  Data on hundreds of variables related to individual consumer finance behavior
(such as credit card and loan activity) is routinely collected in many
countries and plays an important role in lending decisions. We postulate that
the detailed nature of this data may be used to predict outcomes in seemingly
unrelated domains such as individual health. We build a series of machine
learning models to demonstrate that credit report data can be used to predict
individual mortality. Variable groups related to credit cards and various
loans, mostly unsecured loans, are shown to carry significant predictive power.
Lags of these variables are also significant thus indicating that dynamics also
matters. Improved mortality predictions based on consumer finance data can have
important economic implications in insurance markets but may also raise privacy
concerns.
