Risk Selection in a Fluidless Environment
With the introduction of digital activity and artificial intelligence (AI), risk assessment is entering a new era that utilizes digital tools to create more robust and sustainable models. Munich Re North America Life is actively involved in applying machine learning and predictive models to analyze risk in a faster, less invasive manner.
With the COVID-19 pandemic, the need for accelerated underwriting (AUW) in a fluidless environment has become urgent. The webinar on “Risk Selection in a Fluidless Environment” explores how we can understand if and when fluid testing is useful or needed in the underwriting process.
Thomas Naraindas, Senior Data Scientist, Integrated Analytics, expands on the utilization of data analytics, predictive modeling, risk algorithms, and AUW programs.
This is the second in Munich Re’s new five-part North American series exploring innovations in underwriting and data science.
- The use of predictive analytics in life insurance: Over the last number of years, predictive analytics has become more common in life insurance. We look into what roles predictive underwriting, portfolio monitoring, and claims management play in the risk selection ecosystem.
- Accelerated Underwriting Programs: We cover various methods of incorporating predictive analytics into accelerated underwriting (AUW) programs and provides case studies on risk missclassification scenarios.
- Third-party data and risk algorithms: In addition to custom predictive models built for a carrier, many third-party data and tools can be leveraged for the accelerated underwriting process.
Watch a recording of the webinar and download the executive summary for more information.