Guotai Haitong: AI boosts the internal efficiency and external value of financial institutions, opening up a new era of financial digitization.

Guotai Haitong released a research report stating that the release of DeepSeek R1 in 2025 will boost the inference capabilities and reduce costs of general models, and achieve model open source, becoming a turning point for the industry to locally deploy AI in financial institutions. Currently, AI applications have been accelerating penetration in various core businesses and back-office scenarios of financial institutions. In the future, AI is expected to reconstruct financial business processes and organizational structures, ushering in a new era of financial intelligence and boosting internal efficiency and external value for financial institutions. It is recommended to focus on financial information services, third-party payments, bank IT, securities IT, and insurance IT related targets. Guotai Haitong's main points are as follows: Financial AI applications gradually expand from shallow to deep Currently, most financial institutions are still in the stage of exploration and accumulation, and have not yet achieved large-scale deep applications, especially in complex business scenarios. Nevertheless, considering the high degree of fit between the demand for digital transformation in the financial industry and the characteristics of large model technology, and the leap in model technology capabilities and the decrease in model deployment costs providing a good "incubator" for financial institutions to explore AI applications, it is believed that achieving large-scale deep application of models will be an inevitable trend in the development of the financial industry. Currently, a small number of top large financial institutions with good technical foundations and investment budgets have begun to explore deeper levels of AI business empowerment. AI assists financial institutions in achieving internal cost reduction and efficiency improvement as well as external value discovery In terms of cost reduction and efficiency improvement, AI applications focus on optimizing internal operations management, improving core business processes, and empowering financial institution employees; in terms of value discovery, AI focuses on marketing, customer acquisition, customer service, etc. for financial institutions, achieving an increase in sales transactions and single customer value. Large institutions strengthen self-research, while medium and small institutions pursue cost-effectiveness Due to differences in technical foundations and operational focus, different types of financial institutions have different development paths in the deployment of large model technology and application landing. Large financial institutions often rely on strong computing power reserves and strong research capabilities to achieve deep penetration of AI applications through the private deployment of large models; while medium and small institutions mostly rely on lightweight models, large model integrated machines, and other technological forms to achieve flexible access to large models and agile application development. Risk Warning The development and iteration of large model technology may not meet expectations, AI application landing may not meet expectations, and there may be policy and compliance risks.