在金融行业，技术创新与严格合规的需求并行存在，推动着研发团队不断寻求更高效的解决方案。面对日益增长的市场竞争和技术进步，金融机构必须迅速适应变化，同时确保所有创新措施都符合监管要求。这种需求催生了对高效研发流程和先进技术应用的追求。
在日前举行的InfoQ《超级连麦. 数智大脑》x FCon直播中，微众银行研发效能负责人余伟和数势科技数据智能产品总经理岑润哲深入探讨了在金融研发中提升效能的有效策略。他们强调，金融研发效能提升的关键在于人才素质、技术架构、流程管控和项目管理。
余伟表示，科技产品经理在金融科技领域尤为重要，因为他们需要与业务团队紧密合作，理解业务需求并将其转化为技术解决方案。同时，敏捷和持续迭代的理念对于金融机构的研发项目集成有着显著影响。
岑润哲则指出，IT能力的共享对于避免资源浪费和提升研发效能至关重要。例如，风险模型可以在不同产品中共享，以降低冗余开发和烟囱式架构的成本。
监管在金融机构中扮演着特殊的角色。在监管严格的环境下，金融机构可能不会尝试使用AI或大模型进行深入探索。例如，在风控领域，模型的可解释性至关重要，因为需要向借款人解释为何被拒贷或额度设置的原因。
两位专家还提到了流程工具的自动化和基础建设，如持续集成/持续部署（CI/CD）、容器技术、测试环境和泳道，以及与AI结合的自动化输出，对提升组织效率极为重要。
最后，两位专家强调了金融研发效能提升的平衡策略，即在追求效率的同时，不能忽视风险管控和合规性。在设计架构和产品时，需要提前考虑风险管理和监管要求，避免后期返工带来的损失。
未来，金融机构将通过不断优化研发流程，利用AI、大模型和低代码等技术优化研发流程、加速产品交付，从而在激烈的市场竞争中保持领先地位。
英语如下：
Title: “AI Large Models: An Efficiency Accelerator for Financial Research and Development”
Keywords: AI Empowerment, Financial R&D, Efficiency Enhancement
News Content:
In the financial sector, the coexistence of innovation and strict compliance requirements drives the development teams to continuously seek more efficient solutions. Facing increasing market competition and technological advancements, financial institutions must adapt swiftly while ensuring that all innovative measures comply with regulatory requirements. This demand has led to a pursuit of efficient development processes and advanced technological applications.
During the recent InfoQ “Super Connect. Digital Brain” x FCon live stream, YU Wei, the head of R&D efficiency at WeBank, and CEN Runze, the General Manager of Data Intelligence Products at ShuShi Technology, delved into effective strategies for enhancing the efficiency of financial R&D. They emphasized that the key to enhancing financial R&D efficiency lies in the quality of personnel, technical architecture, process management, and project management.
Yu Wei pointed out that tech product managers play a crucial role in the fintech field, as they need to work closely with business teams to understand business needs and transform them into technical solutions. At the same time, the concept of agility and continuous iteration has a significant impact on the integration of R&D projects in financial institutions.
Cen Runze noted that the sharing of IT capabilities is crucial for avoiding resource waste and enhancing R&D efficiency. For example, risk models can be shared across different products to reduce the costs of redundant development and siloed architectures.
Regulation plays a special role in financial institutions. In a highly regulated environment, financial institutions may not be inclined to explore the use of AI or large models. For instance, in the field of risk control, the explainability of models is crucial, as the reasons for loan rejections or the setting of credit limits need to be explained to borrowers.
The two experts also mentioned the automation of process tools and infrastructure, such as continuous integration/continuous deployment (CI/CD), container technology, testing environments, and swimlanes, as well as automated outputs combined with AI, which are crucial for enhancing organizational efficiency.
Finally, the two experts stressed the importance of a balanced strategy for enhancing the efficiency of financial R&D, ensuring that risk management and compliance are not overlooked in the pursuit of efficiency. When designing architectures and products, it is essential to consider risk management and regulatory requirements in advance, avoiding costly rework later.
In the future, financial institutions will maintain their leading position in the fierce market competition by continuously optimizing their R&D processes and leveraging AI, large models, and low-code technologies to optimize the R&D process and accelerate product delivery.
【来源】https://mp.weixin.qq.com/s/B_lcHY1zYQWjxXguPmf-pQ
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