Are Artificial Intelligence and Machine Learning Shaping a New Risk Management Approach?
Bibliographic record
Abstract
Digital revolution is influencing many economic sectors and for a few years banking sector is under a great transformation mainly due to the development and the use of new technologies. The most recent ones are artificial intelligence (AI) with the recourse to advanced algorithms. The main banking services, their offer, but above all, the customer relations have been significantly influenced by the this. The recourse to new channels, the monitoring of risks and the controls of frauds are only some of the applications of machine learning (ML). To manage the increase in financial and non-financial risks AI and ML seem to give a great help to banks. The survey conducted from December 2022 to May 2023 with a sample of Italian banks of different size, shows the level of awareness in the recourse to these technologies. Moreover, it aims to assess the maturity and the future perspectives in the adoption of AI in the financial system. The analysis is divided into different investigation areas that show how banks can mitigate the risks involved with the implementation of AI and how it affects the risk management process. The paper covers the gap in literature where AI and ML are mainly considered as separate tools to face specific banking projects; and Italian banks, even if with differences due to the size, are aware of the relevance of these new technologies. The research is a contribute to the discussion about the application of AI and ML in a holistic dimension.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".