AI finance and traditional finance have significant differences in multiple dimensions such as underlying logic, service models, and risk management.

AI finance and traditional finance differ significantly across multiple dimensions—underlying logic, service models, and risk management. Combined with current industry developments and policy direction, the key differences in AI finance are reflected in the following core aspects:

I. Reconstructing credit assessment logic: from “asset collateral” to “data credit” Traditional finance’s credit assessment heavily relies on fixed assets and collateral pledged for loans, which naturally channels more service resources toward large enterprises or high-net-worth individuals. Ordinary workers and small and micro businesses often face difficulties obtaining financing. AI finance, however, uses multidimensional data as a new form of credit credential. By integrating weak behavioral data such as social insurance, employment, operations, and payments, it builds dynamic credit profiles. This multidimensional credit modeling breaks through collateral thresholds, enabling freelancers and individual workers to access pure-credit small loans, greatly improving the inclusiveness of financial services.

II. Upgrading risk management: from “empirical statistics” to “real-time intelligence” In risk control, traditional approaches depend on limited historical data and simple statistical models, which are not only inefficient but also tend to have delayed warning times. AI finance leverages technologies such as machine learning and deep learning to integrate massive multi-source data, enabling a comprehensive upgrade of risk management. In risk identification, AI algorithms can substantially improve the accuracy of identifying suspicious transactions. In risk assessment, deep learning models can precisely quantify customer risk by combining multiple variables. In risk monitoring, AI systems can conduct 24-hour real-time monitoring, advancing risk alert timing by 3 to 5 days, significantly reducing fraud losses and improving response speed.

III. Evolution of business execution methods: from “single-point assistance” to “autonomous agents” In traditional finance, AI mainly exists in the form of tools or assistants, able only to handle isolated tasks such as generating summaries or answering inquiries. Today’s AI finance is moving into the stage of “financial agents (Agent)”, where agents can autonomously break down tasks based on set goals, call tools, integrate data across systems, and continue interacting until delivering complete business results. This transformation—from “filling in locally” to “replacing across the board”—enables AI to be deeply embedded in the underlying layer of core businesses such as credit approval, investment research and advisory, and claims settlement, achieving an automated closed loop of business processes.

IV. Shift in governance and regulatory focus: from “managing content” to “managing behavior and permissions” With the application of generative AI and financial agents, the risk profiles introduced by AI finance have diversified. Generative AI mainly changes the way financial information is produced; its governance focus lies in preventing “financial information pollution” caused by low-cost, large-scale false information. Financial agents, on the other hand, deeply intervene in the execution layer of financial behavior. As a result, regulatory focus shifts to “managing capabilities, permissions, and execution.” This requires financial institutions to clarify the decision-making authority and execution boundaries of agents, ensure that operations are traceable, that responsibility can be traced through, and to preserve inviolable human-in-the-loop red lines to address the challenge that formal authorization is hard to replace the duty of prudence.

V. Shift in commercial value and competitive barriers: from “technical computing power” to “data cognition” In traditional models, competition among financial institutions often depends on hardware investment or basic model capability. In the AI finance era, as large-model capabilities at the industry level become increasingly homogeneous, core competitiveness is no longer simply model computing power, but rather the depth at which an institution understands data and the thickness of its industry insight. Under the same computing power and Token consumption, the key differentiators will be the institution’s fine-grained operational ability for order flows, alternative data, and time-sensitive factors, as well as its professional knowledge system for striking a balance between compliance boundaries and personalized needs.

You previously mentioned specific scenarios like credit approval and quantitative trading—would you like me to pick one and elaborate on how AI can be “restrained” in relation to human nature?