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Artificial Intelligence Enhancing Financial Regulatory Compliance Through RegTech Governance Applications Wisnu Wira Atmadja Effendi; Maswanto Maswanto; Ma’mun Murod; Isabella Maria
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 7 No 2 (2026): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v7i2.716

Abstract

The increasing complexity of global financial regulatory frameworks poses significant challenges for financial institutions in ensuring compliance, managing risk, and maintaining operational efficiency, particularly in environments characterized by frequent regulatory updates and cross-jurisdictional requirements. This study aims to examine how Artificial Intelligence (AI) can be effectively applied to navigate complex regulatory frameworks by enhancing regulatory interpretation, compliance monitoring, and decision-making processes. A conceptual and analytical approach was adopted, combining a systematic review of recent Regulatory Technology (RegTech) literature with an analysis of AI techniques, including machine learning, natural language processing, and automated reasoning, as applied to regulatory compliance scenarios. Qualitative insights and illustrative use cases were employed to evaluate alignment between AI capabilities and regulatory demands in areas such as regulatory reporting, risk assessment, and anomaly detection. The findings indicate that AI-based systems have the potential to improve the accuracy, speed, and adaptability of compliance processes by supporting automated regulatory interpretation, identifying potential compliance risks, and enhancing monitoring of regulatory changes. When appropriately governed, AI may reduce human error and operational costs while increasing transparency and auditability. The study concludes that AI has strong potential to transform the way financial institutions navigate complex regulatory frameworks, but its effectiveness depends on robust data governance, explainable AI models, and alignment with ethical and legal standards, and it provides strategic insights for regulators and financial institutions seeking responsible adoption of AI in highly regulated financial environments.
Unlocking Success: Human Resource Management for Startupreneur Jaan; Isabella Maria; Mia
Startupreneur Business Digital (SABDA Journal) Vol. 3 No. 1 (2024): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v3i1.505

Abstract

The purpose of this study is to find out how Human Resource Management (SDM) helps startupreneurs succeed. The study takes a qualitative approach, doing a thorough literature assessment of SDM practices pertinent to the startup setting, considering the background that startupreneurs face in managing human resources in a dynamic business environment. The research finds SDM best practices and methods that can support startupreneurs in their long-term success through a thorough examination of academic literature, industry reports, and relevant case studies. The findings of the research emphasize how crucial it is to choose and recruit carefully to draw in the greatest candidates, foster an innovative culture within the company, implement adaptive performance management, and produce creative leaders. Startupreneurs can enhance their business sustainability, promote growth, and optimize organizational performance by proficiently grasping and employing these SDM approaches. To sum up, SDM management is not just an administrative duty; it is also essential for startup entrepreneurs to overcome obstacles and thrive in a cutthroat industry.