CoChat
Sign Up
Cited by 67Ethics and Social Impacts of AIFinTech, Crowdfunding, Digital FinanceArtificial Intelligence Applications

ChatGPT in finance: Applications, challenges, and solutions

Open access
Khan, Muhammad Salar, Umer, Hamza·2024·Heliyon
DOI 10.1016/j.heliyon.2024.e24890PMC PMC10831748
Open this paper in CoChat — free account

Abstract

The emergence of ChatGPT, a generative artificial intelligence tool, has sparked a revolution in the finance industry, enabling individuals to interact with technology in natural language. However, the use of ChatGPT in finance presents a profound array of ethical considerations that demand careful scrutiny to ensure its responsible and ethical use. After a concise exploration of ChatGPT's applications in finance, this policy article delves into the ethical challenges arising from the use of ChatGPT in finance, including outcomes contaminated with biases, incorporation of fake information in the financial decisions, concerns surrounding privacy and security, lack of transparency and accountability in the decision-making processes and financial services, human job displacement, and the intricate web of legal complexities. Our article asserts that financial institutions employing ChatGPT must proactively devise strategies to confront these burgeoning challenges, mitigating their adverse effects on both individuals and society as a whole. Additionally, we propose relevant policies to tackle these ethical quandaries head-on. In essence, this article illuminates the imperative need for a meticulous ethical framework, facilitating an informed and responsible use of ChatGPT in the realm of finance, safeguarding the welfare of individuals and society. While our work significantly contributes to the research and practice of finance, we also identify future research avenues.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.heliyon.2024.e24890

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Facebook's advertising platform was found to skew the distribution of job and housing ads along gender and racial lines, even when advertisers did not intend to target by those attributes.

    “For instance, a well-documented case involved Facebook's advertising platform (now Meta): investigative journalists and researchers found that Facebook's algorithm for ad delivery was skewing the distribution of job and housing ads along gender and racial lines, even when advertisers did not intend to target by those attributes (Kopalle, 2022;Kumar, V., 2024;Khan, 2024;Kumar, R., 2024).”

    From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Khan, 2024

    The “Biased outcomes” section discusses possible gender and racial bias in ChatGPT’s financial responses, not Facebook’s ad-delivery system. Searches for Facebook job and housing ads and discriminatory ad targeting found no passage addressing the claimed case. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Ads for high-paying jobs in tech were shown far more to men than women; housing ads in certain neighborhoods were shown preferentially to white users over minority users.

    “Ads for high-paying jobs in tech were shown far more to men than women; housing ads in certain neighborhoods were shown preferentially to white users over minority users.”

    From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Khan, 2024

    The ‘Biased outcomes’ section discusses possible gender and racial bias in ChatGPT’s financial responses, but does not report how tech-job or housing ads were shown to users. Searches for the specific ad-delivery examples and their demographic comparisons found no passage addressing them in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 27

  1. [3]Foroumandi E. Moradkhani H. Sanchez-Vila X. Singha K. Castelletti A. Destouni G. ChatGPT in Hydrology and Earth sciences: opportunities, prospects, and concerns Water Resour. Res. 59 10 2023 e2023WR036288 10.1029/2023WR036288 · DOI
  2. [4]Brown T. Language models are few-shot learners Advances in Neural Information Processing Systems 2020 Curran Associates, Inc. 1877 1901. [Online]. Available: https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
  3. [5]Dowling M. Lucey B. ChatGPT for (finance) research: the bananarama conjecture Finance Res. Lett. 53 May 2023 103662 10.1016/j.frl.2023.103662 · DOI
  4. [7]OpenAI “GPT-4 Technical Report.” arXiv 2023 10.48550/arXiv.2303.08774 Mar. 27 · DOI · Paper page
  5. [9]Al-Okaily M. Alghazzawi R. Alkhwaldi A.F. Al-Okaily A. The effect of digital accounting systems on the decision-making quality in the banking industry sector: a mediated-moderated model Global Knowledge, Memory and Communication 2022 10.1108/GKMC-01-2022-0015 ahead-of-print, no. ahead-of-print, Jan · DOI
  6. [10]Al-Okaily M. Al Natour A.R. Shishan F. Al-Dmour A. Alghazzawi R. Alsharairi M. Sustainable FinTech innovation orientation: a moderated model Sustainability 13 24 Jan. 2021 10.3390/su132413591 Art. no. 24 · DOI
  7. [13]Dave T. Athaluri S.A. Singh S. ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations Frontiers in Artificial Intelligence 6 2023 [Online]. Available: https://www.frontiersin.org/articles/10.3389/frai.2023.1169595 PMC10192861 37215063 · DOI · Paper page
  8. [14]Parray A.A. Inam Z.M. Ramonfaur D. Haider S.S. Mistry S.K. Pandya A.K. ChatGPT and global public health: applications, challenges, ethical considerations and mitigation strategies Global Transitions 5 Jan. 2023 50 54 10.1016/j.glt.2023.05.001 · DOI · Paper page
  9. [16]Feng Z. Hu G. Li B. Unleashing the power of ChatGPT in finance research: opportunities and challenges Rochester, NY 29 2023 10.2139/ssrn.4424979 Mar · DOI
  10. [18]Zaremba A. Demir E. ChatGPT: Unlocking the Future of NLP in Finance 2023 10.2139/ssrn.4323643 Rochester, NY, Jan. 13 · DOI
  11. [19]Haleem A. Javaid M. Singh R.P. An era of ChatGPT as a significant futuristic support tool: a study on features, abilities, and challenges BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2 4 Oct. 2022 100089 10.1016/j.tbench.2023.100089 · DOI · Paper page
  12. [20]Zhang Y. Zhang H. FinBERT–MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm Neural Process. Lett. Apr. 2023 10.1007/s11063-023-11266-5 · DOI
  13. [22]J. Paul, A. Ueno, and C. Dennis, “ChatGPT and consumers: benefits, pitfalls and future research agenda,” Int. J. Consum. Stud., vol. n/a, no. n/a, doi: 10.1111/ijcs.12928.. · DOI · Paper page
  14. [23]McGee R.W. “Is Chat Gpt Biased Against Conservatives? An Empirical Study.” 15 2023 10.2139/ssrn.4359405 Rochester, NY, Feb · DOI · Paper page
  15. [24]Hartmann J. Schwenzow J. Witte M. “The political ideology of conversational AI: Converging evidence on ChatGPT’s pro-environmental, left-libertarian orientation.” 2023 10.48550/arXiv.2301.01768 arXiv, Jan. 05 · DOI · Paper page
  16. [25]Krause D. “ChatGPT and Other AI Models as a Due Diligence Tool: Benefits and Limitations for Private Firm Investment Analysis.” 2023 10.2139/ssrn.4416159 Rochester, NY, Apr. 11 · DOI
  17. [26]Ali H. Aysan A.F. “What will ChatGPT Revolutionize in Financial Industry?” Rochester 28 2023 10.2139/ssrn.4403372 NY, Mar · DOI
  18. [27]Dwivedi Y.K. ‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy Int. J. Inf. Manag. 71 Aug. 2023 102642 10.1016/j.ijinfomgt.2023.102642 · DOI · Paper page
  19. [28]Liu Y. Jailbreaking ChatGPT via Prompt Engineering: an Empirical Study 2023 10.48550/arXiv.2305.13860 arXiv, May 23 · DOI
  20. [29]Derner E. Batistič K. “Beyond the Safeguards: Exploring the Security Risks of ChatGPT.” 2023 10.48550/arXiv.2305.08005 arXiv, May 13 · DOI
  21. [30]Voigt P. von dem Bussche A. Scope of application of the GDPR Voigt P. von dem Bussche A. The EU General Data Protection Regulation (GDPR): A Practical Guide 2017 Springer International Publishing Cham 9 30 10.1007/978-3-319-57959-7_2 · DOI
  22. [32]Abdullah M. Madain A. Jararweh Y. ChatGPT: fundamentals, applications and social impacts 2022 Ninth International Conference On Social Networks Analysis, Management and Security (SNAMS) Nov. 2022 1 8 10.1109/SNAMS58071.2022.10062688 · DOI · Paper page
  23. [33]Khan M.S. Olds J.L. When neuro-robots go wrong: a review Front. Neurorob. 17 2023 [Online]. Available: https://www.frontiersin.org/articles/10.3389/fnbot.2023.1112839 PMC9935594 36819005 · DOI
  24. [34]Khan M.S. Nayebpour M. Li M.-H. El-Amine H. Koizumi N. Olds J.L. “Explainable AI: a Neurally-inspired decision stack framework,” Biomimetics 7 3 Sep. 2022 10.3390/biomimetics7030127 Art. no. 3 PMC9496620 36134931 · DOI
  25. [35]Rivas P. Zhao L. “Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology,” AI 4 2 Jun. 2023 10.3390/ai4020019 Art. no. 2 · DOI · Paper page
  26. [37]Khan M.S. A Multidimensional Approach towards Addressing Existing and Emerging Challenges in the Use of ChatGPT Oct. 2023 AI Ethics 10.1007/s43681-023-00360-y · DOI
  27. [38]Baltimore D. A prudent path forward for genomic engineering and germline gene modification Science 348 6230 Apr. 2015 36 38 10.1126/science.aab1028 25791083 PMC4394183 · DOI

Cited by

Sorted by citations

Authors
Khan, M. S., & Umer, H.
Affiliations
  • George Mason University (US)
  • Hitotsubashi University (JP)
Corresponding author
  • Khan, Muhammad Salar
  • Umer, Hamza
ORCID
Venue
Heliyon
Publisher
Elsevier BV
Published
2024-01
Volume
10
Issue
2
Pages
e24890
ISSN
2405-8440 (ISSN-L)
DOI
10.1016/j.heliyon.2024.e24890
PMC
PMC10831748
OpenAlex
W4391036703
Cited by
67
References
9
Citations by year
2026: 15 · 2025: 32 · 2024: 20
FWCI
27.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Heliyon (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
ChatGPT, generative AI, finance, AI ethics, algorithmic bias, fake information, privacy and security, transparency and accountability, job displacement, legal challenges, ethical frameworks, AI policy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review