AI-Driven Governance Systems for Proactive Regulatory Compliance and Fraud Risk Management in Financial Service Environments
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) into regulatory compliance frameworks has transformed the financial services sector by enabling more adaptive, predictive, and proactive governance systems. This review examines the current landscape of AI-driven regulatory technologies (RegTech), emphasizing how machine learning, natural language processing, and anomaly detection algorithms are being leveraged to monitor compliance, assess risk, and prevent fraud in real-time. The paper explores the evolution of regulatory requirements, such as Basel III, GDPR, and AML directives, and evaluates how AI tools can streamline compliance reporting and enhance audit readiness. It also assesses the challenges of algorithmic accountability, regulatory uncertainty, data privacy, and explainability in deploying AI for compliance management. Case studies from leading financial institutions and fintech firms illustrate practical applications and emerging best practices. This study concludes by identifying strategic frameworks that integrate AI ethics, legal compliance, and real-time fraud analytics to support resilient and transparent financial ecosystems.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".