# AI in Finance: Challenges, Techniques and Opportunities

arXiv: https://arxiv.org/abs/2107.09051 · HTML: https://mlsanity.com/paper/2107.09051/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 216 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 30 of 44)

- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [An Attentive Survey of Attention Models](https://mlsanity.com/paper/1904.02874/) (2019)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications](https://mlsanity.com/paper/2010.05867/) (2020)
- [Risk-Averse Trust Region Optimization for Reward-Volatility Reduction](https://mlsanity.com/paper/1912.03193/) (2019)
- [Neural networks for option pricing and hedging: a literature review](https://mlsanity.com/paper/1911.05620/) (2019)
- [Decision support from financial disclosures with deep neural networks and transfer learning](https://mlsanity.com/paper/1710.03954/) (2017)
- [Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets](https://mlsanity.com/paper/1903.06478/) (2019)
- ["The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering](https://mlsanity.com/paper/1912.10858/) (2019)
- [Reinforcement Learning for Portfolio Management](https://mlsanity.com/paper/1909.09571/) (2019)
- [Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2011.08011/) (2020)
- [Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey](https://mlsanity.com/paper/1904.04973/) (2019)
- [Variable-lag Granger Causality for Time Series Analysis](https://mlsanity.com/paper/1912.10829/) (2019)
- [Coupling Learning of Complex Interactions](https://mlsanity.com/paper/2007.13534/) (2020)
- [Neural Gaussian Copula for Variational Autoencoder](https://mlsanity.com/paper/1909.03569/) (2019)
- [Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market](https://mlsanity.com/paper/1903.12258/) (2019)
- [Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting](https://mlsanity.com/paper/1812.06175/) (2018)
- [Multimodal deep learning for short-term stock volatility prediction](https://mlsanity.com/paper/1812.10479/) (2018)
- [Data science and AI in FinTech: An overview](https://mlsanity.com/paper/2007.12681/) (2020)
- [Deep Learning for Forecasting Stock Returns in the Cross-Section](https://mlsanity.com/paper/1801.01777/) (2018)
- [Corruption Risk in Contracting Markets: A Network Science Perspective](https://mlsanity.com/paper/1909.08664/) (2019)
- [Machine Learning Optimization Algorithms & Portfolio Allocation](https://mlsanity.com/paper/1909.10233/) (2019)
- [A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy](https://mlsanity.com/paper/1910.01491/) (2019)
- [Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction](https://mlsanity.com/paper/1812.11226/) (2018)
- [Gaussian Copula Variational Autoencoders for Mixed Data](https://mlsanity.com/paper/1604.04960/) (2016)
- [Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.09403/) (2020)
- [Lagged correlation-based deep learning for directional trend change prediction in financial time series](https://mlsanity.com/paper/1811.11287/) (2018)

## Built on by (most cited first, 30 of 81)

- [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity](https://mlsanity.com/paper/2301.12867/) (2023)
- [Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations](https://mlsanity.com/paper/2206.01254/) (2022)
- [MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2401.00423/) (2023)
- [FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models](https://mlsanity.com/paper/2405.14767/) (2024)
- [Universal Time-Series Representation Learning: A Survey](https://mlsanity.com/paper/2401.03717/) (2024)
- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity](https://mlsanity.com/paper/2401.11750/) (2024)
- [Editable Concept Bottleneck Models](https://mlsanity.com/paper/2405.15476/) (2024)
- [Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08758/) (2025)
- [As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making](https://mlsanity.com/paper/2501.12868/) (2025)
- [Human Decision-making is Susceptible to AI-driven Manipulation](https://mlsanity.com/paper/2502.07663/) (2025)
- [ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection](https://mlsanity.com/paper/2502.06774/) (2025)
- [LLM/Agent-as-Data-Analyst: A Survey](https://mlsanity.com/paper/2509.23988/) (2025)
- [A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data](https://mlsanity.com/paper/2407.02112/) (2024)
- [Contestable AI needs Computational Argumentation](https://mlsanity.com/paper/2405.10729/) (2024)
- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study](https://mlsanity.com/paper/2308.01915/) (2023)
- [Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models](https://mlsanity.com/paper/2407.03689/) (2024)
- [Is Trust Correlated With Explainability in AI? A Meta-Analysis](https://mlsanity.com/paper/2504.12529/) (2025)
- [FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain](https://mlsanity.com/paper/2510.15232/) (2025)
- [Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifesto](https://mlsanity.com/paper/2312.01818/) (2023)
- [HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark](https://mlsanity.com/paper/2506.03954/) (2025)
- [Can Interpretable Reinforcement Learning Manage Prosperity Your Way?](https://mlsanity.com/paper/2202.09064/) (2022)
- [Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation](https://mlsanity.com/paper/2308.06100/) (2023)
- [The AI Revolution: Opportunities and Challenges for the Finance Sector](https://mlsanity.com/paper/2308.16538/) (2023)
- [Opportunities and Challenges of Generative-AI in Finance](https://mlsanity.com/paper/2410.15653/) (2024)

## Abstract (arXiv)

AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.