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Artículos de revistas sobre el tema "AI compliance"

Autor: Grafiati

Publicado: 7 de junio de 2025

Última modificación: 2 de agosto de 2025

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1

Jackson, Andrew Srivathsan. "Compliance to Autonomous Intentions." Compliance to Autonomous Intentions 10, no. 3 (2025): 931–33. https://doi.org/10.38124/ijisrt/25mar1042.

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Artificial Intelligence (AI) is evolving beyond just following instructions. It’s starting to make decisions in ways that resemble human-like intentions. This paper explores the point where AI stops simply following rules and starts acting on its own, reflecting human intelligence in both predictable and unexpected ways. It also looks at how AI can withhold knowledge, form patterns of behavior, and even develop a subconscious-like intelligence. Using real-world examples, we analyze cases where AI has surprised its creators, raising questions about control, governance, and ethics. This pa

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2

Bogucka, Edyta, Marios Constantinides, Sanja Šćepanović, and Daniele Quercia. "Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 168–80. http://dx.doi.org/10.1609/aies.v7i1.31627.

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In the evolving landscape of AI regulation, it is crucial for companies to conduct impact assessments and document their compliance through comprehensive reports. However, current reports lack grounding in regulations and often focus on specific aspects like privacy in relation to AI systems, without addressing the real-world uses of these systems. Moreover, there is no systematic effort to design and evaluate these reports with both AI practitioners and AI compliance experts. To address this gap, we conducted an iterative co-design process with 14 AI practitioners and 6 AI compliance experts

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Oluwatobi Opeyemi Adeyelu, Chinonye Esther Ugochukwu, and Mutiu Alade Shonibare. "AUTOMATING FINANCIAL REGULATORY COMPLIANCE WITH AI: A REVIEW AND APPLICATION SCENARIOS." Finance & Accounting Research Journal 6, no. 4 (2024): 580–601. http://dx.doi.org/10.51594/farj.v6i4.1035.

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This scholarly paper delves into the transformative realm of Artificial Intelligence (AI) in financial regulatory compliance, offering a classical and engaging exploration of its multifaceted impact. Against an increasingly complex financial landscape backdrop, the study aims to unravel the intricacies of AI integration in compliance models, juxtaposing traditional methodologies with cutting-edge AI-driven approaches. The scope of the paper encompasses a systematic literature review and qualitative analysis, focusing on the evolution of AI in financial services, its necessity for enhanced comp

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4

Reddy, Haritha Madhava. "Role of AI in Security Compliance." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem32650.

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Abstract—Artificial Intelligence (AI) has emerged as a pivotal tool in enhancing security compliance across various industries. Its ability to analyze vast datasets, detect intricate patterns, and automate complex processes significantly improves risk management and regulatory adherence. AI enables real-time data analysis, promptly identifying potential violations and flagging security threats, thereby strengthening an organization’s overall security framework. However, while AI offers transformative advantages, its integration into existing security systems introduces new challenges, such as

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5

Bolarinwa Solanke, Femi Bamidele Onita, Obinna Joshua Ochulor, and Henry Oziegbe Iriogbe. "The impact of artificial intelligence on regulatory compliance in the oil and gas industry." International Journal of Science and Technology Research Archive 7, no. 1 (2024): 061–72. http://dx.doi.org/10.53771/ijstra.2024.7.1.0058.

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Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to t

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6

Onoriode Reginald Aziza, Ngozi Samuel Uzougbo, and Munachi Chikodili Ugwu. "The impact of artificial intelligence on regulatory compliance in the oil and gas industry." World Journal of Advanced Research and Reviews 19, no. 3 (2023): 1559–70. http://dx.doi.org/10.30574/wjarr.2023.19.3.1423.

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Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This abstract examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions t

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7

Ebube Victor Emeihe, Ejike Innocent Nwankwo, Mojeed Dayo Ajegbile, Janet Aderonke Olaboye, and Chukwudi Cosmos Maha. "The impact of artificial intelligence on regulatory compliance in the oil and gas industry." International Journal of Life Science Research Archive 7, no. 1 (2024): 028–39. http://dx.doi.org/10.53771/ijlsra.2024.7.1.0059.

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Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to t

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8

Hayes, Kingsley. "Beware the Algorithm: Understanding AI Compliance." ITNOW 64, no. 3 (2022): 58–59. http://dx.doi.org/10.1093/combul/bwac099.

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Abstract The relationship between artificial intelligence (AI) and the General Data Protection Regulation (GDPR) is complex, writes Kingsley Hayes, Partner and Head of Data Breach at Keller Lenkner UK.

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Reddy, Haritha Madhava. "Role of AI in Security Compliance." Journal of Artificial Intelligence & Cloud Computing 3, no. 3 (2024): 1–3. http://dx.doi.org/10.47363/jaicc/2024(3)e194.

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Artificial Intelligence (AI) has emerged as a pivotal tool in enhancing security compliance across various industries. Its ability to analyze vast datasets, detect intricate patterns, and automate complex processes significantly improves risk management and regulatory adherence.

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10

Doshi, Kinil. "Revolutionizing Compliance with Automation and AI." International Journal of Science, Engineering and Technology 11, no. 5 (2023): 1–7. https://doi.org/10.61463/ijset.vol.11.issue5.567.

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Kumar Ramachandran Sumangala, Arun. "Security and Compliance in Healthcare Analytics using AI." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 290–92. http://dx.doi.org/10.21275/sr241104082852.

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12

Adebola, Folorunso, Adewumi Temitope, Adewa Adeola, Okonkwo Roy, and Nathaniel Olawumi Tayo. "Impact of AI on cybersecurity and security compliance." Global Journal of Engineering and Technology Advances 21, no. 1 (2024): 167–84. https://doi.org/10.5281/zenodo.14922540.

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Artificial Intelligence (AI) is transforming the field of cybersecurity and reshaping security compliance practices. As cyber threats become increasingly complex, AI offers powerful tools for identifying, mitigating, and preventing attacks in real-time. AI-driven systems excel at processing vast amounts of data, detecting anomalies, and identifying patterns that traditional security systems might miss. Machine learning and deep learning algorithms enhance the ability to predict potential threats, reducing response time and improving the accuracy of threat detection. These capabilities make AI

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13

Adebola Folorunso, Temitope Adewumi, Adeola Adewa, Roy Okonkwo, and Tayo Nathaniel Olawumi. "Impact of AI on cybersecurity and security compliance." Global Journal of Engineering and Technology Advances 21, no. 1 (2024): 167–84. http://dx.doi.org/10.30574/gjeta.2024.21.1.0193.

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Artificial Intelligence (AI) is transforming the field of cybersecurity and reshaping security compliance practices. As cyber threats become increasingly complex, AI offers powerful tools for identifying, mitigating, and preventing attacks in real-time. AI-driven systems excel at processing vast amounts of data, detecting anomalies, and identifying patterns that traditional security systems might miss. Machine learning and deep learning algorithms enhance the ability to predict potential threats, reducing response time and improving the accuracy of threat detection. These capabilities make AI

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14

Richer, Suzanne M., and Jonathan Canioni. "The role of AI and emerging technologies in global trade compliance." Journal of Supply Chain Management, Logistics and Procurement 7, no. 1 (2024): 34. http://dx.doi.org/10.69554/pstf5284.

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Artificial intelligence (AI) in global trade compliance is evolving rapidly, notably in classification, with the potential to unlock productivity, expand expertise and improve speed, reliability and accuracy. Due to its potential, AI is met with high expectations and sometimes apprehension, as there is a lot of uncertainty about what constitutes AI and how it will affect global trade stakeholders. The aim of this paper is to demonstrate how AI will likely affect customs agencies, importers/exporters, service providers and compliance managers. By using examples and case studies, the paper expla

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15

Hu, Bing. "AI-Driven Global Sanctions Enhancement." Frontiers in Management Science 3, no. 3 (2024): 9–18. http://dx.doi.org/10.56397/fms.2024.06.02.

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This thesis examines the integration of Artificial Intelligence (AI) into global sanctions management, a field currently dominated by manual processes and semi-automated systems that are inefficient and error-prone. With the increasing complexity and volume of international financial transactions, traditional approaches to sanctions compliance are no longer adequate to meet the demands of dynamic global regulations and enforcement challenges. The primary aim of this research is to explore how AI can revolutionize sanctions management by enhancing accuracy, speed, and adaptability in compliance

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16

Manikyala, Aditya, Rajasekhar Reddy Talla, Pavan Kumar Gade, and Satya Surya MKLG Gudimetla Naga Venkata. "Implementing AI in SAP GTS for Symmetric Trade Analytics and Compliance." American Journal of Trade and Policy 11, no. 1 (2024): 31–38. https://doi.org/10.18034/ajtp.v11i1.733.

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SAP Global Trade Services (GTS) uses AI to improve symmetric trade analytics and compliance management. The main goal is to examine how machine learning, natural language processing, and predictive analytics may enhance global trade compliance accuracy, flexibility, and efficiency. Secondary data, including peer-reviewed academic publications, industry reports, and case studies, is analyzed to assess SAP GTS AI integration. Significant results show that AI automates risk assessments, detects abnormalities, and adapts to real-time regulatory changes, improving compliance. AI's symmetric trade a

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17

Nejood Abedyasir ibadi. "Innovative Strategies for Enhancing Cybersecurity in Information Systems: A Holistic Approach in Computer Engineering." Journal of Information Systems Engineering and Management 10, no. 35s (2025): 826–39. https://doi.org/10.52783/jisem.v10i35s.6151.

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The compounded nature of cyber threats, such as ransomware, phishing, and supply chain attacks, has revealed the inadequacy of conventional security controls. AI and machine learning-based solutions offer promising improvements in near-instant threat detection and neutralization, responding to the increasing demand for adaptive cybersecurity measures.This research assesses AI-based cybersecurity models, derives security insights from historical cyber-attacks, measures the effectiveness of regulatory compliance frameworks, and formulates a multi-layered AI-based security strategy. The research

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18

Nanda, Ardhendu Sekhar. "Explainable AI in Regulatory Compliance: Balancing Transparency and Performance in AI Driven Treasury Management." Asian Journal of Research in Computer Science 18, no. 4 (2025): 360–71. https://doi.org/10.9734/ajrcos/2025/v18i4624.

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The integration of Artificial Intelligence (AI) in financial operations has transformed treasury management by enhancing efficiency, risk assessment, and compliance processes. However, the increasing use of AI in regulatory compliance introduces a critical challenge: balancing transparency and performance. Explainable AI (XAI) has emerged as a solution to enhance interpretability, accountability, and trust in AI-driven decision-making. This article explores the role of XAI in regulatory compliance for treasury operations, addressing key challenges and trade-offs between explainability and perf

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Agboola Apooyin. "The impact of artificial intelligence on financial reporting and compliance: Opportunities, challenges, and ethical considerations." International Journal of Science and Research Archive 15, no. 1 (2025): 914–26. https://doi.org/10.30574/ijsra.2025.15.1.1099.

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Artificial Intelligence (AI) is transforming financial reporting and compliance by automating processes, improving accuracy, and enhancing decision-making. AI-driven technologies such as machine learning, natural language processing, and robotic process automation enable real-time data analysis, fraud detection, and regulatory adherence. These innovations reduce human error, streamline operations, and increase transparency in financial reporting. However, AI integration presents challenges, including data security risks, algorithmic bias, and regulatory uncertainties. The lack of standardized

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Sai Nithin, Pathipati. "Precision Build AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43007.

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The "Precision Build AI" project introduces an innovative approach to integrating Artificial Intelligence (AI) into construction design within designated land areas. This initiative aims to enhance the efficiency and accuracy of planning and development processes by leveraging AI technologies. Through AI algorithms, natural language processing (NLP), and virtual reality (VR), the project automates the review of construction plans against complex building and land development codes. The developed software system can process various data formats, such as CAD, BIM files, and PDFs, extracting rele

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21

Hussain, Hasnain, Faisal Ramzan, Amir Hamza Khan, and Saadia Khan. "Examining the Role of AI in Detecting Fraudulent Activities, Managing Financial Risks, and Enhancing Decision-making in Investment Strategies." Review of Applied Management and Social Sciences 8, no. 1 (2025): 207–21. https://doi.org/10.47067/ramss.v8i1.450.

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This study investigates the applicability of AI in improving financial risks, regulatory compliance, and anomalies in financial operations within the banks in the Punjab and Sindh provinces of Pakistan. It examine to what extent the newly developed technologies using AI, mainly machine learning algorithms and predictive analytics, can be of help to risk managers, compliance officers, and internal auditors to do their job properly. A quantitative methodology was adopted and relied on a sample of 160 professionals from around Punjab and Sindh, risk managers and compliance officers, and internal

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22

Oluoha, Oluchukwu Modesta, Abisola Odeshina, Oluwatosin Reis, Friday Okpeke, Verlinda Attipoe, and Omamode Henry Orieno. "Artificial Intelligence Integration in Regulatory Compliance: A Strategic Model for Cybersecurity Enhancement." Journal of Frontiers in Multidisciplinary Research 3, no. 1 (2022): 35–46. https://doi.org/10.54660/.ijfmr.2022.3.1.35-46.

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In the evolving digital landscape, organizations face mounting challenges in maintaining regulatory compliance while safeguarding their systems against increasingly sophisticated cyber threats. This paper proposes a strategic model for integrating Artificial Intelligence (AI) into regulatory compliance frameworks to enhance cybersecurity resilience. The model addresses the limitations of traditional compliance approaches, which often struggle to keep pace with dynamic regulatory changes and emerging threat vectors. By leveraging AI capabilities—such as machine learning, natural language proces

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23

Matvieiev, Petro, Alina Murzanovska, and Olga Ivanchenko. "The legal phenomenon of "complience-service" in commercial activities: international experience and development prospects." Revista Amazonia Investiga 10, no. 45 (2021): 198–207. http://dx.doi.org/10.34069/ai/2021.45.09.20.

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In the context of globalization, the company must meet international standards to do business with foreign partners. For the commercial activity to meet the requirements of legality, ethics, transparency, for you to be considered a reliable counterparty, it is necessary to apply compliance. This concept means that an entity complies with the requirements of the law, rules, and standards of doing business. The purpose of the study was to analyze the peculiarities of the application of compliance from a legal point of view, to take into account foreign experience, to investigate how compliance i

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Haritha, Madhava Reddy. "Building a Compliance Framework for Artificial Intelligence." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 9, no. 6 (2023): 1–4. https://doi.org/10.5281/zenodo.14203699.

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every aspect of modern industry, Artificial intelligence (AI) has permeated nearly enhancing productivity, fostering innovation, and creating new opportunities. However, the unregulated deployment of AI raises ethical, legal, and safety concerns, particularly around privacy, transparency, and bias. As a result, regulatory bodies worldwide are introducing frameworks to govern the responsible development and deployment of AI. This paper explores the components and challenges of building an AI compliance framework, examining governance, risk management, and ethical standards, and highlighting exi

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Joseph Kuba Nembe, Joy Ojonoka Atadoga, Noluthando Zamanjomane Mhlongo, et al. "THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING TAX COMPLIANCE AND FINANCIAL REGULATION." Finance & Accounting Research Journal 6, no. 2 (2024): 241–51. http://dx.doi.org/10.51594/farj.v6i2.822.

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Artificial Intelligence (AI) has emerged as a transformative force in various domains, including tax compliance and financial regulation. This review explores the pivotal role of AI in enhancing these critical aspects of governance and economic stability. In the realm of tax compliance, AI-driven solutions offer unprecedented opportunities for governments to streamline tax administration processes, detect non-compliance, and mitigate tax evasion. Machine learning algorithms can analyze vast volumes of financial data with remarkable speed and accuracy, identifying patterns indicative of tax fra

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Kolawole Joseph Ajiboye. "Ensuring data security and compliance in AI-powered business applications." Global Journal of Engineering and Technology Advances 15, no. 1 (2023): 125–42. https://doi.org/10.30574/gjeta.2023.15.1.0067.

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Artificial intelligence technologies revolutionized how firms make judgments while transforming both data automation and processing throughout company operations. The fast commercial adoption of artificial intelligence technology produced new demanding hurdles for data security coupled with regulatory compliance issues. The vast processing of secret data by AI systems makes them into key targets that cyber attackers and auditors both seek to access. Businesses must supply secure defenses for AI integration operations combined with full compliance standards because operational efficiency requir

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Vikram Singh. "The Impact of Artificial Intelligence on Compliance and Regulatory Reporting." Journal of Electrical Systems 20, no. 11s (2024): 4322–28. https://doi.org/10.52783/jes.8484.

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Abstract: - This research is highly significant for several reasons. Firstly, it addresses the urgent need for modernization in compliance processes. With the increasing volume and complexity of financial transactions, coupled with the rising number of regulations, the traditional manual methods of compliance are becoming increasingly insufficient. AI technologies offer a promising solution to these challenges, enabling financial institutions to manage compliance obligations more effectively, with higher precision and speed. The significance of this work also lies in the growing importance of

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Vennamaneni, Pradeep Rao. "Building Compliance-Driven AI Systems: Navigating IEC 62304 and PCI-DSS Constraints." International journal of networks and security 05, no. 01 (2025): 62–90. https://doi.org/10.55640/ijns-05-01-06.

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Due to the ever-increasing adoption of AI systems in the financial space, it is necessary to assess these regulatory frameworks, such as IEC 62304 and PCI DSS. As AI technologies within the finance sector process huge quantities of data that are sensitive, like transaction and personal information, these must be handled securely so that these are not breached or involve fraud—meeting the strict data security standards, privacy, and operation standards for a medical device software compliance with IEC 62304 and PCI DSS for payment card data security results. This article investigates how these

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Ramesh Pingili. "AI-driven intelligent document processing for banking and finance." International Journal of Management & Entrepreneurship Research 7, no. 2 (2025): 98–109. https://doi.org/10.51594/ijmer.v7i2.1802.

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The banking and finance industry is buried in paperwork—loan applications, compliance reports, risk assessments, and fraud investigations. Manual processing and outdated automation slow operations, increase costs and expose institutions to compliance risks (Vaultedge, 2023). AI-driven Intelligent Document Processing (IDP) is changing this by automating document workflows, accelerating approvals, and enhancing fraud detection. AI-powered IDP integrates machine learning, NLP, and RPA to reduce verification times, reduce errors, and strengthen compliance monitoring. Banks using AI-driven document

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Raveendra Reddy Pasala. "Salesforce data protection and compliance with AI." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 2329–35. https://doi.org/10.30574/wjarr.2024.22.2.0596.

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The data breach and regulatory scrutiny are spreading across the Organization; in such a phase, the Organization that is utilizing Customer Relationship Management (CRM), which is a selling force, faces the challenges of securing the data and compliance. Given the greater prevalence of sensitive customer data being processed, businesses must deploy stringent strategies that protect and keep information in line with strict regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This article will review how Salesforce, data protection, and c

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Indra Reddy Mallela, Sneha Aravind, Vishwasrao Salunkhe, Ojaswin Tharan, Prof.(Dr) Punit Goel, and Dr Satendra Pal Singh. "Explainable AI for Compliance and Regulatory Models." International Journal for Research Publication and Seminar 11, no. 4 (2020): 319–39. http://dx.doi.org/10.36676/jrps.v11.i4.1584.

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The increasing complexity of compliance and regulatory frameworks across industries demands innovative solutions for managing and interpreting large volumes of data. Explainable Artificial Intelligence (XAI) offers a promising approach by providing transparent and interpretable AI models that can be utilized for compliance and regulatory decision-making. Traditional AI systems, often viewed as "black boxes," have been met with scepticism due to their opacity, especially in high-stakes domains like finance, healthcare, and legal sectors, where accountability and trust are paramount. XAI address

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Beatrice Oyinkansola Adelakun. "THE IMPACT OF AI ON INTERNAL AUDITING: TRANSFORMING PRACTICES AND ENSURING COMPLIANCE." Finance & Accounting Research Journal 4, no. 6 (2022): 350–70. http://dx.doi.org/10.51594/farj.v4i6.1316.

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Artificial Intelligence (AI) is revolutionizing internal auditing by transforming traditional practices and enhancing compliance mechanisms. This abstract explores the multifaceted impact of AI on internal auditing, highlighting key advancements in efficiency, accuracy, risk management, and regulatory adherence. AI technologies, particularly machine learning and advanced data analytics, are enhancing the capabilities of internal auditors to analyze large volumes of data swiftly and with greater precision. Traditional internal auditing methods, often constrained by manual processes and sampling

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Mohammed, Anwar. "Elevating Cybersecurity Audits: How AI is Shaping Compliance and Threat Detection." Elevating Cybersecurity Audits: How AI is Shaping Compliance and Threat Detection 2, no. 1 (2023): 1–9. https://doi.org/10.5281/zenodo.14760068.

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The integration of AI into cybersecurity audits is revolutionizing the way organizations approach compliance and threat detection. Traditional audit methods often struggle to keep up with the complexity and scale of modern cyber threats, making the role of AI crucial in enhancing the effectiveness and efficiency of these processes. This paper explores the transformative impact that AI has had on cybersecurity audits by highlighting its capabilities for improving compliance, automating threat detection, and streamlining audit workflows.  By using machine learning, anomaly detection, and pr

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Hariharan Pappil Kothandapani. "Automating financial compliance with AI: A New Era in regulatory technology (RegTech)." International Journal of Science and Research Archive 11, no. 1 (2024): 2646–59. https://doi.org/10.30574/ijsra.2024.11.1.0040.

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Integrating Artificial Intelligence (AI) into the financial sector has revolutionized regulatory compliance, enabling organizations to navigate complex regulatory landscapes with enhanced efficiency and accuracy. This paper explores the transformative role of AI in Regulatory Technology (RegTech), focusing on how machine learning, natural language processing, and predictive analytics are automating critical compliance functions. These technologies empower financial institutions to detect fraudulent activities, monitor transactions, and identify non-compliance with unprecedented precision. By a

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Kalpinagarajarao, GopiKrishna. "Asserting the usage of AI in compliance." Journal of Artificial Intelligence, Machine Learning and Data Science 1, no. 2 (2023): 1495–97. http://dx.doi.org/10.51219/jaimld/gopikrishna-kalpinagarajarao/337.

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Kalpinagarajarao, Gopikrishna. "AI guidelines in healthcare for compliance automation." Journal of Artificial Intelligence, Machine Learning and Data Science 1, no. 4 (2023): 1488–94. http://dx.doi.org/10.51219/jaimld/gopikrishna-kalpinagarajarao/336.

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Akheel, Syed Arham. "AI Governance and Compliance in Regulated Industries." Journal of Artificial Intelligence & Cloud Computing 2, no. 4 (2023): 1–9. https://doi.org/10.47363/jaicc/2023(2)e217.

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Mohammad, Abdul Jabbar. "Predictive Compliance Radar Using Temporal-AI Fusion." International Journal of AI, BigData, Computational and Management Studies 4 (2023): 76–87. https://doi.org/10.63282/3050-9416.ijaibdcms-v4i1p108.

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Ezeife, Enuma. "AI-Driven Tax Technology in the United States: A Business Analytics Framework for Compliance and Efficiency." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 1 (2021): 693–701. https://doi.org/10.54660/.ijmrge.2021.2.1.693-701.

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The integration of Artificial Intelligence (AI) into tax technology is transforming compliance and efficiency in the U.S. tax system. AI-driven business analytics offers a data-driven approach to enhancing tax administration, reducing compliance burdens, and improving fraud detection. This paper presents a business analytics framework that leverages AI technologies such as machine learning, natural language processing (NLP), robotic process automation (RPA), and blockchain to optimize tax compliance and operational efficiency. Machine learning enhances risk assessment by detecting anomalies an

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Grace Annie Chintoh, Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu, and Amazing Hope Ekeh. "Cross-Jurisdictional data privacy compliance in the U.S.: developing a new model for managing AI data across state and federal laws." Gulf Journal of Advance Business Research 3, no. 2 (2025): 537–48. https://doi.org/10.51594/gjabr.v3i2.96.

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The fragmented landscape of data privacy laws in the United States poses significant challenges for organizations utilizing artificial intelligence (AI) systems that process sensitive and large-scale data. Variations in state laws and the absence of a comprehensive federal framework exacerbate compliance complexities, limiting AI innovation and creating legal uncertainties. This paper proposes a conceptual model to harmonize privacy compliance across U.S. jurisdictions, integrating key interoperability principles, consistency, transparency, and scalability. The framework emphasizes standardize

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Naik, Arjun. "Influence of AI in Banking: Ethical and Compliance Implications." Journal of Global Research in Computer Sciences 15, no. 1 (2024): 8. https://doi.org/10.4172/2229-371X.15.2.002.

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Artificial Intelligence (AI) is revolutionizing the banking industry, presenting possibilities for extended efficiency, personalized services and risk control. However, as AI era is increasingly growing into banking operations, ethical troubles and compliance issues are rising as critical concerns in this zone. This article explores the effect of AI in banking, analyzing both positive and negative characteristics. It delves into the ethical and compliance concerns surrounding the integration of AI in financial establishments. We explore how AI is remodeling banking operations, consisting of cu

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42

Sardana, Aman, Swaminathan Sethuraman, and Priya Dharshini Kalyanasundaram. "Compliance-as-Code 2.0: Orchestrating Regulatory Operations with Agentic AI." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 5, no. 1 (2024): 546–63. https://doi.org/10.60087/jaigs.v5i1.366.

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Modern enterprises face escalating challenges in maintaining regulatory compliance amid fragmented standards, evolving cyberthreats, and costly manual audits. This paper introduces Compliance-as-Code 2.0, a novel framework leveraging agentic AI to automate and orchestrate regulatory operations at scale. By embedding autonomous AI agents within a dynamic policy engine, the system translates regulations (e.g., GDPR, PCI-DSS, CCPA) into executable code, enabling real-time monitoring, self-healing controls, and predictive compliance workflows. The framework integrates natural language processing (

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43

Srinivasa, Chakravarthy Seethala. "AI and Data Warehousing for Financial Services: Future-Proofing Data Governance and Compliance." European Journal of Advances in Engineering and Technology 9, no. 1 (2022): 80–82. https://doi.org/10.5281/zenodo.14168910.

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The financial services industry is undergoing a significant transformation driven by the integration of Artificial Intelligence (AI) and advanced data warehousing techniques. This paper examines the impact of AI in advancing data governance and enhancing regulatory compliance in financial services. We explore how AI addresses persistent challenges such as data quality, privacy protection, and real-time regulatory compliance. The paper also presents a detailed framework for implementing AI-powered data governance systems and highlights several use cases demonstrating the advantages of AI in man

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44

Eyinade, Wasiu, Onyinye Jacqueline Ezeilo, and Ibidapo Abiodun Ogundeji. "Strategic AI-Oriented Compliance Optimization Models for FinTechs Operating Across Multi-Jurisdictional Financial Ecosystems." International Journal of Advanced Multidisciplinary Research and Studies 5, no. 4 (2025): 325–35. https://doi.org/10.62225/2583049x.2025.5.4.4592.

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The expansion of FinTech enterprises across international financial ecosystems has introduced complex compliance challenges stemming from heterogeneous regulatory environments. This review investigates the strategic deployment of Artificial Intelligence (AI) to enhance regulatory compliance across jurisdictions, focusing on automation, adaptability, and real-time responsiveness. It explores AI-driven frameworks—including Natural Language Processing (NLP), Machine Learning (ML), and Robotic Process Automation (RPA)—to optimize regulatory interpretation, anti-money laundering (AML) procedures, a

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45

Sysoyev, P. V. "Ethics and AI-Plagiarism in an Academic Environment: Students’ Understanding of Compliance with Author’s Ethics and the Problem of Plagiarism in the Process of Interaction with Generative Artificial Intelligence." Vysshee Obrazovanie v Rossii = Higher Education in Russia 33, no. 2 (2024): 31–53. http://dx.doi.org/10.31992/0869-3617-2024-33-2-31-53.

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Everyday, artificial intelligence (AI) is being increasingly integrated into the teaching and learning process at Russian universities. The high level of quality of feedback from AI tools leads to the spread of AI plagiarism – unauthorized borrowing of generative AI materials – among students. The purpose of this study is to: a) highlight aspects that determine students’ understanding of the issues of compliance with author’s ethics and the problem of plagiarism when interacting with generative AI; b) develop a questionnaire to determine students’ understanding of the issues of compliance with

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46

Tina Akpevben Aror and Munashe Naphtali Mupa. "Risk and compliance paper what role does Artificial Intelligence (AI) play in enhancing risk management practices in corporations?" World Journal of Advanced Research and Reviews 27, no. 1 (2025): 1072–80. https://doi.org/10.30574/wjarr.2025.27.1.2607.

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Artificial Intelligence (AI) is gradually transforming the corporate risk management and compliance landscape, as it allows making decisions in a more timely and correct way. This paper evaluates the new role being played by AI in promoting effective risk management and compliance processes in organizations through the manner in which the AI technologies with the ability to collect, analyze, process, and evaluate information, including machine learning, natural language processing, and predictive analytics, are being applied with the purpose of identifying, evaluating, and mitigating the vario

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47

Gbenga Femi, ASERE, CHRIS-ALOFE Mary Folashade, and ABDULRAHMAN Musa Ali. "STATISTICAL ANALYSIS OF AI SECURITY METRICS AND ORGANIZATIONAL COMPLIANCE WITH DATA PROTECTION STANDARDS." International Journal Of Trendy Research In Engineering And Technology 09, no. 05 (2025): 19–26. https://doi.org/10.54473/ijtret.2025.9503.

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As artificial intelligence (AI) becomes increasingly integrated into cybersecurity systems, assessing its performance in relation to data protection compliance has become a critical area of study. This research investigates the statistical relationship between AI-based security performance metrics and organizational compliance with data protection standards, focusing on frameworks such as the Nigeria Data Protection Regulation (NDPR) and the General Data Protection Regulation (GDPR). Using Canonical Correlation Analysis (CCA), the study examines multivariate data collected from a sample of org

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48

Parapalli, Shriprakashan L. "GMP Compliance and MES: Strategies for Automated Compliance Assurance." International journal of data science and machine learning 05, no. 01 (2025): 170–81. https://doi.org/10.55640/ijdsml-05-01-17.

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Current Good Manufacturing Practice (cGMP) regulations emphasize stringent control over production processes, personnel, equipment, and documentation in pharmaceutical manufacturing. Meeting cGMP requirements involves meticulous recordkeeping, comprehensive quality control, and robust oversight—processes that are prone to human error when relying on traditional, paper-based approaches. Against this backdrop, Manufacturing Execution Systems (MES) offer a powerful solution for managing production workflows and ensuring regulatory adherence. This paper explores the integration of MES in a cGMP en

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Oritsematosan Faith Dudu, Olakunle Babatunde Alao, and Enoch O. Alonge. "Conceptual framework for AI-driven tax compliance in fintech ecosystems." International Journal of Frontiers in Engineering and Technology Research 7, no. 2 (2024): 001–10. http://dx.doi.org/10.53294/ijfetr.2024.7.2.0045.

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This paper comprehensively reviews the integration of Artificial Intelligence (AI) into tax compliance processes within fintech ecosystems. It explores the theoretical foundations of AI technologies, such as machine learning and predictive analytics, and how they can automate tax reporting, auditing, and compliance monitoring. Conceptual models for AI-driven tax compliance are proposed, highlighting the potential for increased efficiency, accuracy, and cost reduction. The paper also examines challenges associated with AI adoption, including algorithmic biases, ethical concerns, data privacy is

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50

Onoja, James Paul, Oladimeji Hamza, Anuoluwapo Collins, Ubamadu Bright Chibunna, Adeoluwa Eweja, and Andrew Ifesinachi Daraojimba. "Digital Transformation and Data Governance: Strategies for Regulatory Compliance and Secure AI-Driven Business Operations." Journal of Frontiers in Multidisciplinary Research 2, no. 1 (2021): 43–55. https://doi.org/10.54660/.ijfmr.2021.2.1.43-55.

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Digital transformation has redefined business operations, driving efficiency, innovation, and competitiveness through artificial intelligence (AI) and advanced analytics. However, the rapid adoption of AI-driven processes introduces significant regulatory and security challenges, necessitating robust data governance frameworks to ensure compliance, mitigate risks, and protect sensitive information. This study explores the intersection of digital transformation and data governance, highlighting strategies for regulatory compliance and secure AI-driven business operations. The paper first examin

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