Tech Giants Flocking to "AI Finance": It's About Taking Control of the "Entrances," Not Just Competing for Jobs
Hello everyone, I'm your financial journalist friend. Recently, there's been big news in the industry: tech giants like Tencent, Alibaba, OpenAI, and Google have suddenly all jumped into the "financial AI" sector, as if by some secret agreement.
Previously, we thought AI was great at writing code and creating images, but in the financial world, where precision is of the utmost importance, everyone was still hesitant. However, the tide has turned. According to IDC data, the growth rate of financial generative AI in China is set to reach 90.4% by 2025, making it one of the fastest-growing industries for AI applications.
Why now? What exactly are these giants up to? And what does this mean for us ordinary people and professionals? Today, I'll break it down in simple terms.
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Why Now? Because Technology Finally Dares to Enter the "Forbidden Zone"
Many people wonder, with so much financial data available, why hasn't AI entered the financial sector sooner?
In fact, finance and AI are a natural fit. The financial industry generates a vast amount of reports, announcements, and research papers every day, all in the form of text and numbers—exactly what AI excels at processing and analyzing. In the past, it would take a researcher an entire vacation to write a research report; now, with AI assistance, a draft can be produced in just a few hours.
But the reason we couldn't use AI before was due to two major obstacles:
1. AI Could Produce False Information: Finance requires accuracy to the nearest decimal point, and any mistake could result in millions in losses. AI used to create data inaccurately, which was absolutely unacceptable in the financial world.
2. AI Was Only Good at Conversing, Not Doing Actual Work: Previous AI systems were like chatbots that could answer questions but couldn't automate entire business processes.
This year, these obstacles have been overcome:
- False Information Can Be Controlled: Modern technologies like RAG (Retrieval-Augmented Generation) ensure that AI answers are sourced from specified databases and are clearly cited (e.g., "from page 15 of the 2023 annual report"). If an answer is wrong, it's easy to trace where the error occurred, turning potential risks into auditable mistakes.
- Intelligent Agents Have Emerged: AI systems no longer work alone; they work as teams. For example, one agent finds data, another builds models, and a third writes reports, collaborating to complete the entire workflow.
Plus, there's Money to Be Made: Although the financial industry is relatively small (about 1.74 billion yuan in 2025), its growth rate is rapid, and financial institutions are willing to invest. What used to cost tens of thousands of yuan per year for external data accounts can now be reduced through AI, making the return on investment (ROI) very attractive.
So, with technology ready and funding available, it's only natural for giants to enter the market.
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The Three Approaches of the Giants: Selling Tools, Selling Systems, and Controlling Access
Although all are called "AI finance," the giants are adopting different strategies:
1. Selling "Swiss Army Knives" (Ready-to-Use Tools)
- Representative Players: Anthropic, Google, OpenAI
- How They Operate: They package AI into specific "skill sets" such as "prospectus writing assistants," "financial model builders," and "reconciliation robots." You subscribe, and you can use them immediately.
- Characteristics: Easy to use, low entry barrier, suitable for everyone. It's like giving you a sharp knife, but you still need to decide how to use it.
- Risks: High risk of commoditization; if you use OpenAI today and switch to another model tomorrow, users may not stick with you.
2. Providing "Complete Solutions" (Systems Integrated into Core Processes)
- Representative Players: Tencent (WorkBuddy), Alibaba (Dianjin)
- How They Operate: They don't sell individual tools but install AI systems directly into banks and securities firms' internal networks. For example, Tencent's WorkBuddy is integrated into credit approval and customer management processes at companies like CICC and Ping An Bank.
- Characteristics: Highly impactful; once installed, it requires changes to permissions, data interfaces, and employee habits. Since it's deeply integrated, it's hard for customers to switch.
- Advantages: High transaction value and strong customer loyalty, leading to long-term business relationships.
3. Controlling "Traffic Entrances" (Apps for End-Users)
- Representative Player: Alibaba (Qianwen App)
- How They Operate: They offer services from securities firms, funds, and insurance companies through an AI app for ordinary users. You can check market trends, read research reports, and buy funds all through the app.
- Characteristics: Greatest potential for control; whoever controls the access to users' financial information holds the distribution power.
- Challenges: Most difficult to implement due to regulatory requirements, trust issues, and liability concerns. Users are reluctant to entrust large amounts of money to an AI.
In summary: Foreign giants focus on selling AI capabilities, while domestic giants focus on selling services and controlling user access. There are no absolute winners; each plays a different role in the ecosystem.
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What Has AI Really Changed?
Despite the hype, we need to be realistic: AI has yet to touch the core of finance.
- AI Is Good at Mundane Tasks: It's most effective in routine tasks like data analysis and trading.
- Replaceable Jobs: Interns and junior researchers are the most likely to be replaced by AI, as their tasks involve repetitive and standardized processes.
- Judgment Is Still Human: The valuable part of finance is making judgments based on complex information, which requires deep industry understanding, human insight, and experience.
- Core Business Remains Human: Critical decisions like credit approval, risk assessment, and compliance still rely on humans.
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The Giants' Weaknesses: Data Is Not in Their Hands
Many think that giants winning in finance means they have control over all the data, but that's not the case. They face two major challenges:
- Upstream Data: The quality and availability of data depend on third-party providers like Wind, Tonghuashun, Bloomberg, and Moody's.
- Downstream Innovation: Large financial institutions develop their own AI systems to maintain control over their operations.
Conclusion: AI finance is not a simple matter of giants entering the market and winning. It's a complex ecosystem where different players play different roles:
- Model Providers (OpenAI, Alibaba, etc.) supply the technology.
- Data Providers (Wind, Bloomberg, etc.) provide the necessary data.
- AI Platforms (Tencent, Alibaba, etc.) provide the infrastructure and organizational support.
- Financial Institutions retain control over the core business logic and data.
The winner will be the one who finds an irreplaceable position in this ecosystem.
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Implications for Us
- For Financial Professionals: Don't panic, but adapt. Junior roles are at risk, while those with advanced skills (data analysis, business understanding, and AI management) will be more valuable.
- For Investors: Focus on companies that can reduce costs and improve efficiency, as these will truly benefit from AI.
- For Ordinary People: AI is a tool, not a guarantee of profit. Use it wisely and be cautious about privacy and bias.
In conclusion, AI is reshaping finance, but it's not replacing it. It's about enhancing its efficiency and providing new opportunities. The real winners will be those who can leverage AI as a tool for growth, not just for show.