How to Get Real Results in Predictive Analytics

Rosmery Cruz

In Brief

Contact RGA's research team to learn more about data science, predictive analytics and insurance.

Click here to view a presentation on this subject from the 2018 SOA Predictive Analytics Symposium.

RGA shares basic steps insurers can take to prevent overfitting for more accurate predictive models.

Ever-increasing computing power allows analysts to manipulate data and build models from both small and large datasets more quickly and more effectively than ever before. Yet these enhanced capabilities also come with a greater number of choices and greater exposure to building models that overfit the data.

Tasked with determining an applicant’s mortality risk years into the future, life insurers must be acutely aware of the potential pitfalls of overfitting, take steps to accurately validate every predictive model they develop, and work continuously to update and improve models as new forms of data become available.

See also:

If you would like to discuss this webcast further, please e-mail us at: [email protected]. Contact RGA's research team to learn more about post-term lapse, customer engagement, and insurance.

Meet the Authors & Experts

Expert

Rosmery Cruz

Executive Director, Behavioral Data Science Risk and Behavioral Science, RGA

How to Get Real Results in Predictive Analytics

More Like This...

Related Solutions

Meet the Authors & Experts