寿险新业务价值大幅增长 在寿险业务方面，各家险企的中报都突出强调了新业务价值的强劲增长，这显示了行业在转型期的显著成果。 新业务价值（NBV）高速增长。中国平安寿险及健康险业务新业务价值达成223.35亿元，同比大增39.8%。中国人保寿险半年新业务价值在可比口径下同 比增长71.7%。新华保险上半年新业务价值为61.82亿元，同比增长58.4%。中国太保寿险新业务价值同比增长32.3%。这些数据表明，险企正从过去单纯追 求保费规模，转向追求高质量、高价值的业务发展。 投资端表现抢眼 2025年中报显示，各家险企共同构成了行业稳健向好的发展局面。 中国人寿作为行业巨头，总保费达到5250.88亿元，同比增长7.3%，上半年总资产突破7.29万亿元，总投资收益达1275.06亿元，归属于母公司股东的净利 润为409.31亿元，同比增长6.9%，并宣布派发中期现金股利每股0.238元（含税），总计约67.27亿元，彰显其雄厚的资本实力和稳定的盈利能力。 中国平安上半年净利润为680.47亿元，同比下降8.8%，总保费达到5000.76亿元，同比增长1.0%，总资产达13.51万亿元，并宣布派发中期股息每股0.95 ...

Core Viewpoint - The integration of large language models (LLMs) into the banking sector is driving digital transformation, but the inherent opacity of these models presents significant challenges in explainability, necessitating the establishment of a transparent and trustworthy AI application framework to ensure safe and compliant operations [3][4]. Regulatory Constraints on Explainability - Financial regulatory bodies are increasingly emphasizing the need for transparency in AI models, requiring banks to disclose decision-making processes to meet compliance standards and protect consumer rights, which serves as a primary external constraint on LLM applications [6]. - In scenarios like credit approval that directly affect customer rights, algorithmic decisions must provide clear justifications to ensure fairness and accountability. Regulations such as the EU's General Data Protection Regulation (GDPR) mandate transparency in automated decision-making, and domestic regulators also require banks to explain reasons for credit application rejections [7]. - Global regulatory trends are converging towards the necessity for AI model explainability, with frameworks like Singapore's FEAT principles and China's guidelines emphasizing fairness, ethics, accountability, and transparency. The upcoming EU AI Act will impose strict transparency and explainability obligations on high-risk financial AI systems [8]. Technical Explainability Challenges of LLMs - The architecture and operational mechanisms of LLMs inherently limit their technical explainability, as their complex structures and vast parameter counts create a "black box" effect [10]. - The attention mechanism, once thought to provide insights into model behavior, has been shown to have weak correlations with the importance of features in model predictions, undermining its reliability as an explanation tool. The sheer scale of parameters complicates traditional explanation algorithms, making it difficult to analyze high-dimensional models effectively [11]. - The phenomenon of "hallucination," where LLMs generate plausible but factually incorrect content, exacerbates the challenge of explainability. This issue leads to outputs that cannot be traced back to reliable inputs or training data, creating significant risks in financial contexts [12].

Core Viewpoint - The article discusses the recent fluctuations in the RMB/USD exchange rate, highlighting the factors contributing to the RMB's appreciation and the underlying economic conditions that support this trend [2][4][14]. Group 1: Exchange Rate Dynamics - The RMB experienced a series of fluctuations in 2023, initially appreciating in a weak dollar environment, then depreciating due to tariff concerns, before regaining strength [2]. - The RMB's middle price, onshore price, and offshore price have all shown a tendency to converge towards the 7.0 level, indicating a unified market response [2][4]. Group 2: Core Pillars of RMB Valuation - The three core pillars influencing RMB valuation are the China-US interest rate differential, policy risk premium, and purchasing power parity (PPP) [4]. - The narrowing of the China-US interest rate differential has been a fundamental basis for the RMB's appreciation over the past three months, with the nominal interest rate spread decreasing by nearly 50 basis points [4][5]. - The actual interest rate differential has also narrowed, with China's low inflation levels contrasting with rising inflation in the US, enhancing the relative attractiveness of Chinese assets [5][7]. Group 3: Policy Risk and Market Sentiment - The policy risk premium for Chinese assets is decreasing, while it is rising for US assets, driven by concerns over the independence of the US Federal Reserve [7]. - The stability of RMB assets is becoming a rare value in a globally turbulent macroeconomic environment, as China's reforms and policy stability are expected to further reduce the sovereign risk premium [7][11]. Group 4: Purchasing Power Parity - The RMB is currently undervalued against the USD based on purchasing power parity, with the IMF indicating that 1 USD has the purchasing power equivalent to 3.4 RMB [9]. - Long-term undervaluation is attributed to capital account restrictions and international investor concerns regarding China's economic transition [11]. Group 5: Catalysts for RMB Appreciation - The recent strong performance of the RMB is attributed to both internal and external factors, including the central bank's strong midpoint guidance and geopolitical considerations [14][15]. - The influx of foreign capital into the A-share market, driven by a bullish sentiment, has created additional demand for RMB, contributing to its appreciation [19]. - Companies are accelerating their currency conversion from USD to RMB, as the cost of holding USD increases amid anticipated US interest rate cuts [22]. Group 6: Future Outlook - The weak dollar environment is expected to continue supporting RMB appreciation, although challenges such as declining export expectations and the need for domestic demand recovery remain [25].

Core Viewpoint - The August 2025 non-farm payroll data in the U.S. significantly underperformed expectations, reinforcing the anticipation of a Federal Reserve interest rate cut in September, with some institutions predicting a potential cut of 50 basis points [2][3]. Summary by Sections Non-Farm Employment Data - The U.S. Labor Department reported that non-farm employment increased by only 22,000 in August, a substantial decline from the revised 79,000 in July and far below the market expectation of 75,000 [3]. - The unemployment rate rose by 0.1 percentage points to 4.3%, marking a four-year high [3]. Market Reactions - Following the release of the employment data, the U.S. dollar index dropped nearly 0.8%, while spot gold prices surged over 1%, reaching a new historical high of $3,594.76 per ounce [3]. - The weak employment data is attributed to several factors, including job losses in manufacturing due to tariffs, federal government layoffs, and a crisis of trust in data following the dismissal of the former Labor Statistics Bureau chief [3]. Federal Reserve's Policy Implications - The disappointing non-farm data has led to a strong signal for the Federal Reserve to consider rate cuts, with market expectations for a September cut rising to 99% and some predicting a 50 basis point reduction if subsequent inflation data supports it [3]. - The Fed's dual mandate is shifting focus towards employment, as current wage growth is slowing (with hourly wages increasing by 3.7% year-on-year) and labor participation rates are recovering, but demand remains weak, reducing the necessity for rate hikes [3]. Asset Market Impact - The weakening dollar is expected to see the dollar index fall below the critical support level of 98, potentially testing the 96.5-97 range [4]. - U.S. Treasury yields are declining, with the 2-year yield dropping by 11 basis points in a single day, leading to a flight to safe-haven assets [4]. - The stock market is experiencing divergence, with technology stocks benefiting from rate cut expectations, while manufacturing and energy sectors are under pressure [4]. - Emerging markets may find opportunities, with the Chinese yuan appreciating (breaking the 7.15 level) and Hong Kong stocks (Hang Seng Index) potentially benefiting from foreign capital inflows [4]. Economic Concerns - The weak non-farm employment data not only indicates cyclical slowdown but also points to structural risks, with manufacturing and construction sectors continuing to shrink under high interest rates and tariffs [4]. - Government layoffs and a decrease in immigrant labor are further impacting supply, particularly in the construction industry [4]. Upcoming Key Events - On September 9, the annual benchmark revision of non-farm payrolls is expected to be downwardly adjusted by 600,000 to 900,000 jobs, which may further strengthen the case for rate cuts [6]. - The August CPI data will be released on September 11; a decline in inflation would solidify the rationale for rate cuts, while a rebound could lead to market volatility [6]. - The Federal Reserve's meeting on September 16-17 will determine whether the rate cut will be 25 or 50 basis points, depending on the aforementioned data [6]. Conclusion - The recent non-farm data serves as a critical catalyst for the Federal Reserve's policy shift, with a September rate cut now almost certain. However, attention must be paid to the potential discrepancies between policy pace and market expectations, particularly regarding interest-sensitive assets and currency fluctuations [8].

Core Viewpoint - Under the low interest rate environment, brokerage asset management must find its strategic positioning and enhance its ability to serve the real economy while improving active management capabilities to stand out in a competitive market [3][4]. Group 1: Strengthening Service to the Real Economy - Serving the real economy is fundamental for financial institutions and is essential for brokerage asset management to thrive in a low interest rate environment. This can be achieved by accurately identifying positioning, aligning with policy directions, and enhancing connections between resident wealth and the real economy [6]. - Accurate positioning involves focusing on core responsibilities and establishing a long-term strategic direction that prioritizes financial functionality and addresses the financing needs of the real economy [6]. - Emphasizing policy alignment allows brokerage asset management to channel resources into areas that align with national strategies, such as technology finance, green finance, inclusive finance, pension finance, and digital finance [6][7]. - Enhancing connections between resident wealth and the real economy requires a focus on product innovation, resource allocation, and risk management to meet the growing demand for wealth preservation and appreciation among residents [7]. Group 2: Enhancing Research and Investment Capabilities - Research and investment capabilities are the core competitiveness of brokerage asset management and are crucial for active management, product creation, and client service [9]. - Strengthening the research and investment system involves strategic planning, organizational structure optimization, and talent management to balance various factors such as long-term and short-term goals, risk and return, and research and application [9][10]. - Quality assurance in research and investment can be achieved through methodological upgrades, process improvements, and a comprehensive evaluation system that includes accuracy, consistency, and impact [10][11]. - Technological support for research and investment should focus on integrating distributed computing, artificial intelligence, and data science to enhance the efficiency and effectiveness of research processes [11]. Group 3: Improving Asset Allocation Capabilities - Asset allocation is a key strategy for brokerage asset management to navigate the low interest rate environment and the shrinking returns of traditional fixed-income assets [13]. - Optimizing the asset allocation framework involves deepening the research on strategic and tactical asset allocation methods and enhancing the application of quantitative models [13][14]. - Diversifying asset allocation strategies is essential in a competitive market, necessitating a broader range of investment strategies and the establishment of a comprehensive management model for strategy verification and performance evaluation [14].

Core Viewpoint - The digital asset trading industry is a product of the deep integration of blockchain technology and financial innovation, which has become a significant force in reshaping the global economic landscape amid the acceleration of global digitalization [2][4]. Submission Directions - The editorial team of "Tsinghua Financial Review" is inviting submissions on the topic of global digital assets, focusing on providing insights for policymakers, business decision-makers, researchers, and investors [4][13]. - The submission should be original and unpublished, with a suggested word count of 4,000 to 6,000 words, including charts and tables [8][15]. - The deadline for submission is September 20, 2025 [9]. Topics for Discussion - The article outlines 12 specific topics for discussion, including: 1. Development trends and policy comparisons of global central bank digital currencies 2. Exploration of the synergy between RWA (Real World Assets) and central bank digital currencies 3. Redefinition of traditional financial assets by RWA 4. Comparative study of global policies and regulatory frameworks for RWA 5. International trends and legislative dynamics in digital asset regulation 6. Risk identification, assessment, and prevention mechanisms for digital assets 7. The impact and challenges of digital assets on the traditional financial system 8. Legal attributes and compliance pathways for digital assets 9. The role and prospects of digital assets in cross-border payments 10. Construction of investor protection mechanisms in the digital asset field 11. Trends of "de-risking" in digital assets and industry transformation 12. Digital assets and financial stability [7]. Editorial Guidelines - The articles can be analytical or interpretative, aiming to provide practical insights and stimulate thought among readers [13][15]. - The editorial team emphasizes the importance of a solid academic foundation for the viewpoints and conclusions presented, while avoiding overly complex theories and models [14][15].

Group 1 - The article discusses the economic journey of Edmund Phelps, a Nobel laureate, highlighting his contributions to economic theories such as the "natural rate of unemployment" and the "Great Prosperity" theory, which emphasizes innovation and job satisfaction as key drivers of economic vitality [3][4][5] - Phelps's work is positioned as a significant influence on modern macroeconomic thought, with his theories challenging traditional employment and growth models established by earlier economists [4][5] Group 2 - The article introduces "Breaking the Norm: India's Unique Path to Prosperity," authored by Raghuram Rajan and Rohit Lamba, which analyzes India's economic challenges and proposes a new development strategy that prioritizes human capital investment and innovation [8][9] - The authors argue that India must move away from traditional East Asian development models and focus on creating a knowledge-driven economy to navigate global economic changes [9] Group 3 - The article presents "Read, Write, Own: How Blockchain Networks Lead Us into a Smart New Era" by Chris Dixon, which explores the transformative potential of blockchain technology in reshaping the internet and democratizing ownership [13][14] - Dixon outlines the evolution of the internet through three distinct phases, culminating in the current transition to a "Read, Write, Own" era, where blockchain empowers users rather than corporations [13][14]

Group 1 - The article discusses the second National College Financial Popularization Education Debate Competition, highlighting the importance of financial education in universities [2][4]. - The debate topics include sustainable development of financial culture in China, the role of young people in upgrading the silver industry, and the focus of inclusive finance on equal opportunities versus sustainability [6][7][9]. - The competition features various universities, showcasing their arguments on pressing financial issues, indicating a growing interest in financial literacy among students [5][8][10]. Group 2 - The article emphasizes the need for financial institutions to balance economic compensation and risk prevention in insurance [11]. - It also addresses the effectiveness of fiscal interest subsidies compared to market-based loan rates in solving rural financing difficulties [10]. - The future of health insurance in China is debated, focusing on whether it should prioritize inclusivity or innovation [13].

Core Viewpoint - The article discusses the challenges and opportunities for banks in China under a low interest rate environment, emphasizing the need for effective asset-liability management to ensure sustainable and stable operations in the banking sector [4]. Group 1: Positive Changes in Asset-Liability Management - The pricing behavior in the deposit market is gradually becoming more standardized, with the potential for a wider downward adjustment in deposit interest rates. Since the establishment of the market-oriented adjustment mechanism for deposit rates in 2022, banks have regularly lowered the upper limit of deposit rates, leading to a notable decrease in the average company deposit interest rate by 14 basis points to 1.68% in 2024 [6]. - The implementation of self-regulatory mechanisms has introduced measures to standardize deposit market pricing behaviors, such as prohibiting high-interest deposit solicitation and establishing a deposit bidding rate reporting mechanism [6]. Group 2: Challenges in Loan Market - The irrational competition in the loan market is expected to converge, with a potential slowdown in the downward trend of loan interest rates. From December 2019 to March 2025, the 1-year and 5-year LPRs decreased by 105 and 120 basis points, respectively, while the average interest rate on newly issued loans fell by 200 basis points, indicating excessive downward adjustments by banks [7]. - Regulatory authorities are intensifying efforts to manage "involution" in the banking sector, urging banks to set reasonable loan interest rates based on risk pricing principles. This includes ensuring that personal housing and consumer loan rates do not fall below certain thresholds [7].

Core Viewpoint - The article discusses the transition of large models in the financial industry from pilot projects to mass production by 2025, driven by improved regulations, reduced computing costs, and the integration of large models into core business processes, ultimately enhancing competitive advantage [5][20]. Development Path - By 2025, the financial industry is expected to reach a turning point for large model implementation, with regulations and guidelines being established, and GPU rental prices significantly decreasing, making these models accessible to a wider range of institutions [5]. - The consensus among financial institutions has shifted from whether to adopt large models to how to implement them more efficiently and effectively, influenced by the maturation of regulatory frameworks, model capabilities, costs, and ecosystem development [5]. Benchmark Construction - The industry has lacked a rigorous evaluation system tailored to real business scenarios, which has led to the development of benchmarks that convert real business pain points into assessment frameworks, focusing on core capabilities such as numerical calculation and trend prediction [8][9]. - These benchmarks typically include thousands of bilingual samples and assess models across various tasks, ensuring that evaluations reflect real-world applications and capabilities [8]. Practical Applications - Large model technology is deeply integrated into core business scenarios such as investment advisory and research, transforming financial services and enhancing operational efficiency [11]. - Financial intelligent platforms have emerged, capable of supporting millions of daily active users, combining tools, services, and compliance to address core pain points in financial technology innovation [12]. Industry Empowerment - The integration of large models is expected to enhance the quality of investment advisory and research services, addressing inefficiencies and subjective biases inherent in traditional methods [17]. - Smaller financial institutions can leverage standardized services and solutions provided by large models to overcome technological barriers, allowing them to innovate without significant resource investment [19]. Future Outlook - The selection criteria for suppliers are evolving from mere technical delivery to strategic collaboration and demonstrable effectiveness, requiring suppliers to excel in accuracy, compliance, and innovative business model support [21]. - As large model applications continue to evolve, the industry is expected to move towards a more integrated ecosystem, fostering collaboration among regulators, institutions, and investors to build a secure and inclusive financial intelligence environment [24].