Summary of Key Points in Plain Language
Recently, three leading internet companies—Tencent, Baidu, and ByteDance—have all focused their AI initiatives on the financial industry. These AI systems are capable of automatically executing a range of complex tasks. However, the target users and approaches each company has chosen are vastly different, and the industry as a whole is still unsure about the final form of the products. The question on whether it’s enough to simply add a few financial functions to a general-purpose office AI or to develop a dedicated AI that is fully tailored to financial rules and internal processes remains unresolved. Regardless of the chosen path, the financial AI sector has been proven by Wall Street to be a highly profitable market. Due to the strict regulations and the sensitivity of financial data, this field also serves as a test of AI’s practical capabilities. The company that succeeds first will gain a significant advantage in providing AI services to other high-value industries.
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Detailed Explanation of Each Point
1. The reason why big companies are competing fiercely for the financial AI market is a clear calculation of potential profits
Many wonder why AI is being focused on the financial sector when there are so many other industries it could be applied to. The reason is straightforward and purely commercial:
- The labor costs in the financial industry are extremely high. Analysts at securities firms, risk managers in banks, and insurance product managers earn high salaries, but more than half of their time is spent on tasks that require no technical expertise, such as checking financial reports, searching for information in announcements, and entering data into templates for presentations. AI can take over these tasks, saving financial institutions a significant amount of money. This is a significant incentive for them to invest in AI.
- Almost all financial data is already digitized, making it much easier for AI to process. Unlike manufacturing or healthcare, where data often needs to be collected from physical documents, financial data is readily available in digital formats, making it much simpler for AI to integrate.
- It’s like opening a food delivery business in a neighborhood where most people work and where the roads are well-maintained, making delivery effortless. There’s no doubt that companies will invest in AI in this area.
2. The three companies are not competing with each other; their target audiences are completely different
Despite claiming to be entering the financial AI market, the target users of ByteDance, Baidu, and Tencent are vastly different, so they are not direct competitors at this stage:
- ByteDance’s AI products are designed for individual investors. They break down the professional functions provided by top securities firms into smaller, usable tasks, such as filtering ETFs or comparing fund performances. Users can simply ask the AI to list the top 5 ETFs with the highest net capital inflows in the past week and compare their performance over the past year, and the AI will provide the results using data from securities firms.
- Baidu’s AI products are still positioned as general-purpose office tools, with financial functions serving as a showcase of their capabilities. They aim to demonstrate their versatility and plan to expand into other industries like law and education.
- Tencent’s WorkBuddy financial version targets the internal needs of financial institutions. It provides services such as background checks and regulatory compliance checks for banks and insurance companies, automatically generating reports that meet industry standards.
3. The industry is debating whether to develop a dedicated “financial AI version”
All AI companies are facing a crucial question: should they build a general-purpose AI system and add financial functions later, or should they create a dedicated financial AI product from the start? Tencent has chosen the latter approach, while Baidu and ByteDance have opted for the former.
The debate arises because there are significant differences between the two approaches. Building a general-purpose AI and adding financial functions later would be less costly and could lead to a broader market. However, the financial industry’s strict regulations require that AI outputs be traceable and that human review be involved in critical decisions. Creating a dedicated financial AI would allow for higher prices, as it would cater to the unique needs of this highly regulated sector.
If a dedicated financial AI product is developed, it could generate much higher profits. For example, while a general-purpose AI membership might cost a few hundred dollars per year, a dedicated financial AI product for financial institutions could generate millions. The outcome depends on which approach proves more viable.
4. Wall Street has already shown its confidence in this market
Not only domestic companies are investing in financial AI; overseas tech companies like OpenAI and Anthropic are also adding financial features to their general-purpose AI systems. Google has even launched a dedicated financial AI product. Capital and financial institutions have already shown their support for this direction. For instance, a startup called Rogo, which focuses on financial AI, has seen its valuation increase sixfold in just one year to $2 billion. Its success lies in its ability to provide reliable data by connecting to professional financial databases. More than 300 companies and tens of thousands of financial professionals use its services.
The entire industry is watching to see whether financial institutions are willing to pay a higher price for more tailored and secure AI solutions. If this trend continues, other high-value industries like healthcare and law will also develop their own dedicated AI products, transforming the entire AI industry. If general-purpose AI with additional plugins is sufficient, Tencent’s approach may become less viable in the long run.