21st Century Business Herald reporter Yi Yanjun reported from Guangzhou
The current wave of artificial intelligence applications set off by Chat GPT extends to the financial industry.
Recently, Industrial Securities Global Fund launched the AI capital trading robot - intelligent trader "Xingbao", becoming the first domestic fund company to apply AI technology to the field of capital trading.
In fact, under the tide of AI, from brokerage analyst AI avatar, AI quantitative investment to AI capital trading robot... Since the beginning of this year, a number of financial institutions such as securities firms and fund companies have launched AI products suitable for investment scenarios. At the same time, financial software service providers represented by Hang Seng Electronics are also promoting the landing of AI investment and research products.
Since 2016, securities companies have successively launched intelligent applications such as intelligent investment advisory services and digital innovation laboratories, and it has become a general trend to deeply integrate AI technology with operations, risk management, customer service and other businesses. At the same time, fund companies are also trying the same, and have even integrated AI into the trading process. The birth of Chat GPT accelerated this process.
"For the application of AI, the industry as a whole is still in the initial exploration stage, the industry is thinking from all directions how to use AI technology to improve its own operational efficiency and effect, among which the more active companies have begun to try to launch related products, there are successful cases, but there are many failed cases." A senior industry insider pointed out to the 21st Century Business Herald reporter.
For the combination of AI and trading, the person believes that capital transactions have extremely high requirements for security, stability, accuracy and other elements, so applications such as AI capital transactions still need to guard against hidden risks in data, algorithms and computing power.
AI+ brokerage: need to take into account compliance
As we all know, the advent of ChatGPT marks that the development of AI has entered a new era of general artificial intelligence (AGI), and AI applications such as dialogue, writing, and literary drawings based on "big models" have also begun to affect the format of securities companies.
In May this year, the AI avatar of a brokerage analyst entered the public eye for the first time.
China Merchants Securities created an AI digital avatar for its chief media analyst, Gu Jia. According to the official introduction, Gu Jia's AI avatar can appear at the roadshow site, press conference, research report interpretation, analyst conference call, and any place where customers need it.
It is understood that in recent years, China Merchants Securities has comprehensively built an AI system, and its digital employee assistant cases have been introduced into the "Digital Transformation Practice Report and Case Collection of Securities Companies (2022)".
At the same time, there are also brokers seeking external cooperation to explore AI applications suitable for brokerage businesses.
For example, on May 18, Soochow Securities and Flush officially signed a contract to jointly establish an AI research institute to jointly develop Soochow Securities-Securities Industry Large Model.
China Galaxy said on the investor interactive platform on April 4 that the company has cooperated with a number of AI companies with market competitiveness, and uses AI technology in the fields of intelligent marketing, intelligent investment advisory, intelligent customer service, intelligent risk control, intelligent document, identity recognition, etc., and will continue to follow up the latest AI technology to expand the application scenarios and application fields of AI technology.
Aiming at the "AI+brokerage" scenario are also financial software service providers. Recently, it was reported that Hang Seng Electronics may launch new digital intelligence financial products positioned in AI investment and research.
In addition, overseas Columbia University recently launched FinGPT, a financial large-model product with NYU Shanghai.
In overseas markets, investment banks have applied the latest GPT products to wealth management business. In March, after OpenAI released GPT-4, Morgan Stanley said that it has used GPT-4 technology to convert all think tank content into a more user-friendly and operational format. Morgan Stanley has 300 consultants testing the tool and plans to roll it out widely in the coming months.
The use of AI technology can not only help various business departments of securities firms improve work efficiency, but also tap into a wider range of wealth management needs.
Hou Yanjun, general manager of Houshi Tiancheng Investment, believes that AI will be widely used in the financial field in the future in data mining, algorithms, customer service, work efficiency, etc.
However, some insiders pointed out that the actual landing of some AI technologies still needs to be explored. In addition, how securities firms can balance compliance when exploring AI applications is one of the common challenges faced by the industry. For example, virtual digital humans such as analyst AI avatars are still in the "regulatory gap" zone.
AI+ capital trading: prevent three major risks
In contrast, for fund companies with relatively single business models, their demands for AI are more focused. In recent years, some fund houses have combined AI with risk control, research, customer service or decision-making. The latest trend is that fund companies are beginning to apply AI technology to the field of fund trading and the inquiry process.
Recently, Industrial Securities Global Fund launched an AI capital trading robot - intelligent trader "Xingbao". At present, "Xingbao" has been officially launched on the Qtrade platform.
According to reports, AI traders can not only through the identification and extraction of key elements, understanding of context logic, take the initiative to initiate question confirmation, real-time extraction of deep-level intentions, actively distributed questions, quickly obtain counterparty intentions, but also through continuous question and answer exchanges, through a series of inquiry negotiation process, complete the counterparty's inquiry demand collection, and the inquiry status real-time feedback to the trader, obtain the final matching transaction feedback to the trader, and the counterparty can complete the transaction after confirmation.
In March this year, the "Xing Xiaoer" AI bond trading robot independently developed by Industrial Fund was also launched, and the company became the first public fund company to launch an intelligent inquiry robot on the iDeal platform of the foreign exchange trading center.
"In the past, there have been institutions trying to combine AI and transactions, more through data algorithms or business rules to do artificial intelligence exploration, in recent months, with the rise of large language model capabilities represented by ChatGPT, this kind of exploration has once again become a hot spot in the industry." The above-mentioned senior industry insiders pointed out to the 21st Century Economic Herald reporter.
In his view, in the coming months, if not years, institutions will continue to try to combine AI and transactions.
This is mainly because, "AI technology, especially the technology of a new generation of large language models, has an impact on most industries, in the field of trading, through AI technology and algorithms, constantly deepen the details of investment transactions, such as robot inquiry, etc., liberate people from simple repetitive and even general work scenarios, so that traders can focus more on professional deep digging." ”
However, he also said that AI is currently more in general applications, but trading is a very professional field, due to the limitation of data sources, training scenarios and other factors, there are not many successful cases. In addition to technological breakthroughs, the successful application of AI requires a large number of business scenario integration, and only through the polishing and in-depth refinement of scene technology integration can the actual landing of AI technology be truly achieved.
It is worth mentioning that capital transactions have extremely high requirements for security, stability, accuracy and other elements, and in the process of combining with AI technology, it is still necessary to guard against hidden risks in data, algorithms and computing power.
The above-mentioned senior industry insiders said in detail, first of all, because capital transactions have extremely high requirements for data security and accuracy, the importance of data foundation ranks first in the process of applying AI technology. AI itself cannot solve the problem of data accuracy, so the basic data governance work is very solid, and it is necessary to ensure a high-quality data foundation, including data consistency, accuracy, security, and so on.
Secondly, in terms of algorithms, artificial intelligence can do a lot of things through AI, but at present, AI technology algorithms often do mathematical probability things, artificial intelligence has not developed to be as intelligent as people, so there will be wrong results. For example, the ChatGPT large model performs well most of the time, but it will also answer unreliably in individual cases. The tolerance of capital transactions for errors is extremely low, and the probability of one in ten thousand is not allowed. In this case, how to review or multi-layer recognition of the results generated by AI needs special attention.
Third, in terms of computing power, the new generation of artificial intelligence represented by large language models has high requirements for computing power, and tens of billions and hundreds of billions of parameters still need a lot of training. For fund companies, if they directly borrow the capabilities of public big models such as Baidu and iFLYTEK, it will involve the problem of data security, and if they deploy and train independently, they will face the problem of input-output ratio, and how to balance public computing power and private computing power is a problem that all companies in the industry have to face.
In addition, Yang Delong, chief economist of Qianhai Open Source Fund, said that the risk of applying AI to transactions lies in the compliance of transactions, such as whether such transactions consider the fairness of transactions while taking into account efficiency. At the same time, we should also pay attention to preventing systemic risks, such as the relatively large proportion of quantitative investment in the United States, and there may be stampedes caused by quantitative trading, causing the Dow to fall by 1,000 points in an instant. Such risks require attention.
Can AI replace active fund managers?
On the other hand, the market is more concerned about whether AI will be able to replace fund managers and make independent investments in the future.
"In quantitative investment, AI technology has been widely used in investment decision-making, and there are relatively few cases of subjective bulls, especially value investment." He Li, fund manager of Zhishan Investment, pointed out that if the value investment strategy wants to achieve AI investment, it needs to have deep value investment capabilities + advanced AI technology and understand the technology, and the combination of the two can be done, and this process may have to pay a lot of costs in research and development.
"Chat GPT has brought AI to fire, but specific AI technologies such as machine learning have long been widely used in quantification, so the application of AI in the field of quantification and investment is no longer the initial stage, and it is now becoming more and more mature, but the latest algorithms and computing power are still in the process of continuous iteration." In the long run, AI is one of the good investment aids, but in the future, AI is unlikely to completely replace human investment, but investment that will use AI may replace investment that will not use AI. Hu Bo, fund manager of Rongzhi Investment FOF, pointed out.
Yang Delong also said that it is feasible for fund companies to use AI technology to participate in some auxiliary work in investment research, because AI applications can indeed save a lot of manpower, and there are also some advantages that manual does not have, such as strong data computing ability. But a complete replacement of labor is unlikely. After all, there are still many aspects of the capital market that need to be judged manually, so AI cannot completely replace human labor.
In the view of the above-mentioned senior industry insiders, whether AI technology can be successfully applied to the key links of investment decision-making depends on three levels: first, technical feasibility, which areas of AI can be empowered, and which links AI technology is relatively mature; Second, social feasibility, in which areas investment managers and researchers are more willing to use AI technology in the investment decision-making process, and which links are easier to achieve AI empowerment; The third is the input-output ratio, many AI applications are innovative projects, there is a probability of failure, and the input-output ratio must be considered.
"Considering these three aspects, at present, when investing in research, the collation of research records, the refinement of information content, the writing of research reports, etc., AI technology is relatively mature, it is expected that investment researchers are more willing to use AI technology to improve their work efficiency, and the cost investment will not be large, these can give priority to the formation of intelligent auxiliary tools, empowering investment research and efficiency." The specific investment decision depends more on the personal experience and ability of the fund manager, AI technology is not mature, the willingness and degree of integration of the investment personnel themselves are difficult to determine, and the input-output ratio is even more difficult to measure, so the participation of AI in it is much slower. However, quantitative investing is expected to be the first to use AI capabilities to make decisions. He said.
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