Beyond the hype: How can the financial industry benefit from artificial intelligence?
Bibliographic record
Abstract
Artificial intelligence (AI) has been in the spotlight for several years now and even remains so during the difficult COVID-19 period. There has been a lot of AI hype and a lot of AI-related ‘buzz-phrases’ such as ‘data is the new oil’, fueled by our imagination. In practice, nowadays, AI is more related to machine learning (ML). In this paper, we will review the different approaches that defines ML to have a better sense of the concrete strengths and weaknesses of each. In particular, we will tackle some misconceptions, which involve some mismatches between expectations and practical results. In addition, we will also see how some breakthroughs from academics, such as beating the Go world champion, could translate in applications for the financial industry. Finally, we will see the implications for business and organisation, beyond technical aspects. Indeed, the AI hype tends to create a lot of noise, making harder for decision makers and executives to distinguish noise from actionable signals. For instance, change of organisation culture is perhaps the most important for successfully deploying ML systems in real use cases. It requires changing many things from recognising the value from data from existing processes; increasing the organisation’s risk appetite; creating new teams and fostering internal and external collaborations. As an example, we will review the strategy from Quebec’s Autorité des Marchés Financiers (AMF) for implementing these changes. Beyond a specific organisation, the whole financial industry could benefit from this new wave of innovative technologies as other sectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".