Convolutional neural network case studies: (1) anomalies in mortality rates (2) image recognition

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See all articles by Daniel Meier

Daniel Meier

Swiss Reinsurance Company

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: July 19, 2020

Abstract

We provide a general introduction to convolutional neural networks (CNNs) in this tutorial.
CNNs are particularly well suited to nd common spatial structure in images or time series.
As an insurance related example for life & health insurance we illustrate how to use a CNN
to detect anomalies in mortality rates taken from the Human Mortality Database (HMD);
the anomalies are caused by migration between countries and other errors. As a second
example, we study a CNN to classify images of handwritten digits taken from one of the
most widely used benchmark datasets, the Modi ed National Institute of Standards and
Technology (MNIST) dataset. Our aim is to explore and discuss the building blocks and the
properties of these CNNs, and we showcase their use.

Keywords: Convolutional neural network, CNN, regression, classi cation, mortality rates

JEL Classification: G22

Suggested Citation

Meier, Daniel and Wuthrich, Mario V., Convolutional neural network case studies: (1) anomalies in mortality rates (2) image recognition (July 19, 2020). Available at SSRN: https://ssrn.com/abstract=

Daniel Meier (Contact Author)

Swiss Reinsurance Company ( email )

Mythenquai 50/60
P.O. Box
CH-8022 Zurich
Switzerland

Mario V. Wuthrich

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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