Artificial Intelligence Applications and the Impact on Banking Operations
Bibliographic record
Abstract
Artificial Intelligence (AI) has emerged as a powerful force in the banking and financial sectors, reshaping traditional processes and unlocking new opportunities for efficiency and inclusion. However, its adoption is not without challenges, particularly concerning ethical risks, regulatory compliance, and operational limitations. The purpose of this paper is to explore AI in banking applications and its impact on banking operations. First, a literature review examines the multifaceted role of AI in banking, exploring its applications, the ethical risks it poses, and the strategies required to ensure equitable and responsible deployment. Second, we study 39 banks and their AI applications. Third, based on our research, we develop and distribute opinion surveys to students. The survey results show that students believe most jobs eliminated due to AI will be in the lower levels of financial organizations, particularly bank tellers. While AI offers substantial benefits, its success relies on robust governance frameworks, transparent systems, and ongoing efforts to mitigate bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".