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All Journal JURNAL ILMU EKONOMI & SOSIAL Musamus Journal of Business & Management Musamus Accounting Journal International Journal of Science and Society (IJSOC) Parta: Jurnal Pengabdian Kepada Masyarakat Journal of International Conference Proceedings Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi)
Yumiad Fernando Richard
Universitas Musamus
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences Other

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Artificial Intelligence Based Financial Digital Twin Framework for Credit Risk Monitoring in Banking Systems Ni Luh Putu Nita Yulianti; Yumiad Fernando Richard; Apolinaris S Awotkay; Kania Miftahul Jannah
Journal of International Conference Proceedings Vol 9, No 2 (2026): 2026 Vietnam ICPM Proceeding
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v9i3.4758

Abstract

Artificial Intelligence (AI) and Financial Digital Twin (FDT) are emerging technologies with significant potential to enhance the effectiveness of credit risk monitoring in modern banking systems. This study aims to analyze research developments related to the application of AI and FDT in credit risk monitoring and to develop a conceptual framework for an AI-based Financial Digital Twin in banking systems. The study employs the Systematic Literature Review (SLR) method based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Research data were obtained from 20 relevant scientific articles addressing AI, machine learning, XAI, digital twins, and banking risk management. The findings indicate that AI has become a key technology in credit risk assessment through the implementation of machine learning, deep learning, and predictive analytics, which improve the accuracy of credit risk prediction. Furthermore, XAI contributes to enhancing the transparency and interpretability of AI models in credit decision-making processes. This study proposes a conceptual framework consisting of a data input layer, AI analytics layer, Financial Digital Twin layer, predictive risk system, early warning system, and decision support system to support more adaptive, transparent, and proactive credit risk monitoring practices.Kata kunci: Artificial Intelligence; Financial Digital Twin; Credit Risk; Banking; Explainable Artificial Intelligence
Co-Authors Adi Maulana Rachman Apolinaris S Awotkay Apolinaris S. Awotkay Caecilia Henny Setya Wati Herry Hendri Fernando Mote Irene Ipijei Kania Miftahul Jannah Maria Kaok Maria Natalia Wainip Epin Mensy Otelyo Kastanya Ni Luh Putu Nita Yulianti Samel W. Ririhena