Adoption of Explainable Artificial Intelligence in Decision Support Systems under Complex Data Environments - Adam, Hikmah Adwin; Lopulalan, Pierre Marcello; Husna, Meryatul (Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai, 2026)
The increasing complexity of data in the digital banking industry in Indonesia has encouraged the use of Decision Support Systems (DSS) based on Artificial Intelligence (AI), especially in the credit scoring process. However, the black-box characteristics of conventional AI models pose problems related to transparency, trust, and regulatory compliance, especially in credit decision-making that has a direct impact on customers. This study aims to analyze the adoption of Explainable Artificial Intelligence (XAI) in DSS in credit scoring systems in Indonesian digital banking, with an emphasis on how XAI can improve model interpretability in complex and heterogeneous data environments. The research approach used is mixed-methods, which combines quantitative analysis of the performance of the XAI model with a qualitative study through case studies on digital banking institutions. The results show that the implementation of XAI is able to improve the clarity of credit decisions without sacrificing predictive accuracy significantly, especially through the use of post-hoc explainability techniques and hybrid models. In addition, organizational readiness, compliance with Financial Services Authority (OJK) regulations, and data governance maturity are the main determinants in the success of XAI adoption. This research contributes theoretically through the development of a conceptual framework that integrates the dimensions of model clarity, data complexity, and decision quality in the context of digital banking. In practical terms, these findings provide strategic implications for the development of a transparent, accountable, and trust-oriented DSS, thereby supporting fairer and more accountable credit decision-making in Indonesia.