KMAN Publication Inc. AI and Tech in Behavioral and Social Sciences In Press 2026 10 18 Identification and Prioritization of Banking Risks to Inform Liquidity and Credit Stress Testing: A Mixed-Methods Approach Identification and Prioritization of Banking Risks to Inform Liquidity and Credit Stress Testing: A Mixed-Methods Approach EN Journal Article To identify and prioritize banking risks that may inform liquidity and credit stress-test development in banks listed on the Tehran Stock Exchange, and to examine their perceived interrelationships using expert judgments. A sequential exploratory mixed-methods design was adopted within a pragmatic paradigm. Semi-structured interviews with 20 banking experts informed qualitative coding in NVivo; Cohen’s kappa was 0.837. The quantitative phase covered 10 listed banks and 52 banking managers, while the direct-influence matrix represented mean ratings from 15 experts. DEMATEL-based analytic network process (DANP) calculations were performed in Excel. Eight risk categories were identified. Interest rate risk received the highest final weight (0.1634), followed by credit risk (0.1426) and legal and regulatory risk (0.1274). The remaining weights were 0.1151 for liquidity risk, 0.1146 for business risk, 0.1142 for financial risk, 0.1139 for market risk, and 0.1088 for foreign exchange risk. The reported network statistics and final weights describe expert assessments rather than estimated probabilities of bank failure. The analysis identifies interest rate, credit, and legal and regulatory risks as the three highest priorities within an expert-derived network. It provides an exploratory input to stress-test development, rather than a calibrated or validated stress-testing model. Losses, liquidity shortfalls, and resilience under adverse scenarios were not estimated.