Augment decisions with greater insights for all stakeholders

Customers understand personal health risks to plan for.
Underwriters gain more granular views of risks.
Agents see prescreen products customers can get approval for.

Hero AI

Turning new open data sources into reliable AI-driven risk insights

Open & consent
data sources

Data Sources

Risk models for underwriters

Health insights for consumers

Customers

AI-generated predictive health scores. Algorithmically, clinically, actuarially validated.

Data-to-insight conversion

Gain unprecedented insights extracted from machine learning of over 33 million lives and counting.

Health risk prediction

Assess health status and forecast possible health risks with predictive health scores ― intuitively explainable to customers and underwriters.

Model accuracy validation

Verify accuracy of predictive risk models against actuarial benchmarks, underwriting standards, and risk thresholds defined by insurers.

Underwriting optimization

Streamline human-made decisions with AI-driven insights for greater speed and reliability ― without unnecessary medical examinations.

Digital sales experience

Make insurance easier and more personalized to buy, while protecting customer data privacy and improving sales conversions.

Peer-reviewed

Lydia AI algorithms have been published in international peer review journals.

Market-tested

Lydia-powered companies are staying ahead of the curve

95%

of people are willing to consent data to get a personalized AI health score assessment

24~35%

improvement in mortality risk assessment compared to industry benchmarks in the 90th percentile across sex and age groups

+80%

alignment in decisions between Lydia AI and human underwriters

80%

of cases underwritten and completed in less than 7 days with Lydia

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Recognition

Award-winning

Taiwan Life received the 2022 Celent Model Insurer Award for Data, Analytics and AI for their insurance app powered by Lydia Health AI.

Read case study
Using machine learning models to accelerate underwriting decisions is a major trend; however, there has not been an established industry best-standard practice for how to validate its use in underwriting. The joint-team worked together to develop a methodology to translate machine learning predictive output results into data-driven reference markets that are compatible with use by actuaries and underwriters.
Max Ang
Max Ang
Celent Apac Insurance Technology Research Leader

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And get ready to reimagine your business with Lydia

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