Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

Helmut Wasserbacher () and Martin Spindler ()
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Helmut Wasserbacher: Novartis International AG, Novartis Campus
Martin Spindler: University of Hamburg

Digital Finance, 2022, vol. 4, issue 1, No 3, 63-88

Abstract: Abstract 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.

Keywords: Financial planning; Machine learning; Forecasting; Causal machine learning; Big data; Double machine learning (search for similar items in EconPapers)
JEL-codes: C53 C55 G17 G31 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s42521-021-00046-2

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