<![CDATA[Purpose: This study examines the conditions and trends of non-performing loans (NPL), identifies the internal and external factors that drive NPL, analyzes the credit restructuring strategies applied, and evaluates their effectiveness at Bank Papua Timika Branch, a regional development bank operating in the resource-rich Mimika Regency of Papua Tengah Province, Indonesia. Research Methodology: A descriptive qualitative design was employed, drawing on in-depth interviews with six purposively selected informants (coded PB1–PB6), comprising a Commercial Relationship Manager, Credit Assistants, Funding Officer, Credit Rescue Assistant, and Credit Administration Officer. The data were supplemented by internal financial documentation from 2017 to 2023. The analysis followed the Miles and Huberman model (data reduction, display, and conclusion drawing), with SWOT and McKinsey 7S frameworks applied to synthesize organizational factors, and a 5C credit analysis framework (Character, Capacity, Capital, Collateral, Condition) applied to debtor-level assessment. Results: NPL remained stable at 0.82–0.84% during 2017–2019, spiked to 1.75% in 2020 due to COVID-19 economic disruption, and then declined progressively to 0.77% by 2023 following restructuring interventions. The non-performing portfolio of IDR 34.344 billion (30 debtors) was concentrated in construction (41%, 12 debtors), MSME (39%, 12 debtors), and mining (20%, six debtors). Capacity impairment and external economic conditions were identified as the dominant drivers of NPLs. Rescheduling, reconditioning, and restructuring achieved recovery rates of approximately 75%, 67%, and 60%, respectively, within 6–36 months after the shock. Approximately 25–30% of debtors remain non-performing due to permanent business deterioration or a lack of cooperative intent. Conclusions: NPL reduction at the Bank Papua Timika Branch requires an integrated strategy combining sector-specific restructuring, proactive 5C-based debtor assessment, McKinsey 7S organizational alignment, and sensitivity analysis-informed risk projection, rather than a generic portfolio-level intervention. Limitations: This study covers a single branch over 2017–2023, and the qualitative design precludes causal inference and formal NPL forecasting models. Contributions: This study contributes an integrated multi-framework evaluation model (5C × McKinsey 7S × sectoral portfolio analysis × sensitivity projection) for NPL management in resource-extraction regional banking contexts, extending credit risk and community banking literature in Eastern Indonesia.]]>
