# Predicting Mortality from Credit Reports

arXiv: https://arxiv.org/abs/2111.03662 · HTML: https://mlsanity.com/paper/2111.03662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)

## Built on by (most cited first, 1 of 1)

- [Payday loans -- blessing or growth suppressor? Machine Learning Analysis](https://mlsanity.com/paper/2205.15320/) (2022)

## Abstract (arXiv)

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.