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论文编号：	15758
作者编号：	2320234010
上传时间：	2025/12/9 20:30:44
中文题目：	A银行科创企业信贷风险管理优化研究
英文题目：	Study on the Optimization of Credit Risk Management for Sci-Tech Enterprises in Bank A
指导老师：	张晓农
中文关键字：	科创企业；信贷风险；风险管理
英文关键字：	Sci-Tech Innovation Enterprises；Credit Risk；Risk Management
中文摘要：	A银行作为国有大型商业银行，已将科创金融列为战略方向。当前中国处于创新驱动发展关键阶段，科创企业成为经济增长“新引擎”，而科创企业普遍具有“轻资产、高风险”特征，其核心风险属性与银行传统信贷风控逻辑存在显著差异，这使得A银行在科创企业信贷风险管理中面临独特挑战，相关工作仍有改进空间。 本文聚焦A银行科创企业信贷风险管理优化方案，采用文献研究法、案例分析法、问卷调查法、访谈法及定量与定性结合法展开研究，梳理出五大核心问题：风险评估体系与企业特点不吻合、信贷产品与企业需求错配、贷后风险监测预警机制不完善、专业风险管理人才储备不足、与外部机构风险协同机制不完善，进而阐述信贷风险管理优化的必要性。 结合信贷风险管理理论，针对现有问题设计优化策略，具体包括五方面：一是构建“财务+技术+市场+政策+团队”五维风险评估框架，引入专业评估机构并建立成本分摊机制，整合大数据搭建企业风险画像系统；二是按企业研发周期匹配贷款期限，创新还款方式，构建全生命周期信贷产品体系，增加配套附加服务；三是建立针对性贷后动态监测体系，合理设定预警阈值，提升风险处置效率；四是优化人才结构，完善培养体系，健全外部专家协作机制，加强专业人才储备；五是打破信息共享壁垒，健全风险分担与补偿机制，加强行业自律与监管协同。同时，从组织制度、人员技术、政策协同三大维度提出保障措施，确保策略落地。 本文以点带面，通过设计A银行科创企业信贷风险管理优化方案，探索出具备可推广性的国有银行科创信贷风险管控模式，为A银行提升业务竞争力提供支撑。同时，鉴于国内对国有银行科创信贷的研究多侧重宏观方向，本研究也为银行业针对科创企业的具体信贷风险管理研究提供借鉴。
英文摘要：	As a large state-owned commercial bank, Bank A has designated sci-tech innovation finance as a strategic direction. China is currently in a critical stage of innovation-driven development, where sci-tech innovation enterprises have become the "new engine" of economic growth. However, these enterprises generally feature "asset-light and high-risk" characteristics, and their core risk attributes differ significantly from the logic of banks' traditional credit risk control. This exposes Bank A to unique challenges in credit risk management for sci-tech innovation enterprises, leaving room for improvement in related work. This thesis focuses on the optimization plan for Bank A’s credit risk management of sci-tech innovation enterprises. It adopts literature research, case analysis, questionnaire surveys, interviews, and a combination of quantitative and qualitative methods to conduct the study. Five core issues are identified: misalignment between the risk assessment system and enterprise characteristics, mismatch between credit products and enterprise needs, inadequate post-loan risk monitoring and early warning mechanisms, insufficient reserve of professional risk management talents, and imperfect risk coordination mechanisms with external institutions. The necessity of optimizing credit risk management is further elaborated. Based on credit risk management theories, optimization strategies are designed to address existing problems, specifically covering five aspects: first, constructing a five-dimensional risk assessment framework of "finance + technology + market + policy + team", introducing professional evaluation institutions and establishing a cost-sharing mechanism, and integrating big data to build an enterprise risk profile system; second, matching loan terms with enterprises’ R&D cycles, innovating repayment methods, constructing a full-life-cycle credit product system, and adding supporting value-added services; third, establishing a targeted post-loan dynamic monitoring system, setting reasonable early warning thresholds, and improving the efficiency of risk disposal; fourth, optimizing the talent structure, improving the training system, improving the external expert collaboration mechanism, and strengthening the reserve of professional talents; fifth, breaking information sharing barriers, improving risk-sharing and compensation mechanisms, and enhancing industry self-regulation and regulatory coordination. Meanwhile, safeguard measures are proposed from three dimensions—organizational systems, personnel technology, and policy coordination—to ensure the implementation of the strategies. Taking a point-to-face approach, this thesis explores a replicable sci-tech innovation credit risk control model for state-owned banks by designing the optimization plan for Bank A. It provides support for Bank A to enhance its business competitiveness. Additionally, given that domestic research on sci-tech innovation credit of state-owned banks mostly focuses on macro directions, this study also offers references for the banking industry’s research on specific credit risk management for sci-tech innovation enterprises.
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