Credit analysis has faced significant limitations in the context of digital transformation, where traditional models based on retrospective financial indicators can no longer adequately meet modern market demands alone. Conversely, contemporary digital models, despite their technological sophistication, still often lack transparency and raise ethical and regulatory concerns. This paper theoretically develops a hybrid creditworthiness assessment model that combines the reliability of traditional indicators with the adaptability of digital tools. The model incorporates explainable artificial intelligence (XAI), real-time and unstructured data analysis, and visual risk dashboards. The methodological approach is descriptive and theoretical, involving comparative analysis and normative evaluation. Findings suggest the hybrid creditworthiness assessment model can meet the requirements of accuracy, speed, transparency, and adaptability. The research provides theoretical value in redefining credit analysis frameworks and practical relevance in developing digital credit assessment solutions, particularly for sectors operating outside the traditional financial structure.
Čavlin et al. (Sun,) studied this question.