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What is Actuarial
Data Science?


Actuarial data science is an extension of traditional actuarial science and it defines the application of machine learning techniques in problems such as pricing, risk predictions or lapse predictions.


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Classically, actuaries possess a lot of domain knowledge and create linear models to produce statistics required for regulatory reporting and risk management. Actuarial data scientists are more focused on programming and sophisticated statistics to automaticallyextract patterns from data as well as produce more robust predictions and visualizations.

Why Actuarial Data Science is
important for insurers?


Increased prediction accuracy


Advanced machine learning algorithms can increase predictive force significantly – it is a fact that was proven in many industries.

Faster processes


Properly deployed models can speed up the process from days to just few minutes of execution and 2-3 hours of analysis.


Reduced costs


Automation and deployment of actuarial processes reduces costs and impacts policy prices and shareholder equity leading to competitive advantage.

Explainability


Novel techniques of Explainable AI reduces the black-box-ness of actuarial and machine learning models, which results in better understanding of risk.