第十届融城杯金融科技案例评选启动 数字资产、AI-Agent引关注

Core Viewpoint - The focus of digital financial innovation should be on establishing a trustworthy foundation, managing AI-related risks, and ensuring algorithm and scenario alignment to build trust among clients, markets, and governments [1][2]. Group 1: Digital Financial Innovation - Digital financial innovation should not rely solely on vertical models to solve complex problems but should emphasize trustworthiness and security [1]. - The process of digital financial innovation must ensure the safety and reliability of financial assets, transactions, and data [2]. - Key aspects to focus on include high reliability, explainability, legality, and economic viability of AI models in financial institutions [2]. Group 2: Trends in Digital Assets - The rise of stablecoins has become significant, with their proportion in virtual asset market transactions exceeding 90% in recent years [3]. - The asset tokenization market is expected to grow substantially in the next five years, with traditional financial institutions increasingly participating [3]. - The development of virtual currency exchange-traded funds (ETFs) provides investors with a way to engage in the market without directly holding virtual currencies [3]. Group 3: Risk and Opportunity in Digital Assets - The digital asset market presents both risks and opportunities, particularly in the context of domestic policies that are not fully liberalized [4]. - Exploring avenues such as Hong Kong ETFs and compliant alliance chains can help manage risks while seizing innovative trends [4]. Group 4: AI and Financial Services - The application of AI-Agents is expected to revolutionize the self-operated investment and trading capabilities of financial institutions [5]. - Innovations in data security and risk management are being developed, such as the unified security monitoring platform by Taikang Insurance, which has significantly improved risk analysis and prevention capabilities [5]. - The financial industry is seeing upgrades in model risk management through platforms that streamline processes and enhance model interpretability [6].