mlsanityData to 7 Oct 2026

Opportunities and Challenges of Generative-AI in Finance

arXiv 2410.15653 ↗First posted 21 Oct 2024cs.AIcited by 6

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Abstract (arXiv)

Gen-AI techniques are able to improve understanding of context and nuances in language modeling, translation between languages, handle large volumes of data, provide fast, low-latency responses and can be fine-tuned for various tasks and domains. In this manuscript, we present a comprehensive overview of the applications of Gen-AI techniques in the finance domain. In particular, we present the opportunities and challenges associated with the usage of Gen-AI techniques. We also illustrate the various methodologies which can be used to train Gen-AI techniques and present the various application areas of Gen-AI technologies in the finance ecosystem. To the best of our knowledge, this work represents the most comprehensive summarization of Gen-AI techniques within the financial domain. The analysis is designed for a deep overview of areas marked for substantial advancement while simultaneously pin-point those warranting future prioritization. We also hope that this work would serve as a conduit between finance and other domains, thus fostering the cross-pollination of innovative concepts and practices.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 59 of its 112 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

59 of 112 references.

References · 59

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  6. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  7. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  8. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  9. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  10. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  11. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  12. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
38 more
  1. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
  2. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,127
  3. 2022In-context Learning and Induction Headscited by 930
  4. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  5. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  6. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  7. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 798
  8. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
  9. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
  10. 2024Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Servecited by 507
  11. 2024Large Language Models: A Surveycited by 506
  12. 2021A White Paper on Neural Network Quantizationcited by 453
  13. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  14. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  15. 2021Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworkscited by 278
  16. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 261
  17. 2023Large Language Models in Finance: A Surveycited by 223
  18. 2022ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answeringcited by 194
  19. 2024SaulLM-7B: A pioneering Large Language Model for Lawcited by 136
  20. 2023Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Modelscited by 125
  21. 2023DocLLM: A layout-aware generative language model for multimodal document understandingcited by 112
  22. 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insightscited by 88
  23. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  24. 2021AI in Finance: Challenges, Techniques and Opportunitiescited by 81
  25. 2024ServerlessLLM: Low-Latency Serverless Inference for Large Language Modelscited by 78
  26. 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 77
  27. 2023Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Taskscited by 59
  28. 2023Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutionscited by 59
  29. 2013Estimating or Propagating Gradients Through Stochastic Neuronscited by 54
  30. 2024Understanding the Performance and Estimating the Cost of LLM Fine-Tuningcited by 48
  31. 2023Summarization is (Almost) Deadcited by 43
  32. 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selectioncited by 29
  33. 2024Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendationscited by 28
  34. 2023Synthetic Data Applications in Financecited by 21
  35. 2023End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directionscited by 17
  36. 2024StockGPT: A GenAI Model for Stock Prediction and Tradingcited by 11
  37. 2024CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM Pipelinescited by 11
  38. 2024Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Studycited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

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  2. 2025A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Servicescited by 15
  3. 2025Standard Benchmarks Fail -- Auditing LLM Agents in Finance Must Prioritize Riskcited by 13
  4. 2025Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challengescited by 4
  5. 2026Simulating Complex Multi-Turn Tool Calling Interactions in Stateless Execution Environmentscited by 3
  6. 2025Banking Done Right: Redefining Retail Banking with Language-Centric AIcited by 1