In recent years, with the rapid development of generative artificial intelligence in the world, the pace of related innovation and application in China has been significantly accelerated, and the value potential of artificial intelligence technology in the domestic financial field has attracted much attention, especially in mobile banking, with a huge user base and rich scenarios, which provide a good opportunity for the landing of artificial intelligence technology. Artificial intelligence technology has the opportunity to fly into the homes of ordinary people through everyone's mobile banking, bringing convenience to the people.
In this context, many mobile banks have recently launched iterative upgrades and new versions, focusing on improving the application of artificial intelligence technology. For example, Bank of Communications launched mobile banking 8Version 0, the new version relies on the ability of artificial intelligence big data analysis, extracts from massive information, and launches the first big data list to help customers make investment decisions. Postal Savings Bank Mobile Banking9Version 0 creates a "AI space + digital staff + customer service" service. Among them, the AI space can be entered by pulling down the homepage of mobile banking to visually display the current month's income and expenditure, common payments, recent payees and other information to customers, and provide customers with customized services.
Before artificial intelligence technology promotes the upgrade and iteration of mobile banking, mobile banking is actually facing some problems and challenges. On the one hand, mobile banking focuses on registered users and ignores active users. The number of registered users of mobile banking is important, but if the activity of registered users is very low, then the user meaning is greatly reduced, and at the same time, it also takes up a lot of resources. On the other hand, mobile banking focuses on product deployment over user experience. Although some mobile banking features may seem powerful, users find it inconvenient to actually use them. For example, the interface design of some mobile banking apps is too complex, the operation is cumbersome, and the text description is too professional to make it difficult for ordinary users to understand, which increases the learning cost and makes the user experience very poor.
In response to these problems, people expect artificial intelligence to bring a positive transformation to mobile banking. Some industry experts also shared their views. "In fact, there have been many applications of artificial intelligence technology in mobile banking. Dong Ximiao, chief researcher of Zhaolian and chief researcher of Zhongguancun Internet Finance Research Institute, said for example, for example, voice recognition, as soon as the intelligent voice is opened in the mobile banking app, there is no need to find it layer by layer, and the required functions can be summoned directly through voice. This makes it more convenient for the elderly and users who do not have good eyesight. Now mobile banking is a comprehensive platform, equivalent to an online bank branch, more than 95% of personal business can be handled in mobile banking, artificial intelligence technology has a wide range of application prospects, not only artificial intelligence technology, many new technologies are also combined in mobile banking.
Wang Pengbo, a senior analyst in the financial industry at Broadcom Consulting, said that at present, generative artificial intelligence is developing rapidly, and the financial industry is also actively embracing new opportunities. For mobile banking, generative AI can be applied to a variety of aspects such as risk management, customer service, intelligent recommendation, and transaction monitoring. The application of generative AI, such as personalized recommendations and customer service, can make it easier for customers to access the services and products they need and improve the service experience.
Although the application of artificial intelligence technology in mobile banking has been widespread, there are also controversies related to it. Industry experts pointed out that the application of many artificial intelligence technologies in mobile banking is still in its infancy, and if artificial intelligence wants to bring a qualitative leap to the transformation of mobile banking, the actual effect needs to be tested by time. From the perspective of data security, AI needs a large amount of user data to train and optimize, and if mobile banking is not properly protected in terms of collecting, storing and using user data, user privacy protection is worrying. From an environmental point of view, the awareness and acceptance of AI technology in society as a whole is not high, and the laws and regulations related to AI need to be further improved. From a subjective point of view, some financial institutions are relatively closed, still under the "Party A thinking", lack of awareness of active innovation and service, do not invest much in financial technology, insufficient talent reserves, and artificial intelligence technology is not strong.
In view of how artificial intelligence will help the digital transformation of banks in the future, Dong Ximiao suggested that mobile banking should adhere to "people-oriented", which is the basic principle of mobile banking business development. It is necessary to cultivate "Party B thinking" and study the market and customer needs, especially the development pain points. In order to continue to improve its own scientific and technological capabilities, it is necessary to increase investment in science and technology, and cultivate technical advantages and talent teams with core competitiveness.
Wang Pengbo believes that on the one hand, the use of new technologies such as artificial intelligence to reduce costs is the only way, and on the other hand, it is necessary to create scenarios as much as possible. Technical personnel also need to communicate more with business personnel and be truly familiar with relevant application scenarios and operation rules, so as to better integrate large AI models with scenarios. Mobile banking, which is more compliant, more secure, and more widely meets the needs of users, will be popular. (Economic ** reporter Su Ruiqi).