The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance
Abstract
Abstract Artificial intelligence (AI) is receiving increasing attention in business and society. In banking, the first applications of AI were successful; however, AI is mainly applied in investment banking and backend services without customer contact. AI in commercial banking with its focus on customer interaction has received little attention so far. Introducing AI in commercial banking could change business processes and interactions with customers, which could create research opportunities for behavioral finance. Based on this research gap, we conducted a structured literature review to identify applications of AI in commercial banks and the challenges of implementing AI. Our findings suggest that by using AI, commercial banks can reduce losses in lending, increase security in processing payments, automate compliance-related work, and improve customer targeting. Researchers worry about realizing technological advantages; the embedding of AI in business processes; ensuring user acceptance through transparency; privacy; and suitable documentation. Finally, we propose a research agenda for behavioral finance.
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Cites
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
Conducting a literature review
Rowley, J., & Slack, F. (2004) · Management Research News · doi:10.1108/01409170410784185 · Cited by 885
An Intimate Portrait of the Individual Investor
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(2002) · MIS Quarterly · doi:10.5555/2017160.2017162 · Cited by 179
Cited by
Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021) · IEEE Access · doi:10.1109/access.2021.3086230 · Cited by 209
An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI
Chan, C. K. Y., & Zhou, W. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00284-4 · Cited by 192
Adoption Factors of FinTech: Evidence from an Emerging Economy Country-Wide Representative Sample
Mahmud, K., Joarder, M. M. A., & Muheymin-Us-Sakib, K. (2022) · International Journal of Financial Studies · doi:10.3390/ijfs11010009 · Cited by 68
AI Documentation: A path to accountability
Königstorfer, F., & Thalmann, S. (2022) · Journal of Responsible Technology · doi:10.1016/j.jrt.2022.100043 · Cited by 55
Self-citationPathak, A., & Bansal, V. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100094 · Cited by 40
Metadata
- Authors
- Königstorfer, F., & Thalmann, S.
- Venue
- Journal of Behavioral and Experimental Finance
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 27
- Pages
- 100352
- ISSN
- 2214-6350 (ISSN-L)
- OpenAlex
- W3036262830
- Cited by
- 297
- References
- 5
- FWCI
- 43.73
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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