Generative AI in finance: Replicability, methodological contingencies, and future research directions
<p dir="ltr">Generative Artificial Intelligence (AI) is reshaping finance by transforming decision-making, risk management, and stakeholder engagement. This study provides a theory-informed synthesis of 84 peer-reviewed articles (2022–2025) using PRISMA-based screening, bibliometric...
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513524587298816 |
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| author | Hassnian Ali (22330396) |
| author2 | Muhammad Bilal Zafar (22392157) Ahmet Faruk Aysan (11902115) |
| author2_role | author author |
| author_facet | Hassnian Ali (22330396) Muhammad Bilal Zafar (22392157) Ahmet Faruk Aysan (11902115) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hassnian Ali (22330396) Muhammad Bilal Zafar (22392157) Ahmet Faruk Aysan (11902115) |
| dc.date.none.fl_str_mv | 2025-11-04T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.frl.2025.108797 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Generative_AI_in_finance_Replicability_methodological_contingencies_and_future_research_directions/31047697 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Artificial intelligence Data management and data science Machine learning Philosophy and religious studies Applied ethics Generative AI Finance Bibliometric Systematic literature review Structural topic modeling |
| dc.title.none.fl_str_mv | Generative AI in finance: Replicability, methodological contingencies, and future research directions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Generative Artificial Intelligence (AI) is reshaping finance by transforming decision-making, risk management, and stakeholder engagement. This study provides a theory-informed synthesis of 84 peer-reviewed articles (2022–2025) using PRISMA-based screening, bibliometric analysis, and Structural Topic Modeling (STM). Six themes emerge: financial decision-making, ESG analytics, stock market prediction, advanced modeling for fraud detection and explainable AI, ChatGPT in accounting and education, and sentiment analysis with domain-specific LLMs. Findings show that generative AI enhances predictive capabilities and ESG assessments but raises issues of bias, transparency, and regulation. The review outlines future research priorities around interpretability, multimodal data, and governance frameworks.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Finance Research Letters<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.frl.2025.108797" target="_blank">https://dx.doi.org/10.1016/j.frl.2025.108797</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_22aaff9efd9f668c493f46b08c3dfeb9 |
| identifier_str_mv | 10.1016/j.frl.2025.108797 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31047697 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Generative AI in finance: Replicability, methodological contingencies, and future research directionsHassnian Ali (22330396)Muhammad Bilal Zafar (22392157)Ahmet Faruk Aysan (11902115)Commerce, management, tourism and servicesBanking, finance and investmentInformation and computing sciencesArtificial intelligenceData management and data scienceMachine learningPhilosophy and religious studiesApplied ethicsGenerative AIFinanceBibliometricSystematic literature reviewStructural topic modeling<p dir="ltr">Generative Artificial Intelligence (AI) is reshaping finance by transforming decision-making, risk management, and stakeholder engagement. This study provides a theory-informed synthesis of 84 peer-reviewed articles (2022–2025) using PRISMA-based screening, bibliometric analysis, and Structural Topic Modeling (STM). Six themes emerge: financial decision-making, ESG analytics, stock market prediction, advanced modeling for fraud detection and explainable AI, ChatGPT in accounting and education, and sentiment analysis with domain-specific LLMs. Findings show that generative AI enhances predictive capabilities and ESG assessments but raises issues of bias, transparency, and regulation. The review outlines future research priorities around interpretability, multimodal data, and governance frameworks.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Finance Research Letters<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.frl.2025.108797" target="_blank">https://dx.doi.org/10.1016/j.frl.2025.108797</a></p>2025-11-04T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.frl.2025.108797https://figshare.com/articles/journal_contribution/Generative_AI_in_finance_Replicability_methodological_contingencies_and_future_research_directions/31047697CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/310476972025-11-04T09:00:00Z |
| spellingShingle | Generative AI in finance: Replicability, methodological contingencies, and future research directions Hassnian Ali (22330396) Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Artificial intelligence Data management and data science Machine learning Philosophy and religious studies Applied ethics Generative AI Finance Bibliometric Systematic literature review Structural topic modeling |
| status_str | publishedVersion |
| title | Generative AI in finance: Replicability, methodological contingencies, and future research directions |
| title_full | Generative AI in finance: Replicability, methodological contingencies, and future research directions |
| title_fullStr | Generative AI in finance: Replicability, methodological contingencies, and future research directions |
| title_full_unstemmed | Generative AI in finance: Replicability, methodological contingencies, and future research directions |
| title_short | Generative AI in finance: Replicability, methodological contingencies, and future research directions |
| title_sort | Generative AI in finance: Replicability, methodological contingencies, and future research directions |
| topic | Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Artificial intelligence Data management and data science Machine learning Philosophy and religious studies Applied ethics Generative AI Finance Bibliometric Systematic literature review Structural topic modeling |