<![CDATA[Indonesia's aquaculture industry has substantial economic potential, but it faces considerable credit risk from natural disasters like floods, which lead to high Non-Performing Loans (NPLs). Current methods for assessing credit risk do not adequately consider geographical risk factors. This research addresses this by developing a model to quantify flood-induced credit risk. The model integrates a Spatial Finance approach, Spatial Multi-Criteria Decision Analysis (AHP-GIS), and Monte Carlo risk simulation. Using a case study of flood-related credit losses from 2020 to 2022 in Kampar Regency, Riau, the model effectively maps flood vulnerability zones by weighting geospatial criteria through AHP. Key findings indicate that incorporating spatial factors significantly influences loss predictions. Credit portfolios in high flood risk areas show a maximum estimated loss (Value at Risk - VaR) that is 4.67% higher compared to traditional assessment scenarios. Therefore, this model provides a measurable tool for financial institutions to adjust credit portfolios, implement location-specific risk reduction strategies, and ultimately improve financing stability in the aquaculture sector.]]>
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