This study examines the efficiency-enhancing effects and latent risks of embedding artificial intelligence (AI) into financial systems, with a xfocus on implications for sustainable social development. The significant findings indicates that an AI-driven financial decision-making model incorporating explicit sustainability constraints demonstrates considerable analytical value. Evidence from multi-agent simulation experiments indicates that the performance of the AI-based model is systematically compared with traditional rule-based frameworks across multiple dimensions, including capital allocation efficiency, financial inclusion, individual-level risk control, and systemic risk synchronization. The key data suggest that AI significantly improves the allocation of capital toward high-ESG entities, increasing the capital allocation efficiency index from 0.462 to 0.618. Simulation shows financial inclusion rises from 0.38 to 0.57 while default rate declines to 0.089. The important results indicates that financial inclusion, measured by the coverage of small and medium-sized enterprises (SMEs) receiving financing, increases nearly 50 % in access to critical resources. Additionally, the significant evidence suggests that the systemic risk synchronization index increases from 0.31 to 0.43, which indicates the potential accumulation of latent systemic vulnerabilities. The key findings demonstrate that these results highlight a trade-off: AI advances sustainable finance by enhancing efficiency and inclusion. Data show AI may amplify systemic risk. Evidence indicates that achieving sustainable outcomes requires a dynamic balance. The study indicates that technological deployment and institutional constraints appear to remain in essential tension throughout this important process.

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