Start with Zero Frame AI Agent: Understand "Financial Intelligent Agent" in One Article

2025 is regarded as the first year of AI Agent, or more precisely, the year when it advanced from cutting - edge technology to practical application.

If large - model technology is a master key, then AI Agent is like a clearer guide and a tangible tool, enabling enterprises to quickly connect to the door leading to the "new world", open it, and step in.

It doesn't just stay in auxiliary areas such as marketing and customer service. Instead, it can reach the "core production scenarios", allowing enterprises to achieve "productivity reshaping" at a lower cost and higher efficiency, thus promoting business growth.

The financial industry also entered its own era of intelligent agents this year. Several leading fintech companies, including Ant Group's fintech arm and Qifu Technology, have officially launched financial intelligent agent products.

Of course, there are different voices in the industry regarding this emerging thing. Some are waiting and watching, some are eager to try, and some find it hard to understand.

However, it's undeniable that this "irreversible productivity revolution" has sounded the clarion call.

So today, we attempt to answer some of the most important questions about financial intelligent agents: What exactly is it? What can it do? And what is its greatest value for a financial institution?

"Digital Expert": From Cognition to Execution

When talking about AI Agent, the most common analogy is a "digital employee", which can handle more problems instead of humans.

But at first hearing this concept, some people may associate it with financial digitization or RPA (Robotic Process Automation) in the financial industry. Hasn't it already existed for many years?

In the 1990s, many enterprises began to use automation software to perform certain repetitive operations instead of humans, such as clicking the mouse, entering text, and copying files.

Its advantages are high stability and accuracy, but it requires humans to preset rules and fixed processes to handle well - defined tasks, so it can only be regarded as a tool.

During the financial digitization stage, more intelligent products capable of solving more complex processes emerged.

For example, 10 years ago, MYbank launched the "310 model" - three minutes to apply, one minute to disburse the loan, and zero manual intervention. In recent years, the underwriting and claims settlement of many insurance companies are also completely completed by AI. Customers only need to take photos of the documents and upload them to complete the "instant" approval.

To some extent, they have achieved "human - free" operations. So, how is the "digital employee" in the era of AI Agent different?

Let's start with a more formal definition from the "2025 In - depth Application Report on Financial Intelligent Agents":

A Financial AI Agent is an AI entity with a certain degree of autonomy. It can perceive its financial environment, conduct reasoning and decision - making based on internal models or knowledge, plan the action steps to achieve the goal, execute complex financial tasks by calling external tools or system interfaces, and provide feedback and make adjustments according to the execution results.

(Source: "2025 In - depth Application Report on Financial Intelligent Agents")

Mark the keywords, "Perception - Reasoning - Planning - Execution - Evolution".

During the financial digitization stage, it was similar to the evolution of industrial production from manual work to automated assembly lines. The assembly line is a pre - set program, and once started, no link can be skipped. Moreover, the "fed" data cannot exceed the scope and must be standardized (for example, unstructured data was not acceptable in the early days).

At this stage, although the intelligent system has a certain ability to make decisions and deliver results, it mainly follows the pre - set scripts by humans. Just like many current intelligent customer service systems, they can only answer questions based on established templates and "question banks". When faced with slightly complex questions or different ways of asking questions, the system will give irrelevant answers.

However, during the financial intelligence stage, the development of large - model technology has given AI a smarter "brain".

Compared with before, large models can understand and "learn" a wider range of things, including text, pictures, audio, and video. Through training with massive data, large models can also have the ability to understand and generate natural language, and even reasoning ability, capable of logical analysis and generating high - quality content.

AI Agent based on large - model technology takes it a step further. With a smarter "brain", they no longer need any pre - set processes or scripts by humans. Instead, they can truly perceive, think, and solve problems like humans, achieving "end - to - end" result delivery, rather than just process assistance.

If large models are a smart "brain", then financial intelligent agents transform the cognitive and understanding abilities of this "brain" into the executive ability for financial business, including formulating multi - step strategies and calling internal and external tools, and learning and optimizing according to the execution results.

In short, financial intelligent agents are more like a bridge and a shortcut connecting large - model technology and the real financial world.

If we use the analogy of a "digital employee", it is an expert - level employee with more experience and stronger professional abilities. It has a set of professional and high - level thinking, planning, and execution abilities, and can even flexibly handle problems beyond existing experience, and its own abilities are constantly evolving.

Ant Group's fintech arm summarized in an internal sharing about financial intelligent agents that a financial intelligent agent should have several key elements: financial large models, financial knowledge bases, financial toolkits, and security & professional evaluations. That is to say, it is not a single model but a "systematic project".

Therefore, the "digital employee" in the era of AI Agent is more "anthropomorphic" but far exceeds the capabilities of an ordinary person.

Productivity Revolution: From Periphery to Core

After understanding the capabilities of financial intelligent agents, it's not difficult to understand why they can trigger a new round of "productivity revolution" in the financial field.

It's not just a simple upgrade of existing tools. Its autonomous working ability is fundamentally reshaping the production mode of the financial industry, thus releasing huge productivity.

The "2025 In - depth Application Report on Financial Intelligent Agents" summarized five disruptive potentials of financial intelligent agents:

1. Break process barriers and achieve end - to - end automation

The core advantage of intelligent agents lies in their ability to perceive, plan, and execute complex task chains, thus breaking the automation breakpoints in traditional processes and achieving end - to - end process automation.

2. Autonomous perception and real - time response

Intelligent agents can monitor real - time changes in the internal and external environment through sensors, such as market price fluctuations, risk events, changes in customer behavior patterns, or emergencies.

3. Intelligent planning and execution of complex tasks

Facing a high - level business goal (such as "generating a customized investment report for a customer"), an intelligent agent can autonomously break it down into a series of specific subtasks (such as "querying the customer's holdings, obtaining the latest market data, running the asset allocation model, writing a draft report based on the model results, and formatting the report"), and plan the execution order. By calling corresponding external tools (such as investment model APIs and report - generating tools), the intelligent agent can autonomously complete the entire complex task chain.

4. Continuous learning and self - optimization

Excellent financial intelligent agents have the ability to learn from historical task executions. By analyzing successful or failed cases, intelligent agents can continuously optimize their decision - making logic, planning strategies, and tool - calling methods, improving their ability to handle complex and unknown situations, and thus continuously enhancing performance and accuracy in long - term applications.

5. Lower the threshold and cost of financial services

The automation and low - cost characteristics of financial intelligent agents enable them to effectively reach marginal populations and underdeveloped areas that are difficult to cover by traditional financial services.

"The role of financial intelligent agents in the financial system is gradually evolving, leading an irreversible productivity revolution. Their development trajectory has evolved from the initial auxiliary tools to more advanced collaborators, and may even become task leaders in specific scenarios." The report concluded.

When generative AI first emerged, people had high expectations and hopes for large - model technology, and the financial industry was no exception. However, looking at the situation in the past two years, after the initial excitement, financial institutions' attitudes towards large - model technology have remained lukewarm.

After all, intelligent customer service and intelligent marketing are peripheral scenarios and do not directly contribute to growth. Challenges such as cost, data, and technical thresholds objectively exist, but the essence is that people have not seen the contribution of new technologies to core business, revenue, and profits.

However, the emergence of AI Agent may change this situation because it is reshaping productivity and constantly advancing from the periphery of business to core scenarios.

Judging from current practices, the launched financial intelligent agent products have penetrated into different links of the entire financial industry chain, such as Qifu Technology's "Super Credit Intelligent Agent".

Ant Group's fintech arm has explored more than 100 in - depth application scenarios of financial intelligent agents, covering four major fields: banking, securities, insurance, and general finance, and penetrating into scenarios such as customer service, internal operations, marketing and sales, risk management, product innovation, and decision - making support.

Let's start from a specific wealth management scenario to see what financial intelligent agents can do and what incremental value they can create.

For example, when a customer asks, "Is my current portfolio reasonable? Please give me some advice."

After receiving this question, the financial intelligent agent will first analyze the user's instruction, including interpreting the hidden needs behind the instruction. For example, the subtext of the customer's words may be "Do I need to adjust my portfolio due to market changes? Is my current portfolio too risky?"

After analyzing the instruction, the financial intelligent agent will break this demand into several specific tasks: understanding the customer's profile, portfolio situation, and recent market changes, and then completing the analysis and diagnosis and giving corresponding advice. Then, based on these tasks, it will call relevant data and tools to complete them one by one.

On this basis, the financial intelligent agent will also output answers from multiple dimensions such as professionalism and emotion, and even actively guess the questions the customer may want to ask.

Thus, the customer can get a high - quality "consultation" and "advice" on wealth management in a very short time.

Compared with the relatively standardized business process of the credit business, the pain point of the wealth management business is that customers' situations and financial needs vary greatly, making it difficult to provide precise services. At present, the combination of humans and digitalization still has "breakpoints", and the customer experience is not smooth.

The closed - loop mechanism of "Perception - Reasoning - Planning - Execution - Evolution" of financial intelligent agents can achieve "end - to - end" solutions to more complex financial problems, and is expected to truly realize "personalized" financial services.

Embracing AI Agent: Ultimately a "Top - Down Project"

The financial digital transformation in previous years laid a certain foundation for today's intelligence. Even so, financial institutions still face challenges such as rigid processes, data silos, high labor costs, and insufficient personalized services in more complex business scenarios.

The emergence of generative AI has excited all industries, but for financial institutions, there are still many obstacles from the deployment to the application of large - model technology. Many financial institutions that high - profilely connected to DeepSeek at the beginning of the year have taken no further actions.

Although DeepSeek has reduced the threshold for enterprises to deploy and use large models to a certain extent, it doesn't mean there is no investment.

It is understood that from infrastructure investment to application implementation, the one - time cost investment of financial institutions is at least in the millions. However, what is the output? How much of the result can be quantified? There is no answer.

In addition, some "hidden thresholds" cannot be ignored. For example, regarding the "data silo" problem mentioned above, according to a McKinsey research report, 40% of enterprises have more than 50 data silos, which will lead to a 20% - 30% decline in the accuracy of large models.

Another problem is that the shortage of compound - type talents is becoming more and more acute. Applying large - model AI technology to specific financial scenarios is not easy, and the cross - ability collaboration of AI + finance is much more complex.

McKinsey predicts that the shortage of AI talents in China will reach 5 million in 2030, and those with both algorithm and financial business capabilities account for less than 15%.

Facing various challenges, the ability of financial intelligent agents to penetrate into business scenarios and autonomously solve problems has reduced the upfront investment cost, running - in cost, and compliance risk of financial institutions, allowing more institutions to see the dawn of bringing "value increment" through investing in "technological variables". In particular, this value growth is both quantifiable and sustainable.

Currently, the development of financial intelligent agents in China is in a state where single - agent and multi - agent collaborative applications coexist.

According to McKinsey's definition, "single agents" mainly solve some specific and relatively simple business needs. The "multi - agent system" is like a virtual workplace. Each intelligent agent has its own expertise in a specific field and is uniformly called by the "coordinating agent", enabling the multi - agent system to have the abilities of action planning, using tools to execute plans, cooperating with other intelligent agents and personnel, and self - improvement through practice, just like humans.

This single + multi - agent ecosystem further lowers the "trial - and - error" threshold for financial institutions. They can start from a specific business scenario or demand to try out corresponding financial intelligent agent products without subverting the previous business framework from the beginning.

Based on the implementation experience of financial intelligent agents, Ant Group's fintech arm summarized four paths:

In the era of large models, the evolution and iteration speed of AI far exceed our imagination. With the accelerated implementation of financial intelligent agents, the application threshold is lower and the results are faster. For financial institutions, the business gap caused by whether to apply the "new - quality productivity" will also become larger and larger.

Of course, the new round of productivity change not only brings technological upgrades but also a systematic project of strategic reconstruction, organizational change, and cultural reshaping.

McKinsey's survey and analysis show that having the CEO personally supervise this work is one of the key factors for enterprises to improve financial performance with generative AI. Especially in large enterprises, the direct participation of the CEO has the most significant impact on EBIT.

For the financial industry, from digital migration to paradigm reconstruction, the future is here.

This article is from the WeChat official account "Xin Finance" (ID: Xinfinance), author: Focus on the Frontier. It is published by 36Kr with authorization.