THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE FINANCIAL INDUSTRY: A REVIEW
Bibliographic record
Abstract
Artificial Intelligence (AI) is revolutionizing the financial industry, offering new opportunities for improved efficiency, personalized services, and enhanced decision-making. This review article provides a comprehensive overview of the impact of AI on finance, discussing its applications, challenges, and future prospects. The article explores key areas where AI is making a significant difference, including fraud detection and prevention, risk management, trading and investment, and customer service. It highlights the benefits of AI in each domain, such as real-time anomaly detection, accurate credit risk assessment, algorithmic trading, and personalized financial advice. However, the article also addresses the challenges and considerations associated with AI adoption, including regulatory compliance, data privacy, algorithmic bias, integration with legacy systems, and the talent and skills gap. Looking ahead, the article discusses emerging trends and opportunities, such as the integration of AI with blockchain, AI-driven financial inclusion, and collaborative human-machine partnerships. It also explores potential disruptions to traditional financial roles and business models. The article concludes by emphasizing the need for strategic planning, investment in research and development, and collaboration among stakeholders to harness the full potential of AI in finance while navigating its challenges.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".