香港交易及結算所有限公司及香港聯合交易所有限公司對本公告的內容概不負責，對其準確性或完整性亦不發表任 何聲明，並明確表示，概不對因本公告全部或任何部份內容而產生或因倚賴該等內容而引致的任何損失承擔任何責 任。 中 國 農 業 銀 行 股 份 有 限 公 司 AGRICULTURAL BANK OF CHINA LIMITED （ 於 中 華 人 民 共 和 國 註 冊 成 立 之 股 份 有 限 公 司 ） 於本公告日期，本行的執行董事為谷澍先生、王志恒先生、劉洪先生和林立先生；本行的非執行董事為 周濟女士、李蔚先生、劉曉鵬先生、張奇先生和張洪武先生；本行的獨立非執行董事為吳聯生先生、汪昌雲先生、 鞠建東先生、莊毓敏女士、張琦先生和王沛詩女士。 中国农业银行股份有限公司 （A 股股票代码：601288） （股份代號：1288） 海外監管公告 本公告乃根據香港聯合交易所有限公司證券上市規則第13.10B條作出。 根據中華人民共和國的有關法律法規，中國農業銀行股份有限公司在上海證券交易所網 站刊登《中國農業銀行股份有限公司2025年度報告》。 茲載列該公告如下，僅供參閱。 承董事會命 中國農業銀行股份有限公司 劉 ...

香港交易及結算所有限公司及香港聯合交易所有限公司對本公告的內容概不負責，對其準確性或完整性亦不發表任 何聲明，並明確表示，概不對因本公告全部或任何部份內容而產生或因倚賴該等內容而引致的任何損失承擔任何責 任。 中 國 農 業 銀 行 股 份 有 限 公 司 AGRICULTURAL BANK OF CHINA LIMITED （ 於 中 華 人 民 共 和 國 註 冊 成 立 之 股 份 有 限 公 司 ） （股份代號：1288） 海外監管公告 本公告乃根據香港聯合交易所有限公司證券上市規則第13.10B條作出。 根據中華人民共和國的有關法律法規，中國農業銀行股份有限公司在上海證券交易所網 站刊登《中國農業銀行股份有限公司董事會決議公告》。 茲載列該公告如下，僅供參閱。 承董事會命 中國農業銀行股份有限公司 劉清 公司秘書 中國北京 2026年3月30日 於本公告日期，本行的執行董事為谷澍先生、王志恒先生、劉洪先生和林立先生；本行的非執行董事為 周濟女士、李蔚先生、劉曉鵬先生、張奇先生和張洪武先生；本行的獨立非執行董事為吳聯生先生、汪昌雲先生、 鞠建東先生、莊毓敏女士、張琦先生和王沛詩女士。 证券代码：601 ...

Core Insights - Agricultural Bank of China (ABC) is focusing on wealth management as a key strategy to align with the current trend of light capital transformation in the banking sector, emphasizing sustainable and stable returns as a pathway from scale expansion to value creation [1][2] Group 1: Wealth Management Strategy - ABC's wealth management business is driven by four main factors: value-driven approach centered on customer asset allocation, reform-driven initiatives leveraging a vast customer base and extensive network, organizational enhancements to improve research and service capabilities, and digital transformation utilizing AI and other technologies to enhance service efficiency [1] - The bank's private banking assets under management (AUM) have reached 3.65 trillion yuan, with wealth management product scale exceeding 2.2 trillion yuan, generating over 340 billion yuan in cumulative returns for investors [1] Group 2: Financial Performance - In 2025, ABC's wealth management revenue amounted to 35.7 billion yuan, with fee income reaching 25 billion yuan, showcasing strong resilience, balanced stability, and low volatility in overall business performance, marking a new growth driver for the bank [2]

报告编制说明 报告主体 本报告主体为中国农业银行股份有限公司及其子公司。 编制依据 本报告按照财政部等九部委联合发布的《企业可持续披露 准则——基本准则（试行）》及其应用指南、《企业可持续 披露准则第 1 号——气候（试行）》，上海证券交易所《上 海证券交易所上市公司自律监管指引第 14 号——可持续 发展报告（试行）》、《上海证券交易所上市公司自律监管指 南第 4 号——可持续发展报告编制》，国家金融监督管理总 局（原中国银保监会）《关于加强银行业金融机构社会责任 的意见》，中国银行业协会《中国银行业金融机构企业社 会责任指引》以及香港联合交易所《主板上市规则》附录 C2《环境、社会及管治报告守则》的要求编制。参照中国 人民银行《金融机构可持续信息披露指南（试用稿）》、全 球可持续发展标准委员会（GSSB）《可持续发展报告标准》 （GRI Standards）、联合国可持续发展目标（SDGs）。 报告原则 本报告遵循重要性、可靠性、相关性、可比性、可验证性、 可理解性、相称性、及时性、量化等原则。 重要性：本报告明确区分重要可持续信息与其他信息，综 合考虑本行所处行业和经营业务特点等，对可持续信息进 行适当 ...

Group 1 - The core viewpoint of the article emphasizes that the future differentiation among commercial banks will hinge on their risk management capabilities over the next 2 to 3 years [1] Group 2 - Agricultural Bank's Vice President Lin Li made this statement during the 2025 annual performance release [1]

Report Industry Investment Rating - The report does not mention the industry investment rating [1] Core Viewpoints - Multiple regulatory indicators form the "fence" for banks' allocation behavior, and banks' "choices" within these fences determine their asset allocation structure [1][3][24] Summary by Relevant Catalog 1. Fence Within: How Regulatory Constraints Determine Banks' Bond Market Choices - **"Ballast Stone" Status of Bank Allocation in the Bond Market**: As the main bond allocators in the bond market, commercial banks' "ballast stone" status is rooted in three logics: scale dominance, counter - cyclical characteristics, and stability under regulatory constraints. As of the end of February 2026, commercial banks' bond allocation in the inter - bank market was 82.16 trillion yuan, ranking first among various institutions, mainly investing in interest - rate bonds. Their counter - cyclical allocation provides a buffer for the market, and regulatory constraints make them natural buyers of interest - rate bonds [2][17][18] - **Commercial Bank Regulation: Macro - Prudential + Micro - Constraints**: Understanding banks' bond allocation behavior requires understanding their regulatory constraints, including the Macro - Prudential Assessment System (MPA), interest - rate risk indicators (ΔEVE/NII), liquidity risk indicators (LCR/NSFR), and capital adequacy ratio. These indicators form the "fence" for banks' allocation behavior [3][24] 2. Central Bank MPA: From Broad Credit to Bond Allocation - **Overview of MPA Indicator System**: MPA reshapes banks' bond - allocation behavior in three dimensions: total amount, structure, and timing. In terms of total amount, the broad - credit growth constraint makes bond investment a "regulatory item" after loan issuance. Structurally, capital - adequacy pressure forces banks' self - operated funds to concentrate on interest - rate bonds with zero risk - weight. Temporally, liquidity assessment indicators create a rigid "quarter - end effect". Under these constraints, banks' self - operated bond - allocation behavior shows characteristics of "quota restricted by credit, concentration on interest - rate bonds, and rhythm restricted by quarter - ends" [4][29] - **Three Transmission Paths of MPA on Banks' Bond Allocation**: - **Broad - Credit Growth Constraint → Limited Bond Allocation Quota**: The upper limit of broad - credit growth locks the growth rate of bond investment, squeezing out bond allocation when loan growth is fast, especially at quarter - ends [32][33] - **Capital - Adequacy Constraint → Decreased Risk Appetite + Increased Supply of Capital Instruments**: To meet capital - adequacy requirements, banks issue secondary - capital bonds and perpetual bonds and increase the allocation of low - capital - occupancy interest - rate bonds while reducing high - capital - occupancy credit bonds. In a period of strict capital regulation, the spread between interest - rate bonds and credit bonds tends to widen [34] - **Liquidity Indicator Constraint → Quarter - End Fund Pulse + Solidified Maturity Preference**: LCR assessment tightens the quarter - end capital market and releases concentrated demand for interest - rate bonds. NSFR constraint restricts banks from lending to non - bank institutions at quarter - ends, inhibits excessive maturity mismatch, and solidifies banks' preference for short - term bonds or long - term interest - rate bonds [35] 3. Triple Constraints of the Banking Risk Supervision System under the Financial Regulatory System - **Capital - Adequacy Constraint: Risk Weights Guide Allocation**: Capital - adequacy ratio is the core regulatory indicator. Risk weights determine the capital occupancy of bonds, and banks prefer bonds with lower risk weights. The investment priority of bond types is: treasury bonds, policy - financial bonds > local - government bonds > general - credit bonds, commercial - financial bonds > secondary - capital bonds > perpetual bonds. When capital adequacy is under pressure, banks compress high - weight assets, and the regulatory rating affects business qualifications and asset structure. Capital - supplement pressure increases the supply of capital instruments [37][44][45] - **Liquidity Risk Indicators: LCR and NSFR's "Rigid Demand" for High - Liquidity Assets**: The core goal of liquidity - risk supervision is to guide banks to match the maturity structure of assets and liabilities. LCR and NSFR are the two pillars. Different bonds have different conversion rates in HQLA and RSF coefficients, which affect banks' bond - type preferences. The comprehensive impact includes a significant quarter - end effect, solidified maturity preference, and structural differentiation [47][51][57] - **Interest - Rate Risk Supervision Indicators: How ΔEVE and ΔNII Constrain Allocation Maturity**: ΔEVE measures the maximum loss of the net present value of banks' assets and liabilities under different interest - rate shocks, and ΔNII measures the impact of interest - rate changes on net interest income. These two indicators jointly restrict large domestic banks' long - bond allocation. Banks tend to "buy short and sell long" to control bond maturity [58][59][60]

Core Insights - Agricultural Bank is leveraging artificial intelligence (AI) to enhance its banking services and operations, establishing a dedicated office for smart banking initiatives and focusing on the development of an "AI+" capability system [1][3] Group 1: Achievements in Smart Banking - The bank has accelerated product service innovation, launching new financial products such as "Fei Shou Loan" for drone operators and "Ke Jie Loan" for technology finance, significantly increasing support for agriculture and inclusive finance [1] - As of the end of 2025, the balance of "Nong Yin e Loan" reached 6.8 trillion, representing an 18.7% increase from the previous year [1] - The bank has improved online service quality, maintaining industry-leading monthly active users on its mobile banking platform and launching the "Inclusive e Station 5.0" to enhance online service channels [1] Group 2: Intelligent Risk Control - The bank has strengthened credit risk management through advanced technologies such as GPS positioning and generative AI, enhancing the verification of customer authenticity and improving risk identification [2] - A centralized monitoring system has been developed to optimize risk control models for various customer segments, enabling efficient batch processing of potential risks [2] - The application of AI in anti-fraud and anti-money laundering efforts has shown significant improvement in effectiveness [2] Group 3: AI Empowerment and Efficiency - The bank has deepened the application of generative AI in intelligent customer service, reducing average call duration from 207 seconds to 176 seconds, thereby alleviating the workload of frontline staff [2] - Over 70% of data in investigation and review reports is now generated automatically, significantly reducing manual work for credit personnel [2] - The bank's enterprise-level AI digital assistant "Yi Ming" provides comprehensive support for frontline employees, including intelligent Q&A and marketing assistance [2] Group 4: Foundation for AI Applications - Agricultural Bank is systematically advancing AI applications by creating an AI application blueprint and capability map, establishing the "Nong Yin Zhi +" platform, and deploying multiple mainstream large models [3] - The bank is building a robust model matrix that covers various types and sizes, providing strong support for the large-scale application of AI [3] - Future plans include continuing the implementation of smart banking initiatives to enhance the precision, convenience, and inclusiveness of financial services [3]

Core Viewpoint - Agricultural Bank of China reported a continued "double positive growth" in net profit and operating income for the year 2025, indicating stable financial performance [1] Financial Performance - Net profit reached 292 billion yuan, representing a year-on-year increase of 3.3% [1] - Operating income totaled 725.3 billion yuan, with a year-on-year growth of 2.1% [1] Asset Quality - Non-performing loan (NPL) ratio stood at 1.27%, a decrease of 3 basis points from the previous year [1] - Overdue loan ratio was recorded at 1.25%, which is lower than the NPL ratio, reflecting strict asset quality assessment standards [1] - Loan provision balance remained above 1 trillion yuan, with a provision coverage ratio of 292.55%, indicating strong risk mitigation capability [1] Asset Scale - Total assets of Agricultural Bank of China amounted to 48.8 trillion yuan, with total loans of 27.13 trillion yuan, an increase of 2.23 trillion yuan [1] - Financial investments reached 16.3 trillion yuan, with an increase of 2.47 trillion yuan [1] - Total customer deposits stood at 38.69 trillion yuan, with an increase of 3.71 trillion yuan, showing improved deposit stability [1] - The foreign and domestic currency deposit deviation rate was 0.58%, the best among peers and consistently below 3% for seven consecutive quarters [1]

关于下一阶段的主要增长驱动因素，王志恒表示将着重做好三个方面的工作： 一是推动巩固利息净收入增速转正，加强量价协同管理。王志恒认为，在规模方面，应深挖有效信贷需 求，加大实体贷款投放，持续调优资产结构，推动生息资产总量结构协调发展；在息差方面，则需加强 精细化定价管理，提升资产边际收益，夯实客户基础，同时做好低成本稳定性资金组织，灵活安排主动 负债，推动存款成本进一步下降。 二是积极拓展非息收入增长空间。王志恒表示，一方面要把握提振消费政策与资本市场改革发展的机 遇，加大财富管理等产品服务供给，满足客户多元化需求，提升中间业务价值贡献；另一方面要加强综 合化经营前瞻，研判市场走势，动态优化投资交易策略和大类资产配置，力争保持较好的其他非息收入 水平。 三是管控好风险和成本，统筹发展与安全。王志恒强调，要持续抓好重点领域信用风险防控，严控预期 和新发生不良，稳住资产质量，减少风险成本消耗；同时加强集约化和精细化管理，压降非紧急非必要 开支，努力以更优成本实现更高收益。 最后，王志恒总结道，农业银行将保持战略定力，持续优化金融服务，更好统筹量价显效，深入推进增 收节支、降本增效，努力将当前良好的发展态势延续下去， ...

北京商报讯（记者 孟凡霞 周义力）3月30日，农业银行召开2025年度业绩发布会，该行副行长林立谈及 风险管控措施时表示，"未来2—3年，商业银行的发展分水岭应该在于风险管理的能力。"林立指出，商 业银行产品、服务乃至未来的AI应用都可能出现同质化，但风险管理的差异化客观存在，做好风险管 理对银行发展至关重要。 数据显示，2025年，农业银行不良贷款率为1.27%，较年初下降3BP。逾期贷款率1.25%，保持可比同业 最优水平。逾期贷款率低于不良贷款率，资产质量认定标准严格。贷款拨备余额保持万亿元以上，拨备 覆盖率292.55%，拨备余额和拨备覆盖率均居可比同业首位，保持较强的风险抵补能力。 对此，林立介绍，农业银行始终将"防控风险"作为金融工作的永恒主题，围绕控新增、降存量、强基 础，重点抓好五个突出：一是突出审慎稳健经营，坚持"小心驶得万年船"；二是突出系统观念；三是突 出发展质效；四是突出问题导向；五是突出底线思维。通过不断强化集团一体化管控，不断提升前瞻防 险、主动控险、精准化险能力，确保资产质量保持总体稳定。 针对普惠零售业务的风险管控，林立介绍，主要做了六方面工作：一个是构建了分层营销的体系，推广 ...