The increasing intensity and frequency of natural disasters, amplified by climate change, are profoundly reshaping the risk profile of financial institutions. A growing body of empirical evidence shows that events such as hurricanes, floods, droughts, and earthquakes deteriorate loan portfolios,
\nincrease default probabilities, and weaken banks\u2019 ability to maintain adequate capital buffers (IPCC, 2023; NGFS, 2024; Bitar et al., 2022). Studies from diverse contexts \u2014 including rural China (Li et al., 2021), India (Singh et al., 2020), European microfinance (Di Bella, 2025), U.S. banks exposed to
\nhurricanes (Brei et al., 2019; Cort\u00e9s & Strahan, 2017), and seismic events in Italy (Banca d\u2019Italia, 2023) \u2014 consistently highlight the emergence of nonlinear, geographically asymmetric mechanisms that traditional econometric models struggle to capture. Such models typically rely on equilibrium
\nassumptions and representative agents, making them inadequate for modelling behavioural adaptation, credit contagion, and endogenous feedback loops (Colander et al., 2009; Farmer & Foley, 2009; Lux, 2009). To address these limitations, this study introduces an agent-based model (ABM) developed to
\nsimulate bank solvency under physical climate shocks, integrating NGFS scenarios and an empirical calibration grounded in both micro- and macro-financial evidence. The model architecture consists of four interconnected modules: (i) households and firms, characterized by climate vulnerability,
\nincome dynamics, and adaptive default behaviour; (ii) banks, featuring regulatory capital, provisioning rules, and endogenous credit rationing; (iii) the climate environment, generating physical shocks based on distributions aligned with NGFS (2024); and (iv) the regulator, equipped
\nwith dynamic macroprudential tools and mitigation instruments (moratoria, countercyclical buffers). Calibration relies on elasticity estimates and empirical relationships drawn from key contributions: post-hurricane capital deterioration in Florida (Brei et al., 2019), rural credit sensitivity to climate
\nshocks in China (Li et al., 2021), liquidity contraction in India (Singh et al., 2020), climate-induced microfinance vulnerability (Di Bella, 2025), seismic impacts on credit and deposits in Italy (Banca d\u2019Italia, 2023), and the most comprehensive cross-country analysis available, covering over 9,000
\nbanks (Bitar et al., 2022).<\/p>\n
Simulation results highlight three core dynamics: (1) a nonlinear relationship between shock severity and default probability; (2) substantial deterioration of capital ratios, with amplified effects under multiple shocks; and (3) emergent forms of endogenous credit rationing. Furthermore, the model
\nshows that adaptive prudential instruments \u2014 such as climate-sensitive capital requirements, territorially differentiated risk weights, and temporary moratoria \u2014 can mitigate systemic fragility. Overall, the model provides a computational platform for intelligent climate stress testing, cognitive
\nsimulation applied to financial stability, and regulatory assessment based on complex adaptive system modelling. This aligns closely with the conference\u2019s thematic areas on AI-driven modelling, agentbased cognition, and complex adaptive systems.<\/p>\n","protected":false},"featured_media":5117,"template":"","meta":{"_acf_changed":false},"schedule":[],"speaker-category":[],"class_list":["post-5116","speaker","type-speaker","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\n