# Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls

arXiv: https://arxiv.org/abs/2107.04851 · HTML: https://mlsanity.com/paper/2107.04851/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 10 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [Curriculum Learning in Deep Neural Networks for Financial Forecasting](https://mlsanity.com/paper/1904.12887/) (2019)
- [High-Dimensional Metrics in R](https://mlsanity.com/paper/1603.01700/) (2016)
- [Revenue Forecasting for Enterprise Products](https://mlsanity.com/paper/1701.06624/) (2016)

## Built on by (most cited first, 5 of 5)

- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [What are the Machine Learning best practices reported by practitioners on Stack Exchange?](https://mlsanity.com/paper/2301.10516/) (2023)
- [A Conceptual Model for AI Adoption in Financial Decision-Making: Addressing the Unique Challenges of Small and Medium-Sized Enterprises](https://mlsanity.com/paper/2512.04339/) (2025)
- [Privacy-Preserving Model and Preprocessing Verification for Machine Learning](https://mlsanity.com/paper/2501.08236/) (2025)
- [Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting](https://mlsanity.com/paper/2606.27688/) (2026)

## Abstract (arXiv)

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.