论文标题
使用机器学习为福利收件人创建预警系统
Using Machine Learning to Create an Early Warning System for Welfare Recipients
论文作者
论文摘要
使用机器学习工具，使用高质量的全国社会保障数据，为2014年至2018年间，为澳大利亚社会保障系统的任何付款人提供了预测的收入支持收据强度的预测模型。我们表明，与当前使用中的简单启发式模型或预警系统相比，现成的机器学习算法可以显着提高预测性准确性。具体而言，前者预测，与后者相比，该时间个人在随后的四年中的时间比例至少为22％（R2增加了14个百分点）。由于算法使用当前可用于案例工作者可用的管理数据，因此可以无需额外的费用就可以实现这一收益。因此，我们的机器学习算法可以改善对长期收入支持者的检测，这可能会使政府为应计福利成本节省大量资金。
Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals are on income support in the subsequent four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at no extra cost to practitioners since the algorithms use administrative data currently available to caseworkers. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients, which can potentially provide governments with large savings in accrued welfare costs.
