Bitesize Impact 25: Atidot


Atidot is a cloud-based machine learning platform offering predictive analytics for life insurance.

Atidot – which is Hebrew for fortune telling – combines data held by insurers such as age, gender and occupation, with open sources such as census data and property prices, to build cohorts of existing customers, predict their behaviour, and flag them for CRM purposes.

In January the company completed a $5m Series A fundraising round, generating significant runway to hone and scale the proposition. This has been used in part to build out the team across the globe with hires in the US, London and Switzerland.

Systemic legacy issues plague the life and pensions sector. Clients struggle to identify, extract and combine even internal data sources, missing opportunities across the value chain – finding new business, addressing persistency issues, identifying transition points from accumulation to decumulation, for example.

Aditot claims that interest from incumbents of all sizes continues to grow and emphasizes the ease of use of its platform as a major attraction: cloud-based, ‘drag and drop’ functionality that allows data to be ingested more rapidly from sources including legacy systems (up to one million policies per hour). It is then cleansed, restructured and models layered on top to generate actionable insights.

In addition to the pilots focused on underinsurance and lapses discussed in our Impact 25 profile, Atidot is set to begin a field test with another top 3 global insurer in the US to identify underinvestment in annuity products. The company is in negotiations with three other insurance partners and a reinsurer.

While the company has the tools to support scouting and obtaining new customers, it says its focus will remain on optimising existing books of business, and building out its advisory capabilities.


Reconnecting with a customer base that has been getting increasingly distanced from insurers has been a primary goal of many insurance executives. Harnessing the power of data across the life and pensions estate has been the much discussed – but rarely executed – solution to this goal. Whether is it is understanding lapse triggers, identifying underserved needs at different life stages, or connecting with the customer before the neighbourhood IFA calls to consolidate with anther provider, each of these goals have significant benefits attached.

Given the numbers involved, Atidot will have no shortage of opportunities if it has truly made it easier to get a real understanding of behavioural drivers in the life and pensions sector through more efficient access to data and analysis through preconfigured models.

That said, data and analysis is only one of several issues that insurers face doing this work. A lack of internal change capabilities, corporate project lifecycles and funding issues remain challenges for many insurers to derive value from their customer bases. For this reason we often make the point that InsurTech is not a silver bullet – it is simply a very useful tool that can be used within a broader change framework.

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About the author

Chris Hess is an alumnus of Oxbow Partners.

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