Core Insights - The integration of AI in finance is transitioning from exploration to value validation, focusing on the growth potential AI can bring rather than just its capabilities [1] - AI is becoming essential for financial institutions, shifting from a cost center to a core element of service, marketing, and value creation [1] Group 1: Challenges Faced by Financial Institutions - Leading financial institutions have ample resources and talent but face challenges in leveraging AI for deeper value extraction and innovation [2] - Smaller financial institutions are more focused on immediate business growth challenges due to narrowing net interest margins and increased competition [2][3] Group 2: AI Evolution Paths for Financial Institutions - Regional banks with limited budgets should start with scenario applications and gradually increase investment based on effectiveness [3] - Some banks are upgrading existing mobile applications to AI-enabled platforms to enhance user experience and engagement [3] - The ideal model involves establishing a dedicated team to create a comprehensive knowledge base and data set, leveraging AI for end-to-end business process transformation [3] Group 3: "Pay for Performance" Model - The "pay for performance" model is designed to ensure AI applications deliver tangible business results, addressing concerns of smaller institutions regarding initial investments [6][7] - This model allows institutions to pay based on agreed business outcomes, fostering a collaborative approach to AI implementation [7] Group 4: Data Compliance and AI Model Support - Ant Financial's approach to data compliance involves a two-phase training strategy for AI models, utilizing public data for initial training and proprietary data for further refinement [8] - The company adheres to regulatory requirements by ensuring data flows are compliant and leveraging privacy computing technologies to protect sensitive information [8]