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Abstract: Recent advances in foundation models have ushered in a paradigm shift across the field of artificial intelligence (AI), with profound implications for financial technology (FinTech). Foundation models refer to large-scale neural networks trained on vast and heterogeneous corpora using self-supervised or instruction-driven objectives, which endow them with strong generalization and transfer capabilities across downstream tasks. Representative classes of such models, including large language models (LLMs), multimodal foundation models, and time-series foundation models, exhibit emergent abilities in semantic understanding, reasoning, and multi-modal representation learning. These capabilities are fundamentally transforming the operational landscape of financial institutions, including how they process information, evaluate risk, design investment strategies, and interact with clients. Collectively, the rise of foundation models signals a transition toward more adaptive, data-centric, and cognitively informed financial intelligence systems, spanning the entire service lifecycle from risk management and quantitative trading to customer advisory and regulatory compliance.