mlsanityData to 14 Oct 2026

AI in Finance: Challenges, Techniques and Opportunities

arXiv 2107.09051 ↗First posted 20 Jul 2021q-fin.CP cs.AI cs.CEcited by 81

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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.

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

44 of 216 references.

References · 44

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32 more
  1. 2019Reinforcement Learning for Portfolio Managementcited by 12
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 12; 2024: 23; 2025: 26; 2026: 12.

81 papers.

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  10. 2024Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Modelscited by 9
  11. 2025Is Trust Correlated With Explainability in AI? A Meta-Analysiscited by 8
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  19. 2025RobustX: Robust Counterfactual Explanations Made Easycited by 6
  20. 2022Error Parity Fairness: Testing for Group Fairness in Regression Taskscited by 5
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  23. 2024FANAL -- Financial Activity News Alerting Language Modeling Frameworkcited by 4
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  25. 2021Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretationcited by 3
  26. 2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detectioncited by 3
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  29. 2025On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomycited by 3
  30. 2022On Explainability in AI-Solutions: A Cross-Domain Surveycited by 2
  31. 2023Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modelingcited by 2
  32. 2023Tabular Few-Shot Generalization Across Heterogeneous Feature Spacescited by 2
  33. 2024AI in ESG for Financial Institutions: An Industrial Surveycited by 2
  34. 2025QuantMCP: Grounding Large Language Models in Verifiable Financial Realitycited by 2
  35. 2026Controllable Concept Bottleneck Modelscited by 2
  36. 2022Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Managementcited by 1
  37. 2022Do Deep Neural Networks Always Perform Better When Eating More Data?cited by 1
  38. 2023The Promise and Peril of Artificial Intelligence -- Violet Teaming Offers a Balanced Path Forwardcited by 1

50 most cited of 81 All 81 (JSON)