Regulatory Compliance

SoK: Stablecoins for Digital Transformation – Design, Metrics, and Application with Real World Asset Tokenization as a Case Study ArXiv ID: 2508.02403 “View on arXiv” Authors: Luyao Zhang Abstract Stablecoins have become a foundational component of the digital asset ecosystem, with their market capitalization exceeding 230 billion USD as of May 2025. As fiat-referenced and programmable assets, stablecoins provide low-latency, globally interoperable infrastructure for payments, decentralized finance, DeFi, and tokenized commerce. Their accelerated adoption has prompted extensive regulatory engagement, exemplified by the European Union’s Markets in Crypto-assets Regulation, MiCA, the US Guiding and Establishing National Innovation for US Stablecoins Act, GENIUS Act, and Hong Kong’s Stablecoins Bill. Despite this momentum, academic research remains fragmented across economics, law, and computer science, lacking a unified framework for design, evaluation, and application. This study addresses that gap through a multi-method research design. First, it synthesizes cross-disciplinary literature to construct a taxonomy of stablecoin systems based on custodial structure, stabilization mechanism, and governance. Second, it develops a performance evaluation framework tailored to diverse stakeholder needs, supported by an open-source benchmarking pipeline to ensure transparency and reproducibility. Third, a case study on Real World Asset tokenization illustrates how stablecoins operate as programmable monetary infrastructure in cross-border digital systems. By integrating conceptual theory with empirical tools, the paper contributes: a unified taxonomy for stablecoin design; a stakeholder-oriented performance evaluation framework; an empirical case linking stablecoins to sectoral transformation; and reproducible methods and datasets to inform future research. These contributions support the development of trusted, inclusive, and transparent digital monetary infrastructure. ...

FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports ArXiv ID: 2507.01991 “View on arXiv” Authors: Muhammad Bilal Zafar Abstract The proliferation of artificial intelligence (AI) in financial services has prompted growing demand for tools that can systematically detect AI-related disclosures in corporate filings. While prior approaches often rely on keyword expansion or document-level classification, they fall short in granularity, interpretability, and robustness. This study introduces FinAI-BERT, a domain-adapted transformer-based language model designed to classify AI-related content at the sentence level within financial texts. The model was fine-tuned on a manually curated and balanced dataset of 1,586 sentences drawn from 669 annual reports of U.S. banks (2015 to 2023). FinAI-BERT achieved near-perfect classification performance (accuracy of 99.37 percent, F1 score of 0.993), outperforming traditional baselines such as Logistic Regression, Naive Bayes, Random Forest, and XGBoost. Interpretability was ensured through SHAP-based token attribution, while bias analysis and robustness checks confirmed the model’s stability across sentence lengths, adversarial inputs, and temporal samples. Theoretically, the study advances financial NLP by operationalizing fine-grained, theme-specific classification using transformer architectures. Practically, it offers a scalable, transparent solution for analysts, regulators, and scholars seeking to monitor the diffusion and framing of AI across financial institutions. ...

Beyond the Black Box: Interpretability of LLMs in Finance ArXiv ID: 2505.24650 “View on arXiv” Authors: Hariom Tatsat, Ariye Shater Abstract Large Language Models (LLMs) exhibit remarkable capabilities across a spectrum of tasks in financial services, including report generation, chatbots, sentiment analysis, regulatory compliance, investment advisory, financial knowledge retrieval, and summarization. However, their intrinsic complexity and lack of transparency pose significant challenges, especially in the highly regulated financial sector, where interpretability, fairness, and accountability are critical. As far as we are aware, this paper presents the first application in the finance domain of understanding and utilizing the inner workings of LLMs through mechanistic interpretability, addressing the pressing need for transparency and control in AI systems. Mechanistic interpretability is the most intuitive and transparent way to understand LLM behavior by reverse-engineering their internal workings. By dissecting the activations and circuits within these models, it provides insights into how specific features or components influence predictions - making it possible not only to observe but also to modify model behavior. In this paper, we explore the theoretical aspects of mechanistic interpretability and demonstrate its practical relevance through a range of financial use cases and experiments, including applications in trading strategies, sentiment analysis, bias, and hallucination detection. While not yet widely adopted, mechanistic interpretability is expected to become increasingly vital as adoption of LLMs increases. Advanced interpretability tools can ensure AI systems remain ethical, transparent, and aligned with evolving financial regulations. In this paper, we have put special emphasis on how these techniques can help unlock interpretability requirements for regulatory and compliance purposes - addressing both current needs and anticipating future expectations from financial regulators globally. ...

AI in ESG for Financial Institutions: An Industrial Survey ArXiv ID: 2403.05541 “View on arXiv” Authors: Unknown Abstract The burgeoning integration of Artificial Intelligence (AI) into Environmental, Social, and Governance (ESG) initiatives within the financial sector represents a paradigm shift towards more sus-tainable and equitable financial practices. This paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. With the advent of stringent regulatory requirements and heightened stakeholder awareness, financial institutions (FIs) are increasingly compelled to adopt ESG criteria. AI emerges as a pivotal tool in navigating the complex in-terplay of financial activities and sustainability goals. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. Further, we delve into the critical con-siderations surrounding the use of data and the development of models, underscoring the importance of data quality, privacy, and model robustness. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes. Conclusively, our findings suggest that while AI offers transformative potential for ESG in banking, it also poses significant challenges that necessitate careful consideration. The final part of the paper synthesizes the survey’s insights, proposing a forward-looking stance on the adoption of AI in ESG practices. We conclude with recommendations with a reference architecture for future research and development, advocating for a balanced approach that leverages AI’s strengths while mitigating its risks within the ESG domain. ...

Explaining AI in Finance: Past, Present, Prospects ArXiv ID: 2306.02773 “View on arXiv” Authors: Unknown Abstract This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI’s evolution from early statistical methods to sophisticated machine learning, highlighting XAI’s role in popular financial applications. The paper underscores the superior interpretability of methods like Shapley values compared to traditional linear regression in complex financial scenarios. It emphasizes the necessity of further XAI research, given forthcoming EU regulations. The paper demonstrates, through simulations, that XAI enhances trust in AI systems, fostering more responsible decision-making within finance. ...

SOX after Ten Years: A Multidisciplinary Review ArXiv ID: ssrn-2379731 “View on arXiv” Authors: Unknown Abstract We review and assess research findings from 120 papers in accounting, finance, and law to evaluate the impact of the Sarbanes-Oxley Act. We describe significan Keywords: Sarbanes-Oxley Act, corporate disclosure, audit quality, regulatory compliance, accounting standards, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review of existing research on SOX, discussing concepts and evidence without presenting new mathematical models or complex formulas. Its empirical work relies on synthesizing findings from other studies rather than conducting original backtests or data analysis. flowchart TD A["Research Goal:<br>SOX impact after 10 years"] --> B["Methodology:<br>Multidisciplinary Review"] B --> C["Data: 120 Papers<br>(Accounting, Finance, Law)"] C --> D["Process:<br>Review & Assess Findings"] D --> E{"Analysis<br>Focus Areas"} E --> F["Accounting<br>Disclosure Standards"] E --> G["Finance<br>Equities & Market"] E --> H["Law<br>Regulatory Compliance"] F & G & H --> I["Key Outcomes:<br>Audit Quality & SOX Impact"]

SOX after Ten Years: A Multidisciplinary Review ArXiv ID: ssrn-2343108 “View on arXiv” Authors: Unknown Abstract We review and assess research findings from 120 papers in accounting, finance, and law to evaluate the impact of the Sarbanes-Oxley Act. We describe significan Keywords: Sarbanes-Oxley Act, corporate disclosure, audit quality, regulatory compliance, accounting standards, Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review synthesizing findings from over 120 studies, focusing on descriptive analysis, policy implications, and identifying research gaps rather than presenting new mathematical models or complex statistical methodologies. It discusses cost-benefit analysis conceptually but lacks deep statistical modeling, code, or backtesting, resulting in low scores on both axes. flowchart TD A["Research Goal: Assess SOX Impact<br>after 10 Years"] --> B["Data Collection: 120 Papers<br>Finance, Accounting, Law"] B --> C["Multidisciplinary Review"] C --> D{"Evaluate SOX Impact"} D --> E["Audit Quality &<br>Regulatory Compliance"] D --> F["Corporate Disclosure &<br>Accounting Standards"] D --> G["Equities &<br>Market Effects"] E --> H["Key Findings: SOX Improved<br>Credibility & Transparency"] F --> H G --> H H --> I["Outcome: Comprehensive<br>Multidisciplinary Assessment"]