Optimizing Mortgage Lending Strategies: A Data-Driven Approach to Enhancing Bank BTN’s Non-Subsidized Credit Model

The rising demand for non-subsidized mortgages in Indonesia has intensified competition among banks, necessitating improvements in credit approval efficiency and risk management. This study evaluates the business model of PT Bank Tabungan Negara (Persero) Tbk (Bank BTN) in the non-subsidized mortgage sector using SWOT analysis and the Analytical Hierarchy Process (AHP) to prioritize strategic interventions. The findings highlight decision-making conflicts, weak initial verification processes, and fraud risks as critical weaknesses, with AHP results ranking Credit Decision-Making Integration (0.54) as the most urgent strategic action. Digital transformation (0.52) presents the greatest opportunity, while competition from more efficient banks (0.49) is the most significant external threat. Managerial implications suggest the necessity of process standardization, AI-driven credit risk assessment, and automation in document verification to enhance efficiency and mitigate fraud risks. Benchmarking against leading competitors like BCA and Mandiri underscores the importance of real-time verification and centralized decision-making in reducing non-performing loan (NPL) ratios. The study provides a data-driven roadmap for Bank BTN to enhance its competitiveness, optimize risk management, and improve operational efficiency. However, limitations include the study’s focus on internal process improvements without extensive consideration of external macroeconomic fluctuations and regulatory changes. Future research should incorporate predictive modeling techniques to refine credit evaluations and explore global best practices in mortgage lending.