Compliance-as-a-Service Frameworks Using AI for Real-Time Risk Intelligence in Decentralized Financial Systems
Bibliographic record
Abstract
The rise of decentralized financial systems (DeFi) presents both unprecedented opportunities and critical regulatory challenges, particularly in ensuring compliance, transparency, and risk mitigation in real time. This paper reviews the emerging paradigm of Compliance-as-a-Service (CaaS) frameworks integrated with Artificial Intelligence (AI) to provide scalable, real-time compliance monitoring and risk intelligence across distributed financial ecosystems. It explores how AI technologies such as machine learning, natural language processing, and knowledge graphs enable automated detection of non-compliant behavior, smart contract auditing, fraud detection, and adaptive regulatory reporting. Special attention is given to the architectural design of AI-enabled CaaS platforms, their interoperability with blockchain-based systems, and their capacity to address challenges posed by jurisdictional fragmentation, pseudonymity, and data privacy. Furthermore, the paper examines current implementations, industry trends, and regulatory innovations shaping the future of CaaS in DeFi. The review concludes by identifying research gaps and proposing a roadmap for developing trustworthy, transparent, and explainable CaaS infrastructures to enhance financial integrity and regulatory agility in decentralized environments.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".