Life Insurance | Sikka Software

DATA SCIENCE

INSURANCE TECHNOLOGY

We use machine learning, artificial intelligence and data science to create our proprietary Sikka Tobacco Indicator and Life Expectancy at Current Age (LECA) Score®, which help insurance companies deliver real-time decisions in underwriting policies and population modeling.

Sikka is a very interesting company that’s developed sophisticated indicators around Tobacco, Cardiovascular, and Diabetes from dental records.

Manisha Dias, FSA, FCIA, CERA – AVP of Strategic Partnerships at SCOR

DATA SCIENCE

INSURANCE TECHNOLOGY

Accelerated Underwriting

Sikka delivers real-time individual decision API scores including a Sikka Tobacco Indicator and a LECA Score® to facilitate life insurance underwriting and pricing.

Real-Time Scoring

Sikka provides an instant LECA Score® on life expectancy, a score reflecting tobacco use, and additional scores on certain chronic diseases and conditions, all with appropriate consent.

Retrospective Studies

Dataset validated with comprehensive retrospective studies with industry leaders.

RETROSPECTIVE STUDIES

Oral Health, Mortality and Smoker Detection

Oral health is recognized by the medical community as an important determinant of overall health, and is associated with other health conditions, socioeconomic indicators, and lifestyle behaviors that are risk factors for mortality. As such, dental health records may be a fertile source of data for life insurers.

The Referee, the Scorekeeper, and the Coach: Which Insurtech technology are You installing?

By Greg Solomon, Managing Director, Head of Life & Health for Peak Re, Hong Kong

As this insurance industry insider thought leadership piece states, Sikka Software epitomizes the Scorekeeper model, with our data showing the strong correlation between oral and overall health.

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Sikka Tobacco Indicator

Sikka’s Tobacco Indicator is generated for an individual based on his or her dental clinical notes and completed oral health evaluations, which have been fed through a machine-learning model. Sikka’s patient records are assigned to one of three classes: tobacco user (T), not a tobacco user (NT), or no evidence of tobacco use (N).

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