# Deep Learning for Financial Applications : A Survey

arXiv: https://arxiv.org/abs/2002.05786 · HTML: https://mlsanity.com/paper/2002.05786/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 30 of 91)

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- [Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning](https://mlsanity.com/paper/2410.16105/) (2024)
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- [GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection](https://mlsanity.com/paper/2311.09620/) (2023)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
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- [Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features](https://mlsanity.com/paper/2207.04307/) (2022)
- [Interpretability in deep learning for finance: a case study for the Heston model](https://mlsanity.com/paper/2104.09476/) (2021)
- [Wild-Tab: A Benchmark For Out-Of-Distribution Generalization In Tabular Regression](https://mlsanity.com/paper/2312.01792/) (2023)
- [Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls](https://mlsanity.com/paper/2107.04851/) (2021)
- [Multicriteria interpretability driven Deep Learning](https://mlsanity.com/paper/2111.14088/) (2021)
- [Dynamic Time Warping based Adversarial Framework for Time-Series Domain](https://mlsanity.com/paper/2207.04308/) (2022)
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## Abstract (arXiv)

Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.