science and development techniques to pick up that fine signal and have domain expertise to do it in a way that resonates with business units. At Atidot, actuaries help to understand domain expertise and identify the significant currents that matter to the business. At the same time, the data team focuses on analyzing the nuances in the data, and the development team ensures the software is scalable, reproducible, and can run in real-time. By incorporating actuarial insights into the process, data scientists can collect and analyze data to build statistical models, which can further improve over time with machine learning capabilities. — Alexia

MS: How do you see the role of data science/actuary evolving in the InsurTech industry in the future?

ADST: Data scientists continuously develop better and more intricate tools to collect and analyze the data. AI and predictive data analytics will reach the point where it’s transforming traditional industries as companies become more data-driven and produce meaningful insights, faster, and in a way that impacts customers. This is the direction towards which all verticals are converging.

The human/machine interaction between actuaries and machine learning algorithms will likely deepen over time, making strategic roles more heavily data-driven. This also implies a need for auditable and transparent AI-based insights, to ensure that actions taken based on such insights align and support the overall company goals and profitability. In other words, the human factor in an AI-driven world will become ever more significant, because decisions can’t be made by the algorithms themselves. That will differ between the best insurance companies. — Shimon

Another area of expertise that will be increasingly important for data scientists in InsurTech is data ethics. As data becomes more prevalent and valuable, companies must ensure they use data ethically and responsibly. This means being transparent about how data is collected, stored, and used. Additionally, data scientists in InsurTech will need to be able to communicate their findings and insights effectively to stakeholders, including executives, actuaries, and product managers. This means presenting complex data clearly and understandably, and using data to tell compelling stories that drive action and inform decision-making. — Sherry

MS: What makes Atidot unique from other InsurTech companies?

ADST: Atidot differentiates itself from other InsurTech companies by tackling problems that are typically difficult to overcome. While the industry focuses mainly on new business, Atidot recognizes the significance of in force and retention, which accounts for 90% of the industry. Aitdot’s platform has the ability to drive in-force behavior substantially. — Shaked

Atidot’s platform can provide behavioral insights to the carrier, allowing them to provide quality service to the average policyholder and offering personalized product recommendations. This allows customers to make informed decisions that benefit both people and the economy, thus pushing companies to provide better services. — Dror Katzav, data scientist, CEO and founder of Atidot

From an actuarial point of view, Atidot’s insights empower us to be better at our jobs by providing value and revealing data-driven insights that allow us to make more informed decisions and significantly impact organizations. — Sherry

Technical innovation

Questions focused on hot topics in the industry.

MS: What are your observations on current industry approaches to dynamic lapse behavior based on interest rates?

ADST: For many executives, the current rise in interest rates is a new experience, as the last time rates went up was over 30 years ago when many were not in the workforce. During this recession, people who buy policies with financial understanding behave differently than those who do not understand the economic situation. People who purchase products as an investment behave differently than those who face financial difficulty and decide every month which bills to pay. It is essential to consider their unique behaviors to deal with different populations effectively.

As a technique, we approach an interest rate increase like a decline. Individuals who understand the implications of interest rates will behave differently from those who do not. This change in interest rates can also impact the type of recommended products, such as annuities, where policies with guarantees below the market rate may experience lapse rates. Carriers must implement creative retention strategies, identify individuals who care about their policies and provide better service to retain assets. This is where technology can help insurance carriers address these challenges. — Alexia

MS: What did you keep in mind when modeling your data in the post-COVID era?

ADST: Generally, when using physical data, AI technology is very good at picking out micro currents and small trends that end up being significant trends. That’s why when COVID started, we knew what was happening because our technology captured most of the behavior in real time. In contrast, many actuaries and companies had to wait two to three years to fully understand what was happening. One thing to consider when building features for the model is that behavior pre- during- and post-COVID is different. While understanding pre-COVID behavior is essential, it must be taken with the caveat that it may not fully apply to the current situation. For example, one study found that agents in rural areas, where it was easier to get to the office, behaved differently from agents in the city center, whose offices were shut down. The policies sold by these two types of agents were also different. This behavior was unique to COVID and has since stopped now that offices are reopening. — Shimon

In conclusion, the evolving insurance industry demands a balance between data scientists and actuaries as we adopt new technology and business contexts shift. For many who think about what the actuarial team of the future looks like, perhaps this profile of one InsurTech startup gives you some concrete examples.

I’d like to thank the team at Atidot for their time and acknowledge how their application of advanced AI models in the insurance industry is notable, and incredibly relevant in an era where ChatGPT and AI models are starting to impact our daily lives.

Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries, the newsletter editors, or the respective authors’ employers.

Mark Spong, FSA, CERA, MAAA, is a senior manager with the Actuarial Practice of Oliver Wyman. He can be contacted at

Dror Katzav can be contacted at
Atidot Data Science Team—

Reimagining Life Insurance: The Potential of Generative AI in Hyper-Personalization

Reimagining Life Insurance: The Potential of Generative AI in Hyper-Personalization

by: Meir Marciano, Director of Marketing

August 21st, 2023

Just as mobile payments surpassed credit cards in certain markets, AI is now positioned to outshine traditional software solutions in certain industries, particularly those in the early stages of adoption. While many entrepreneurs are enthusiastic about implementing AI in already digitized sectors, the most profound impact is expected elsewhere.

While the fields of technology and finance have experienced significant productivity gains through digitization in the past decade, numerous industries have seen little change in their protocols and software for over 20 years, unaffected by the SaaS revolution. Surprisingly, some sectors still rely on outdated pen-and-paper methods. Sectors such as insurance have immense potential for disruption and transformation through the application of Generative AI.

By providing intuitive packaging, AI can venture into unexplored territories, revolutionizing processes that have long been untouched by traditional software solutions. This opens up new possibilities and breaks new ground in ways never before imagined.

Opportunities in the Life Insurance Industry:

The current version of OpenAI demonstrates the ease with which AI can engage with customers. Its robustness and user-friendly nature make it an ideal tool for initial customer interactions with insurance carriers. We envision GPT-4 as the missing link between core legacy technology, AI-based insights, and effective communication with end customers. Some companies, including Salesforce, have already implemented AI to enable hyper-personalized engagement. This involves leveraging data from various touchpoints and generating tailored messages and experiences for each individual customer.

Reimagining Key Parts of the Journey:

Customer Service and AI Bots are significant use cases in the insurance industry. Imagine going beyond simple correspondence to leverage insights from enriched data and AI models. Applying OpenAI to these insights allows for hyper-personalized conversations, not only about specific customers but also their unique product needs. This empowers the end customer and the life insurance agent by providing valuable information before their meeting, making their interaction more productive.

This approach can also be extended to Marketing and Distribution for cross-selling and upselling opportunities. Agents often have limited insights into customers’ interactions with their offerings or websites, requiring numerous calls to generate a few leads.

Leveraging Gen AI capabilities in agents’ outreach can enhance efficiency substantially, specifically in the initial communication stage. Specifically, a bot powered by Gen AI can ask questions like: “What kind of life insurance are you in search of?” and “When would be a good time for you to meet us?” following this, the bot could independently schedule the meeting without requiring an agent intervention.  

Taking it a step further, we can utilize insights previously generated concerning potential prospects, including product recommendations (such as Term Life, Whole Life, IUL policies, etc.), potential interest rates for annuities, and more. This would enable hyper-personalized conversations with potential customers, delving into the advantages, disadvantages, and financial alignment.  

The outcome would be an increase in scheduled meetings, leading to heightened revenues over a shorter timeframe and with reduced resource expenditure.

To summarize:

By embracing AI and automation, the life insurance industry can accelerate growth, optimize customer experiences, and unlock new revenue streams. The possibilities are vast, and with the right implementation, AI can revolutionize the sector in ways never seen before and effortlessly.

How Do You Incorporate Generative AI Into Life Insurance

How Do You Incorporate Generative AI Into Life Insurance

by: Meir Marciano, Director of Marketing

August 7th, 2023

Incorporating generative AI in the life insurance industry can be done through several key applications:

  1. Customer Experience Enhancement: Use generative AI to create virtual agents or chatbots that can interact with customers, answer their queries, and provide personalized assistance throughout the insurance journey. These virtual agents can simulate human-like conversations and offer a seamless and efficient customer service experience.

  2. Personalized Policy Recommendations: Leveraging generative AI, insurance providers can develop sophisticated recommendation engines. By analyzing a customer’s unique circumstances and preferences, the system can generate tailor-made policy options that align with their specific needs and financial capacities.

  3. Product Development and Innovation: Generative AI can assist in developing new insurance products by simulating scenarios and assessing potential risks and benefits. This helps insurers stay competitive by offering innovative policies that cater to emerging customer demands.

  4. Automated Claims Processing:
    By using generative AI, life insurance companies can automate claims processing, reducing the need for manual intervention. This expedites the claims settlement process, leading to faster payouts for beneficiaries and policyholders.

  5. Natural Language Processing (NLP): Integrating NLP capabilities through generative AI allows insurers to extract valuable insights from unstructured data sources like medical reports, customer feedback, and social media, improving the accuracy of risk assessment and decision-making.

  6. Personalized Marketing and Sales: Generative AI can analyze customer data and generate targeted marketing content and offers. This allows insurance companies to engage customers with personalized campaigns, increasing conversion rates and customer retention.

  7. Life Expectancy Estimation: Generative AI can analyze various factors like medical history, lifestyle, and socio-economic data to estimate a person’s life expectancy. This information aids insurers in customizing policies based on individual life expectancies.

  8. Fraud Detection and Prevention: Generative AI algorithms can learn patterns from historical fraud cases and generate predictive models to identify potentially fraudulent claims. This helps insurers detect and prevent fraudulent activities more effectively, saving resources and ensuring fair premiums for genuine policyholders.

  9. Risk Assessment and Underwriting: Generative AI can analyze vast amounts of data, including medical records, lifestyle information, and historical insurance claims, to assess an individual’s risk profile accurately. By generating predictive models, insurers can make more informed decisions about coverage options and pricing.

While incorporating generative AI in life insurance can yield significant benefits, it’s crucial to ensure compliance with data privacy regulations and maintain transparency in how AI algorithms impact policy pricing and customer interactions. Regular monitoring and auditing of AI systems are also essential to mitigate potential biases and ensure ethical practices in the insurance industry.

ChatGPT and AI are Winning Over the Life Insurance Industry.  Here’s Why.

ChatGPT and AI are Winning Over the Life Insurance Industry.  Here’s Why.

by: Dror Katzav, CEO and Founder

July 3rd, 2023

Life insurers and annuity companies have always been conservative when it comes to adopting new technology.  But when ChatGPT burst onto the scene, it was hard not to pay attention.  The benefits of using generative AI to increase productivity were too good to be true.  More efficient processing, more customer-centric approaches, and decreased lapse and surrender rates.  What insurer wouldn’t want that?  

If you think that ChatGPT has the potential to be a game-changer, just add predictive AI to the mix.  Now we’re talking about a whole new ballgame.  Here’s why.  

ChatGPT is Redefining the Customer Experience

When it comes to life insurance, ChatGPT gets brownie points for customer service.  Chatbots’ natural language processing allows for real-time conversations that make customers feel like they are interacting with humans. Since responses aren’t constrained to a script, a wide range of topics can be addressed, driving increased satisfaction and higher engagement.  

ChatGPT Powered by AI Predictive Models Makes the Magic Happen

ChatGPT is a baseline model that uses web scraping to collect data from various sources on the internet.  Data collected may not be accurate or relevant.  Companies who want to use ChatGPT for customized applications need to add their own data and intelligence.  That’s where predictive models come into play.

Predictive models were first used in the 80s to support the underwriting process with demand modeling.  In the 2010s, select companies started using AI for underwriting, fraud detection, customer service, predictive analytics, and more.  Then came ChatGPT in November of 2022.  ChatGPT put AI into the spotlight.  

AI algorithms improve predictive modeling by considering a broader range of factors and incorporating non-linear relationships.  Insurers can generate more sophisticated models that capture complex risk dynamics and provide more accurate projections. 

Combining AI machine learning with chatbot capabilities results in personal, proactive customer communications that benefit both customers and insurers.  Insurers can keep in touch with timely and personalized messages about policy renewals, policy changes, and payment deadlines.  They can also offer relevant new products and services. That’s the kind of engagement that builds loyalty and generates growth.  

It’s Still Early in the Game

ChatGPT made the insurance industry stop and take a good look at AI’s potential.   It’s not just a trend and it’s not going away anytime soon.  Neither are the AI models based on machine learning that ChatGPT promoted.  It’s early in the adoption curve, though. That’s not because interest is lacking.  It’s because carriers are just beginning to understand their data assets and how those assets can work for them.  It’s only a matter of time until AI is at the core of every life insurance enterprise.  Stay tuned and keep up with the latest.  This is just the beginning.

Impacting growth of annuity companies with AI driven solutions

Impacting growth of annuity companies with AI driven solutions

by: Dror Katzav, CEO and Founder

July 3rd, 2023

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

Leveraging technology in a growing annuity market

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

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

Why is this?

“On average, Fixed-rate deferred (FRD) annuity crediting rates continue to outperform CD rates, making these products very attractive to conservative investors,” said Giesing. “LIMRA anticipates FRD products to have strong sales this year even though interest rates are expected to fall in the second half of 2023. While we don’t expect FRD sales to match the record set in 2022, LIMRA is forecasting sales to be above $100 billion in 2023.”

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

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

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

How do you keep customers from leaving your company?

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

AI and predictive solutions can provide insights into existing customer data and automatically increase engagement with at-risk customers . AI technology can use internal and external data (issuer’s data) to help engage, retain, and upsell their existing customers instead of losing them to another annuity company. Additionally, AI insights can help issuers sell directly to customers by using elasticity models and sensitive pricing that will adjust to market supply and demand. 

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

AI Automation Can Build Life Customer Continuity

AI Automation Can Build Life Customer Continuity

by: Dror Katzav, CEO and Founder

June 7th, 2023

John Weber:

How should AI be used to enhance, inforce policy management for the life and annuity sector?

Dror Katzav:

Our company focuses primarily on life and annuity. And one of the things we noticed is people buy policies from agents. Now, because of the turnover in the industry, agents live after two or three years. But, you expect the customer to stick around for 20 or 30 years. And you find yourself in a situation where the consumer owns a product that they don’t understand. The insurance company was never built for services and the agent is gone. So, from a consumer perspective, you’re going to find yourself buying more and more policies from other carriers. From a carrier perspective, the difference between having an agent, or not having an agent is going to be dramatic in the lifetime value of the customer. Using AI and automation, you could create this customer engagement that drives higher lifetime value over time, provides better service, creates the retention, upsell, and cross the opportunities where the customer benefits from having a better product, and insurance company benefits from having a higher lifetime value from their existing customers.

John Weber:

But, why should the insurer even care about this technology post issuance of the policy?

Dror Katzav:

Traditionally, insurance companies spend a lot of money on the first 30 days of the policy, from the point you see a Super Bowl ad to the point you finish underwriting. But then you still need to own the customer for another 20 or 30 years. And in those 20, 30 years, there’s a lot of upside that could be made if you provide the right service. Most of these customers don’t understand the product. You don’t necessarily know what is the right time to convert, what is the right time to take a cash value loan or to surrender, partially surrender, make this decision, that decision. If the insurance company can drive these decisions, they drive better lifetime value on the same risk that they already incur and provide better service that support our customers.

John Weber:

Why do you think this technology is so important to Life and Annuity writers at this time?

Dror Katzav:

I think in the last few years, we saw a lot of volatility and a lot of changes. We had a few years of global pandemic followed by recession, changing interest rate, changing the financial markets. Regulatory exposure on policy or behavior becomes a bigger and bigger issue. From insurance company’s perspective, the only way to do this kind of analysis at this scale with that many customers could only be done with AI and automation of the data processes.

John Weber:

Dror, can you give us a peek behind the curtain? How does all this work?

Dror Katzav:

Yeah, of course. The way we work is we partner with insurance company to get access to the data, and then we use, in addition to that, third party data that we have access to and public data such as demographic and house prices and so on and so forth. We don’t go into the PII level because we don’t want to breach privacy, so we stay as a zip code level or zip plus four level. And we basically run the analysis to tell you what action the customer would make at which point and why. So, for example, who is going to lapse in the next quarter? What is the reason they’re going to lapse? And build you the capability to engage with these customers and put them either in front of the contact center or the agent depends on the right action they want to make.

John Weber:

How has artificial intelligence changed the modeling for life and annuity writers?

Dror Katzav: So, the rule of big numbers is the role of big numbers. 4% has always been 4%. Lapse rate has always been lapse rate. But if you want to drive to the next level and say who is it and when and why that are going to make these decisions, you want to start asking deeper questions on the nuances. For example, with annuities today, we all have annuities that were locked in at a significantly lower rate. 3% was beautiful five years ago. Today, it’s less than what you get from a Fidelity money market. At which rate I’m going to be happy enough to keep the policy without surrendering it? That’s the type of question that you have to use more data and more AI in order to unlock, in addition to the general average games that companies were doing for years.

Unlocking Lifelong Benefits with Whole Life Insurance

Unlocking Lifelong Benefits with Whole Life Insurance

by: Dror Katzav, CEO and Founder

September 27th, 2022

When it comes to financial planning, insurance plays a pivotal role. While term life insurance offers coverage for a defined period, whole life insurance brings enduring advantages to your financial strategy. Let’s explore why whole life insurance is a valuable addition to your portfolio.

  1. Lifetime Coverage: Unlike term insurance, which expires, whole life insurance covers you for your entire life. Your beneficiaries will receive a death benefit whenever you pass away as long as premiums are paid.
  1. Cash Value Growth and Long-Term Savings: Whole life policies include a cash value component that grows over time. A portion of your premiums contributes to this cash value, which earns interest or dividends, depending on the policy. Some policies invest your premiums in diverse asset classes to provide strong, inflation-beating returns. As cash value accumulates, you can use it for expenses like buying a car, making a down payment on a home, or bolstering your retirement savings.
  1. Tax Advantages: Whole life insurance offers tax benefits. The cash value grows tax-deferred, and beneficiaries typically receive death benefits free of income tax. This means your loved ones receive financial support without tax-related concerns.
  1. Financial Security: A whole life insurance policy provides peace of mind. It acts as a financial safety net for your loved ones, assuring them of financial protection when you’re no longer there to provide. Life is unpredictable, but your family won’t fret about living expenses, mortgages, tuition, debts, and more with the right life insurance. They can maintain their lifestyle and meet financial obligations.

In conclusion, whole life insurance offers a unique blend of protection, savings, and financial flexibility. It’s a versatile tool that benefits you and your loved ones throughout your lifetime. As with any financial decision, consult a qualified insurance professional to select the best policy and coverage for your specific goals. With careful planning, whole life insurance can become a valuable asset for securing your financial future.

How is AI Transforming the Future of Digital Marketing?

How is AI Transforming the Future of Digital Marketing?

by: Dror Katzav, CEO and Founder

December 29th, 2022

Online customer acquisition costs are high for many industries, and the life insurance industry is no exception.


Insurance companies are currently examining their digital platforms and customer journey and searching for new ways to decrease their cost of acquisition (CAC). Many are turning to AI technology for help.


Artificial intelligence (AI) solutions use data to understand the correlations between different parameters for taking action and solving problems. Data is becoming increasingly important as companies look for new ways to understand their customers and create better marketing campaigns. 


By leveraging AI models, companies can better segment audiences and generate highly personalized ad campaigns. AI models and machine learning capabilities leveraging industry data can provide ‘superpowers’ in social campaigns. Advanced AI and predictive technology can accelerate and help marketing campaigns target the right audience with the right message at the right time. Yes, we know Facebook, Google, and other social platforms are experts in this field. However, insurance companies hold the most relevant data about their customers and need to figure out a quick way to understand and monetize it.


Additionally, cross-channel marketing ensures that the data gathered from all channels is used effectively and efficiently. Using AI models trained with industry data enables insurance companies with strong digital capabilities to build highly effective advertising campaigns to reach the right people with the right message.


These models have been shown to accelerate social campaigns allowing them to scale up, dramatically* reducing the cost of acquisition (CAC), improving campaign performance, and helping digital operations grow significantly.


Any edge a company can gain in an increasingly competitive market is essential, and insurance companies that adopt new technologies, especially at the point of sale, will benefit more than others.  


*CAC reduction of 60-90% has been achieved by applying AI Modules. AI in life insurance offers the chance to increase revenue, improve efficiency, and reduce risk. As AI becomes more sophisticated and pervasive, those insurance companies that adopt it will prevail in this competitive landscape.

Life Insurers Could Be Sitting on a Massive Goldmine

Life Insurers Could Be Sitting on a Massive Goldmine

by: Dror Katzav, CEO and Founder

December 21st, 2022

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


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

But what if life insurers used this data?


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


Let’s say a certain policyholder has been missing premium payments for the last few months, which could suggest that the customer is nearing lapsation. The finance department has this information, but the marketing and sales departments are likely kept in the dark with no access to these important records. Any other information about this particular policyholder, such as knowing he moved to a low-income neighborhood three months ago and that he is a mechanic, is not available to these departments in a clear or timely fashion.


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


Should they attempt to contact the customer to prevent lapse? Should they attempt a cross-sale? Should they allow him to lapse his policy? How old is his policy? What is his premium? What makes more sense for the business?


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

A buried treasure, just waiting to be discovered


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


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


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


With Atidot, insurance companies can uncover invisible value in their existing books of business. They’ve been sitting on a goldmine all this time.

Life Insurance 2.0: How Amazon-Inspired Service Providers are Changing the Game

Life Insurance 2.0: How Amazon-Inspired Service Providers are Changing the Game

by: Dror Katzav, CEO and Founder

December 19th, 2022

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


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


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


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


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


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


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