Jul 2026· Global Leadership Organizational Research in Management· Vol 4, pp. 41-68· 0 citations· 44 references
TL;DR
This study recommends strengthening AI governance based on the principles of privacy by design, data minimization, and accountability, supported by the harmonization of national regulations with international standards, by examining regulatory frameworks, data governance, and information security.
Abstract
The use of Artificial Intelligence (AI) in the banking sector enhances operational efficiency, the accuracy of decision-making, and the quality of customer services. However, AI’s reliance on large-scale data processing creates risks to personal data protection and privacy. This study analyses policies governing the implementation of AI in the banking sector by examining regulatory frameworks, data governance, and information security. It employs a qualitative method with a juridical-normative approach through an analysis of laws and regulations, financial-sector policies, and AI implementation practices in banking. The findings reveal a gap between the existing legal framework and its implementation. Although Indonesia has enacted the Personal Data Protection Law and sectoral regulations issued by the Financial Services Authority and Bank Indonesia, transparency, accountability, and AI risk controls have not been adequately implemented. The primary risks include excessive data collection, the use of data beyond its original purpose, algorithmic bias, and violations of data subjects’ rights in automated decision-making systems. Limited human resource capabilities and weak AI governance further increase legal and reputational risks for banking institutions. This study recommends strengthening AI governance based on the principles of privacy by design, data minimization, and accountability, supported by the harmonization of national regulations with international standards. The implementation of AI in the banking sector must balance technological efficiency with the protection of customers’ privacy rights.
Artificial intelligence (AI) has significantly transformed various sectors by enabling extensive collection, processing, and utilization of personal data. While AI offers substantial benefits in improving efficiency, innovation, and public services, it also presents serious legal challenges related to privacy protection, data security, algorithmic transparency, and accountability. This study aims to analyze the legal protection of personal data in the era of artificial intelligence and examine the challenges and regulatory prospects in Indonesia. The research employs a normative legal research method using statutory, conceptual, and comparative approaches. Data were collected through a literature review of laws, regulations, academic journals, books, and other relevant legal documents concerning personal data protection and artificial intelligence. The findings indicate that although Indonesia has enacted Law Number 27 of 2022 concerning Personal Data Protection, several legal gaps remain regarding the governance of AI-driven data processing, automated decision-making, and oversight mechanisms. Comparative analysis with international regulatory developments also demonstrates the need for more comprehensive legal frameworks. This study concludes that strengthening legal policies, harmonizing sectoral regulations, and developing AI-specific governance principles are essential to ensure effective personal data protection while supporting responsible technological innovation in Indonesia.
Muhammad Farhan Abdullah· Jurnal Hukum, Administrasi P...· 0 citations
Artificial intelligence (AI) is rapidly reshaping public administration worldwide. In the United States, AI-enabled systems support automation, predictive analytics, fraud detection, and data-driven policymaking while simultaneously raising ethical concerns related to fairness, transparency, privacy, accountability, and discrimination. This article analyzes U.S. federal frameworks for ethical AI governance, including the Blueprint for an AI Bill of Rights, the NIST (National Institute of Standards and Technology) AI Risk Management Framework, and recent Executive Orders. Their strengths, limitations, and institutional implications are assessed in comparison with global approaches. Building on this review, the article proposes a context-adapted ethical AI governance model for the Republic of Armenia. Key recommendations include the adoption of a national AI strategy, establishment of oversight institutions, improvement of data-quality systems, strengthening procurement integrity, and embedding AI ethics in civil service reforms. The paper concludes that Armenia can leverage U.S. and global best practices to develop a transparent,
accountable, and citizen-centered AI ecosystem.
Harutyun Aleksanyan· Journal of US-China Public A...· 0 citations
Introduction. The digital transformation of public finance is increasing the role of artificial intelligence (AI) in government auditing, financial monitoring, data management, and risk forecasting. At the same time, the active implementation of AI in public and financial institutions is accompanied not only by potential economic benefits, but also by new regulatory, operational, and algorithmic risks associated with data quality, cybersecurity, algorithmic bias, false positives, and transparency of automated decision-making. This creates the need for a comprehensive scientific analysis of international AI governance models and approaches to their adaptation in Ukraine under conditions of digital transformation and geopolitical instability.
The purpose of the article is to systematize international experience in the application of AI in public and financial institutions, conduct a comparative analysis of U.S. and EU approaches to AI governance, and substantiate approaches to the development of an adaptive AI regulatory model for Ukraine’s public finance system.
Results. The study demonstrates that AI is gradually being integrated into key public finance functions, including government auditing, financial monitoring, transaction analysis, budget data management, and risk forecasting. AI implementation contributes to faster financial data processing, automation of control procedures, and optimization of selected managerial decisions. At the same time, significant differences between the U.S. innovation-oriented model and the EU regulation-oriented model based on risk-oriented governance and algorithmic accountability principles are identified.
The study substantiates that alongside the positive effects of AI implementation, new systemic risks emerge, including operational risk, model risk, algorithmic bias, and false-positive blocking of legitimate financial operations in AI-driven financial monitoring systems. The concept of algorithmic financial disruption is proposed as a separate analytical direction for studying digital finance risks associated with the potential impact of algorithmic errors on public finance stability, budgetary flows, and national financial security.
Conclusions. The study substantiates the feasibility of developing a hybrid AI governance model combining innovation capacity with regulatory oversight, algorithmic accountability, and human oversight mechanisms. For Ukraine, strategic importance is attached to harmonization with EU approaches to AI regulation and virtual assets regulation, development of digital financial monitoring systems, and protection of critical public financial flows. The results may be used in shaping public policy in the fields of digital finance, AI governance, and financial security.
The increasing use of Artificial Intelligence (AI) in corporate tax administration has introduced a set of unique challenges. As governments look towards AI-based technologies, machine learning systems, automated risk assessment tools, data analytics, and algorithmic decision-making mechanisms to address the problem of tax evasion, identify areas of non-compliance, and enhance tax collection efficiency, there arise serious questions about how to ensure legal accountability while maintaining the effectiveness of such initiatives.With the ever-increasing sophistication of corporate taxpayer structures and the corresponding rise in their data-intensity, AI offers tax authorities a powerful means at their disposal to uncover anomalies within financial flows and mitigate against related risks. But where this technology begins to replace more traditional forms of manual audit processes – especially where algorithms are used to make decisions regarding taxpayer status and obligations – important issues around transparency, accountability and the nature of taxpayer rights become paramount considerations.This paper explores the implications arising out of the growing involvement of artificial intelligence as part of tax administration strategies and identifies several ways forward to deal with some of the resulting complexities. These include advocating for increased transparency in algorithmic processes, implementing mechanisms for providing explanations, introducing independent auditing of AI systems, ensuring adequate levels of data protection, and assigning clearly defined responsibilities for AI-assisted decisions. A key focus throughout the discussion is on achieving balance between the use of cutting-edge technologies and the need for upholding basic legal rights.
Jaya Priya S and Dr. Sreeja BG· International Journal of Adv...· 0 citations
Artificial intelligence (AI) and in particular generative AI (GenAI) has accelerated data risk in financial services by changing how data is accessed, transformed, and used to drive decisions that regulators closely scrutinise. The pace of AI adoption has outrun many organisations’ data control foundations: AI tools frequently require broad access to data systems, deepen reliance on third-party vendors, and create new pathways through which sensitive data can leak, via prompts, model outputs, automated retrieval processes, and AI-driven actions. At the same time, regulatory expectations for data accuracy, completeness, timeliness, and traceability remain uncompromising, particularly for high-stakes use cases such as capital and liquidity management, regulatory and financial reporting, and financial crime detection. This paper proposes a practical, audit-ready approach to governing data risks in the AI era. The central idea is to add a second lens to traditional data classification, one focused on business consequence rather than confidentiality alone. Specifically, it introduces the concept of critical data elements (CDEs): data elements whose inaccuracy, unavailability, or misuse can produce material regulatory, financial, or customer-facing impact. Pairing CDE designation with conventional confidentiality classifications creates a dual-axis model that directs the strongest controls to the highest-consequence data, even when that data may not appear sensitive on the surface. Drawing on established regulatory frameworks including BCBS 239 (risk data aggregation and reporting), SR 26-2 (model risk management, the interagency guidance issued jointly by the Federal Reserve, Office of the Comptroller of the Currency (OCC), and Federal Deposit Insurance Corporation (FDIC) in April 2026, superseding SR 11-7), and US interagency third-party risk guidance, the paper explains why AI amplifies data risk across four dimensions (privacy, security, integrity, and accountability), and presents an eight-domain data governance framework with concrete audit evidence examples. The goal is to equip compliance, risk, and audit leaders with a repeatable structure for demonstrating that AI innovation rests on controlled, auditable data foundations. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com.business/.
Xin-Fa Tu· Journal of financial complia...· 0 citations
This study examines the extent to which artificial intelligence (AI) applications contribute to reducing accounting errors in bank financial statements, with a focus on the obstacles facing their adoption in accounting practice. Using a descriptive-analytical approach, a structured questionnaire was distributed to 100 preparers and auditors of financial statements in the Iraqi banking sector, yielding 91 valid responses for analysis. The findings indicate that AI applications can detect discrepancies between the methods used to prepare banking financial statements and standard or cross-country practices, and can identify the party responsible for an accounting error, regardless of its size. However, the results also show that AI applications lack the discretionary judgement that human auditors apply in assessing the materiality of an error and its broader implications for banking operations. The study recommends that preparers and auditors of bank financial statements leverage AI's error-detection capabilities, irrespective of error size, to improve the accuracy and reliability of financial statements in the banking sector. The findings contribute to the growing literature on AI-enabled auditing and offer practical implications for bank management, financial statement preparers, and regulators in emerging banking markets.
Ali H. N. BniLam· International journal of bus...· 0 citations
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