Brokerage AI Investment Advisory: From Tool "Involution" to Value Reconfiguration

[Introduction] The Current Status of AI Wealth Management: From Tool “Involution” to Value Reconstruction

China Fund News Reporter Sun Yue Zhao Xinyi

When AI meets wealth management, is it “showing off skills” or “real service”?

After experiencing a build-up of functions and homogeneous competition, brokerage firms’ AI wealth management is undergoing a profound value reconstruction. Interviews with multiple brokerage firms reveal that they are no longer satisfied with AI merely serving as a simple “stock selection tool,” but are embedding it throughout the entire wealth management chain, exploring a leap from “tool thinking” to “service thinking.”

Several business leaders from brokerage firms mentioned that for AI wealth management to break free from “involution” and achieve value reconstruction, the key lies in shifting from “tool empowerment” to “ecosystem building,” completing the transformation from “people seeking services” to “services seeking people.”

Key Leap

Regarding the current development stage of brokerage firms’ AI wealth management, interviewed institutions believe that AI wealth management has surpassed early pilot explorations and is undergoing a critical leap from tools to ecosystems.

A relevant person from Guolian Minsheng Securities defined the current phase as “the growth stage of AI wealth management 2.0.” “The fundamental change lies in the transition of technology from simple algorithms to a driving model centered on large models and intelligent agents; services are upgraded from fragmented Q&A to deep collaboration across the entire lifecycle; organizational logic shifts from ‘machines replacing humans’ to ‘machines enhancing humans’; and business philosophy transitions from competition for traffic to comparisons of data capabilities, customer retention, and long-term account outcomes.”

He Linjie, head of AI quantitative at First Capital Securities’ wealth management department, believes that brokerage firms’ AI wealth management has entered a key transformation period of scaling application and deepening business models. Leading brokerage firms have achieved breakthroughs in customer outreach and service coverage with AI wealth management, but most firms are still in the process of transitioning from “tool-based” to “service-based.”

From practical results, some brokerage firms have initially built AI wealth management service systems covering multiple core scenarios, making progress in user scale and service depth.

He Linjie introduced that First Capital has invested in several AI technology applications for wealth management tools, covering stock selection indicators, hot trend tracking, quantitative strategies, and more, primarily serving stock trading scenarios.

Guolian Minsheng Securities explained that their AI wealth management has evolved into a service system covering the entire process of client asset allocation, using a hybrid architecture of “large model understanding needs + professional configuration engine generating solutions + rule-based risk control and manual review.”

A relevant person from GF Securities reported that since the launch of their AI wealth management, they have implemented core service scenarios such as “AI selecting good stocks,” “AI optimizing holdings,” “AI investing in ETFs,” and “AI selecting good funds,” building a “strategy + service” deeply integrated buy-side advisory service system around investment pain points such as “what to buy, when to buy, when to sell, and what to do with held assets.”

It is evident that brokerage firms’ layout of AI wealth management is always centered around their wealth management business transformation. A relevant person from Huabao Securities stated that the original intention of laying out AI wealth management is to “provide customers with better service,” leveraging AI to enhance service efficiency, broaden service radius, achieve timely responses, and implement differentiated strategies of “thousands of faces,” while empowering wealth management advisors to improve service skills.

What Is the Value of AI Wealth Management?

When it comes to AI wealth management, an essential question arises: Is it useful during market downturns?

Every significant market fluctuation tests the “practical ability” of AI wealth management. During market adjustments, some investors complain about AI signals malfunctioning, while others use it as a tool to hedge emotions. The industry must confront the profound question— is AI wealth management truly a “safe haven” or a “truth mirror”?

A relevant person from Guolian Minsheng Securities put forth a distinct viewpoint: The core value of AI wealth management is not in predicting the market or completely avoiding drawdowns, but in keeping risks within the client’s budget and preventing clients from making irrational decisions driven by panic as much as possible.

In his view, AI cannot predict “black swan” events. Models are trained on historical data, and their effectiveness inevitably diminishes during significant structural changes in the market. More critically, when it comes to substantial reallocation decisions, reliance on the professional judgment of licensed advisors is still necessary. Therefore, he sees AI clearly as an “efficiency amplifier” for risk management and client service, rather than a “predictor” of the market.

He explained that Guolian Minsheng’s system mainly employs three mechanisms to cope with market downturns: first, proactive defense, which focuses on constructing risk-adaptive portfolio structures; second, in-process monitoring, which real-time tracks multiple risk indicators such as volatility, correlation, concentration, and liquidity, setting thresholds for observation, warning, and response; third, emotional reassurance and companionship. “During market fluctuations, AI can provide market interpretations, portfolio insights, and behavioral suggestions based on clients’ holdings and risk attributes, which is crucial for stabilizing client expectations.”

He Linjie candidly stated that the shortcoming of AI wealth management lies in that most models are trained on historical data, with limited adaptability to extreme market conditions and structural changes. Even if signals are accurate, the uncertainty remains regarding whether users will execute them and how they will do so. “A significant drawdown could even lead to a permanent loss of user trust.”

A relevant person from GF Securities introduced their triple risk control mechanism: “AI wealth management has established a three-fold risk control system encompassing market timing, stock screening, and stop-loss parameters.” In extreme market downturns, its core value lies in “reducing opening signals and triggering rapid stop-loss mechanisms to avoid greater loss risks.”

Not a Replacement, but Redefined Division of Labor

In the wave of AI technology deeply empowering wealth management, how “AI + human” collaborates has become one of the core issues.

This proposition arises from the industry’s understanding of the boundaries of AI capabilities. Although large model technology has demonstrated remarkable abilities in natural language processing and data mining, in investment decision-making—a field heavily reliant on professional judgment, emotional management, and trust building—AI has inherent limitations. Consequently, “replacement theory” gradually gives way to “collaboration theory,” with “human-machine symbiosis” becoming a consensus in the industry.

Guolian Minsheng Securities adheres to the core principle of allowing AI to focus on data processing, efficiency enhancement, and execution discipline, while human advisors focus on emotional connection, complex judgment, personalized decision-making, and ultimate compliance risk control. The two are deeply integrated in market responses, with AI serving as a “super external brain” and “execution arm,” while humans fill the gaps in complex judgment and accountability.

A relevant person from GF Securities believes that “AI + human” is not a simple division of functions but a structural reshaping of wealth management service capabilities. On the client side, AI achieves stable, objective standard outputs, while humans focus on macro interpretation and emotional communication; on the employee side, AI facilitates efficient responses and professional consistency across business services. The core lies in the reconstruction of service boundaries: AI raises the lower limit of professional service capabilities, while human advisors elevate the upper limit of service experience.

Fundamental Shift in Thinking and Evaluation Criteria

In the face of homogeneous “involution” in the industry, brokerage firms generally believe that the key to value reconstruction lies in a fundamental shift in thinking and evaluation criteria.

A relevant person from Huabao Securities stated that for AI wealth management to break free from “involution” and achieve value reconstruction, the key is to shift from “tool empowerment” to “ecosystem building,” completing a paradigm revolution from “people seeking services” to “services seeking people,” constructing a service matrix driven by intelligent agents to achieve deep personalized decision-making empowerment and promoting wealth management teams to transform into composite roles such as “AI strategy trainers” and “human-machine collaborative solution designers.”

In He Linjie’s view, the breakthrough point lies in the transition from “tool thinking” to “service thinking,” from “strategy supply” to “user value,” which includes: deepening client segmentation, constructing a differentiated service system, and no longer creating “general-purpose tools”; moving from “single advisory service” to “account-level allocation,” providing asset allocation suggestions based on client accounts to enhance service depth.

A relevant person from Guolian Minsheng Securities believes that the key lies in shifting from “pursuing functional showiness” to “being responsible for client account outcomes and long-term experiences.” Future business competition should focus on three aspects: the rationality of asset allocation, the robustness of response to fluctuations, and the persistence of long-term investment.

He proposed three breakthrough points: first, shifting from tool thinking to account thinking, connecting the entire service process of “demand insight - allocation planning - continuous tracking - fluctuation companionship - dynamic adjustment - review and optimization”; second, transitioning from traffic indicators to operational indicators, where evaluation standards shift from dialogue volume, click-through rates, to deep operational indicators such as client retention rates, investment adherence rates, service asset net inflow, drawdown control, and client satisfaction; third, deepening from “replacing human labor” to “human-machine collaboration,” based on the essence of trust in wealth management, allowing AI to be responsible for enhancing service efficiency and consistency, while human advisors focus on professional judgment, accountability, and communication at critical junctures.

Editor: Du Yan

Proofreader: Wang Yue

Production: Xiao Mo

Review: Xu Wen

Note: The cover image of this article was generated by AI.