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 (AIBPM Publisher, 2026)
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