Generative AI Solutions to Empower Financial Firms
Bibliographic record
Abstract
The advent of generative AI (GenAI) has caused consternation across the industrial landscape. The financial industry is no exception. The scramble to find GenAI solutions in the financial industry has led to a proliferation in the academic and practitioner literature on the subject. However, the field of knowledge remains scattered. The authors offer four deliverables. First, using a survey of the literature and interviews of managers in financial firms, they create a funnel-shaped, two-stage framework of how GenAI can empower financial businesses. The top stage comprises seven GenAI value propositions for financial firms, condensed into the EMPOWER acronym. The bottom stage includes three functions for each proposition. Second, the authors propose ten novel GenAI-based applications spanning the five verticals of financial services, thus extending the current industrial focus of GenAI applications. Third, they outline the benefits and risks of these GenAI applications, visualizing them in a benefit–risk matrix to assist financial managers in prioritizing these applications. Fourth, they propose research questions to guide academic research and policy making at the intersection of GenAI and finance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".