mlsanityData to 14 Oct 2026

A Comprehensive Review on Financial Explainable AI

arXiv 2309.11960 ↗First posted 21 Sep 2023cs.AI cs.CE q-fin.CPcited by 10

Not on the map. Why?

Abstract (arXiv)

The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns. However, due to their lack of explainability, there are significant concerns regarding their use in critical sectors, such as finance and healthcare, where decision-making transparency is of paramount importance. In this paper, we provide a comparative survey of methods that aim to improve the explainability of deep learning models within the context of finance. We categorize the collection of explainable AI methods according to their corresponding characteristics, and we review the concerns and challenges of adopting explainable AI methods, together with future directions we deemed appropriate and important.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 61 of its 178 references that are in the map. First posted on arXiv 21 Sep 2023.

Builds on

61 of 178 references.

References · 61

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  6. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  7. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  8. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  9. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  10. 2023BloombergGPT: A Large Language Model for Financecited by 939
  11. 2016The Mythos of Model Interpretabilitycited by 721
  12. 2020Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?cited by 599
38 more
  1. 2019Federated Machine Learning: Concept and Applicationscited by 412
  2. 2019Is Attention Interpretable?cited by 389
  3. 2020What is being transferred in transfer learning?cited by 368
  4. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 344
  5. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
  6. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 285
  7. 2018Metrics for Explainable AI: Challenges and Prospectscited by 260
  8. 2023FinGPT: Open-Source Financial Large Language Modelscited by 253
  9. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 246
  10. 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 213
  11. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  12. 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
  13. 2023Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Modelscited by 129
  14. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4cited by 73
  15. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 72
  16. 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
  17. 2023The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challengescited by 59
  18. 2021Active Learning for Domain Adaptation: An Energy-Based Approachcited by 53
  19. 2022Evaluation of post-hoc interpretability methods in time-series classificationcited by 53
  20. 2023EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commercecited by 49
  21. 2018Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systemscited by 48
  22. 2018Interpretable Credit Application Predictions With Counterfactual Explanationscited by 43
  23. 2020Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classificationcited by 40
  24. 2019Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Modelscited by 26
  25. 2021To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodscited by 26
  26. 2021Designing Inherently Interpretable Machine Learning Modelscited by 23
  27. 2022Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappingscited by 23
  28. 2021Explainable AI in Credit Risk Managementcited by 19
  29. 2020Explainable AI for Interpretable Credit Scoringcited by 18
  30. 2019Explainable Text-Driven Neural Network for Stock Predictioncited by 13
  31. 2022Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoringcited by 12
  32. 2020Machine Learning Explanations to Prevent Overtrust in Fake News Detectioncited by 11
  33. 2018Instance-Level Explanations for Fraud Detection: A Case Studycited by 10
  34. 2019Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy predictioncited by 6
  35. 2022Can Interpretable Reinforcement Learning Manage Prosperity Your Way?cited by 6
  36. 2022Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detectioncited by 6
  37. 2021On the Trustworthiness of Tree Ensemble Explainability Methodscited by 5
  38. 2021Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authoritiescited by 5

50 most cited of 61 All 61 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 3.

10 papers.

  1. 2024How Interpretable are Reasoning Explanations from Prompting Large Language Models?cited by 29
  2. 2024XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Modelscited by 23
  3. 2024Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Modelscited by 14
  4. 2025New Money: A Systematic Review of Synthetic Data Generation for Financecited by 5
  5. 2024A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Researchcited by 2
  6. 2025Bridging Minds and Machines: Toward an Integration of AI and Cognitive Sciencecited by 2
  7. 2025Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routingcited by 0
  8. 2026Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Modelcited by 0
  9. 2026Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AIcited by 0
  10. 2026Explaining Time Series Forecasting with Horizon-Resolved Attributioncited by 0