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Journal : Multidisciplinary Indonesian Center Journal

THE INFLUENCE OF ARTIFICIAL INTELLIGENCE LITERACY ON LEADERSHIP EFFECTIVENESS IN FINANCE INDUSTRY Yonghwa Han; Nurwulandari, Andini; Hasanudin
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 2 No. 3 (2025): Vol. 2 No. 3 Edisi Juli 2025
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v2i3.959

Abstract

This research investigates the impact of AI literacy on leadership effectiveness within the finance industry, a sector experiencing rapid digital transformation. As artificial intelligence becomes increasingly integrated into financial operations, leaders are required to possess not only traditional managerial skills but also a strong understanding of AI concepts, applications, and ethical considerations. Using a quantitative approach, data were collected from 55 finance professionals occupying leadership roles across various organizations. The study assessed their levels of AI literacy and examined its relationship with their perceived leadership effectiveness. The results reveal a significant positive correlation between AI literacy and leadership effectiveness, indicating that leaders with higher AI literacy are better equipped to drive innovation, make strategic decisions, and successfully adapt to technological change. These findings highlight the importance of integrating AI literacy into leadership development programs and fostering a culture of continuous learning to ensure sustained organizational agility and competitiveness in the finance sector.
ARTIFICIAL INTELLIGENCE IN FINANCIAL RISK MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW ON ENHANCING ORGANIZATIONAL RESILIENCE FOR FUTURE GLOBAL FINANCIAL CRISES Han, Yonghwa; Nurwulandari, Andini; Hasanudin; Wulandari, Aghnia
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 1 (2026): Vol. 3 No. 1 Edisi Januari 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i1.1572

Abstract

This study explores how incorporating artificial intelligence improves institutional resilience and overcomes the rigidity of conventional, data-based methods to alter financial risk management. To find patterns in AI applications, resilience theory, and integration pathways, a qualitative systematic literature review was carried out utilizing theme synthesis in accordance with PRISMA peer-reviewed protocols. Findings show that AI techniques, machine learning for tail-risk detection, deep learning for high-frequency forecasting, and explainable AI for transparent decisions, yield up to 28% reductions in forecasting errors and halve recovery times during crises. The hybrid CNN Transformer architectures and transformer-based NLP models significantly enhance predictive accuracy and forward-looking insights. The study suggests financial institutions adopt integrated AI frameworks, invest in data quality and human–AI collaboration, and implement principle-based governance to balance innovation with fairness and stability. Limitations include reliance on published literature and limited representation of emerging AI models, warranting future longitudinal and context-specific empirical research.