This article is an introduction to machine learning for financial forecasting, planning and analysis (FP\&A). Machine learning appears well suited to support FP\&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP\&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.
翻译:本文是用于财务预测、规划和分析的机器学习(FP ⁇ A)的导言。机器学习似乎非常适合支持FP ⁇ A,高度自动化地从大量数据中提取信息。然而,由于大多数传统的机器学习技术侧重于预测(准备),我们讨论必须特别小心避免在规划和资源分配中使用机器学习的陷阱(必然推论),虽然天真地应用机器学习通常在这方面失败,但最近开发的双机学习框架可以解决引起兴趣的因果关系问题。我们审查目前有关FP ⁇ A机器学习的文献,并在模拟研究中说明机器学习如何用于预测和规划。我们还调查随着数据点的增加,预测和规划如何改进。