Asset management method and system based on AIGC digital human technology

Abstract

The invention provides an asset management method and system based on an AIGC digital human technology. The method comprises the following steps: a step of detailed description of multi-source data collection and preprocessing; a step of detailed description of AIGC digital human model construction; a step of detailed description of personalized customer portrait generation; a step of detailed description generated by a dynamic asset allocation strategy; a step of detailed description of risk modeling and assessment; and carrying out detailed description of real-time monitoring and self-adaptive adjustment. According to the method, a dynamic and personalized asset management ecosystem is constructed through ten logically coherent steps. And each step is on the premise of the output of the previous step, so that the continuity and operability of the process are ensured. The AIGC digital human technology serves as the core, a virtual asset manager is created by means of generative AI, and natural language interaction, sentiment analysis and adaptive learning are achieved. The method focuses on solving the problems of insufficient personalization and inaccurate risk prediction, and improves the specialization level through complex statistics.

Description

Technical Field

[0001] This invention specifically relates to a method and system for asset management based on AIGC digital human technology. Background Technology

[0002] The asset management industry has gradually become digitalized in recent years with the development of fintech, but traditional methods still have many limitations. The industry currently relies primarily on human advisors, standardized algorithms, or simple automated tools for asset allocation, risk management, and customer service. These methods are typically based on historical data and static models, lacking dynamic adaptability and personalization. For example, many financial institutions use models based on Modern Portfolio Theory (MPT), but these models assume market efficiency and depend on historical volatility, failing to capture real-time market shifts or changes in customer behavior. Furthermore, customer interactions are mostly based on standardized reports or simple chatbots, making it difficult to provide in-depth, personalized advice.

[0003] Before this approach, the industry faced several major challenges: First, insufficient personalization; traditional models failed to fully integrate multidimensional customer data (such as risk preferences, life events, and emotional states), leading to generalized recommendations. Second, inaccurate risk prediction; existing statistical models, such as VaR (Value at Risk), often ignored tail risks and extreme events, and were outdated. Third, inefficiency; human advisors faced high costs and slow response times when handling large numbers of clients, while automated tools lacked emotional intelligence and struggled to build customer trust. Fourth, compliance challenges; increasingly complex regulatory requirements made it difficult for traditional systems to adapt to new regulations in real time. Fifth, limited learning capabilities; existing AI models were mostly based on supervised learning and could not continuously optimize themselves from new data. These challenges resulted in low asset management efficiency, low customer satisfaction, and vulnerability to significant losses during market fluctuations. Therefore, the introduction of AIGC digital human technology, combined with advanced statistics and real-time interaction, holds the promise of a revolutionary solution to these problems. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing an asset management method based on AIGC digital human technology, which can effectively solve the aforementioned problems.

[0005] To achieve the above requirements, the technical solution adopted by the present invention is: to provide a method for asset management based on AIGC digital human technology, which includes the following steps:

[0006] S1: A detailed description of the steps involved in multi-source data collection and preprocessing;

[0007] S2: A detailed description of the steps involved in constructing the AIGC digital human model;

[0008] S3: Steps for generating a detailed description of personalized customer profiles;

[0009] S4: Steps for generating a detailed description of dynamic asset allocation strategies;

[0010] S5: Steps for detailed description of risk modeling and assessment;

[0011] S6: A detailed description of the steps for real-time monitoring and adaptive adjustment;

[0012] S7: Detailed description of the steps for personalized customer interaction and report generation;

[0013] S8: A detailed description of the steps involved in system performance optimization and model updates;

[0014] S9: A detailed description of the steps involved in performing automated compliance checks;

[0015] S10: Steps for a detailed description of continuous learning and ecosystem integration.

[0016] The advantages of this asset management method based on AIGC digital human technology are as follows:

[0017] First, it enhances personalization through dynamic customer profiling and emotional interaction, increasing customer loyalty and satisfaction. Second, it strengthens risk management, with sophisticated statistical models accurately predicting extreme events, reducing portfolio volatility and maximum drawdown. Third, it improves operational efficiency, with automated processes reducing human intervention, lowering costs, and supporting large-scale deployment. Fourth, it ensures compliance, with real-time regulatory adaptation reducing legal risks. Fifth, it fosters continuous innovation, with self-learning mechanisms ensuring methodologies constantly adapt to market changes. These effects collectively drive the asset management industry towards a smarter, more reliable, and sustainable future. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, use the same reference numerals to denote the same or similar parts. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 A schematic flowchart illustrating an asset management method based on AIGC digital human technology according to an embodiment of this application is shown. Detailed Implementation

[0020] To make the objectives, technical solutions and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments.

[0021] In the following description, references to "an embodiment," "an embodiment," "an example," "example," etc., indicate that the described embodiment or example may include a particular feature, structure, characteristic, property, element, or limitation, but not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Furthermore, the repeated use of the phrase "an embodiment according to this application," while possibly referring to the same embodiment, does not necessarily refer to the same embodiment.

[0022] For simplicity, certain technical features known to those skilled in the art are omitted in the following description.

[0023] According to one embodiment of this application, a method for asset management based on AIGC digital human technology is provided, such as... Figure 1 As shown, it includes the following steps:

[0024] Step S1: Detailed description of multi-source data collection and preprocessing

[0025] This step is the cornerstone of the entire system, with the core objective of establishing a comprehensive, clean, and usable data lake. Operationally, this is far more than simple data transfer; it's a highly engineered and intelligent process. First, data collection must cover five dimensions: customer dimension (including static data such as age and occupation, dynamic behaviors such as app clickstream and transaction frequency, and psychological characteristics such as risk preference profiles derived from specific questionnaires or social media analysis), market dimension (high-frequency trading data, macroeconomic indicators, and alternative data such as satellite imagery and supply chain information), sentiment dimension (sentiment trends and keywords extracted from news, research reports, and social media using NLP), compliance dimension (real-time updated global regulatory texts), and digital human interaction dimension (historical dialogue records and customer feedback ratings). In the preprocessing stage, differentiated strategies are adopted for different types of data: for numerical market data, wavelet transform is used for denoising instead of simple smoothing to preserve key fluctuation features; for unstructured text data, a BERT-based pre-trained model is used for named entity recognition and sentiment analysis to accurately extract events such as "a company CEO resigns" and their impact on market sentiment; for missing values, instead of simply using the mean to fill in the missing values, generative adversarial networks (GANs) are used to simulate the possible distribution of missing data and generate filler values ​​that are more in line with reality. The entire process is built on a cloud-native data pipeline, using Apache NiFi for data flow orchestration and Apache Spark for distributed processing. Data security and privacy are embedded in the design. All personally identifiable information (PII) is anonymized upon entry into the database and partial computation is performed in an encrypted state using homomorphic encryption. The output of this step is not a static data snapshot, but a continuously updated dynamic data grid with data lineage markers, providing a continuous and high-quality source of nourishment for the subsequent "cognition" of the digital human.

[0026] Step S2: Detailed description of AIGC digital human model construction

[0027] This step aims to create a digital human with financial expertise and emotional empathy. Its construction is not instantaneous but a layered, iterative training process. The bottom layer is a general-purpose large language model (such as LLaMA or GPT architecture), providing powerful language understanding and generation capabilities. The middle layer is a domain-specific adaptation layer, which undergoes supervised fine-tuning using massive amounts of financial text (such as listed company annual reports, brokerage research reports, and economic academic papers) to make the digital human proficient in financial terminology, logical reasoning (such as understanding that "interest rate hike cycles typically put pressure on growth stock valuations"), and compliant expression. The top layer is the personalization and interaction layer, which is where the digital human's "personality" resides. This is shaped through a reinforcement learning (RL) framework: the digital human, as an intelligent agent, acts by generating linguistic responses or investment advice for customers; its "state" is the current dialogue context and customer profile; and its "reward" is a comprehensive function including customer satisfaction survey scores, the completeness of the dialogue (whether the customer's problem was solved), and the ex-post risk-adjusted return of the investment advice. Through millions of simulated dialogues, the digital human learns which communication strategies to employ in different market environments and with different customer personalities (e.g., prioritizing emotional reassurance before explanation for risk-averse customers during market crashes). Simultaneously, the model integrates multimodal fusion capabilities, processing both trembling in the customer's voice (fear) and questions in their text, generating a unified, empathetic response. Ultimately, this digital human model is a dynamic and interpretable set of models, not merely a black box; its decision-making logic can be traced through a knowledge graph, laying the foundation for compliance.

[0028] Step S3: Detailed description of personalized customer profile generation

[0029] The goal of this step is to go beyond traditional KYC and create a dynamically evolving "digital twin customer." It leverages the deep reasoning capabilities of the digital human already developed in Step 2 to deeply mine the pristine data provided in Step 1. Operationally, it first discovers natural customer clusters through traditional machine learning clustering (such as using the DBSCAN algorithm), but more importantly, it introduces deep sequence models to characterize changes in customer behavior over time. For example, it uses Long Short-Term Memory (LSTM) networks to analyze a customer's transaction history over the past year to predict their next potential investment product. The profile is extremely rich, including: quantitative indicators (such as a dynamic risk score calculated based on historical trading profits and losses and portfolio volatility), psychological characteristics (such as "loss aversion coefficient" and "overconfidence level" calibrated through behavioral finance experiments), life cycle stage (predicting future cash flow needs based on disposable income, debt, and family structure), and investment competence (determining which industries the customer is familiar with based on their professional background and historical investment success rate). The digital human proactively initiates "cognitive interviews," filling data gaps by generating open-ended questions, such as, "Do you anticipate a major home purchase within the next three years?" The customer's answers are analyzed in real time and updated in the profile. Ultimately, each customer profile is a living entity, stored in a complex, timestamped attribute graph structure. Any external event (such as a market crash) or internal event (such as a change in a customer's life stage) will trigger the recalculation of its attributes, ensuring that the strategy generation in step 4 is always based on the latest and most comprehensive understanding of the customer.

[0030] Step S4: Detailed description of dynamic asset allocation strategy generation

[0031] In this step, the digital human acts as a tireless, data-driven chief investment officer. Based on the high-fidelity client profile output from step 3, it executes highly personalized asset allocation. Its core working principle is a deep modification and extension of the classic Black-Litterman model. The digital human first uses the market equilibrium return rate as a priori view, then uses its own generated "personal view" as a posterior adjustment. These personal views do not come from human analysts, but are formed by the digital human through analyzing sentiment data, macroeconomic models, and market technical indicators, simulating tens of thousands of market paths using Monte Carlo simulations to generate probability distributions of future returns for various assets. The allocation process is a multi-objective optimization problem, with the objective function simultaneously pursuing maximizing expected returns, minimizing risk (measured by CVaR), controlling downside risk, and maximizing alignment with client values ​​(e.g., excluding ESG-negative assets). The optimization algorithm employs multi-objective particle swarm optimization, which can find the Pareto optimal frontier and present clients with multiple allocation options under different risk-return trade-offs. The key innovation lies in the "adaptive rebalancing trigger." Instead of being based on a fixed timeframe (such as quarterly), it is based on a series of dynamic conditions: including portfolio weight drift exceeding a threshold, market volatility regime switching, significant changes in customer profiles, or the digital human predicting that short-term momentum in a particular asset class is about to exhaust. Once the trigger is activated, the digital human initiates a lightweight optimization process, fine-tuning the configuration weights to achieve truly "dynamic" management.

[0032] Step S5: Detailed description of risk modeling and assessment

[0033] This step embodies the methodology's expertise, aiming to penetrate the surface of traditional risk models and deeply capture the "fat tail" nature of financial markets. Operationally, the digital human executes a multi-layered and rigorous risk measurement process. First, it meticulously fits the distribution of the return series of each individual asset in the portfolio, using a family of GARCH models to accurately describe the time-varying nature and clustering of volatility. Then, the core lies in modeling the dependencies between assets—where traditional correlation coefficients fail in extreme cases. The digital human employs a time-varying Copula function, particularly the time-varying Student's-Copula, because it simultaneously captures non-linear correlations and tail correlations between assets (i.e., the probability of assets falling together significantly higher than usual during extreme market downturns). After calculating the overall loss distribution of the portfolio, the focus shifts to tail risk measurement. Here, extreme value theory is used specifically to model extreme losses at the tail of the distribution. Using a POT model, a high threshold is set, and all losses exceeding this threshold (i.e., "over-limit values") are modeled and fitted with a generalized Pareto distribution. This makes the estimation of the conditional value at risk more accurate and robust. The entire calculation process is completed in parallel by a high-performance computing cluster. The digital human can update the risk indicators of the entire portfolio multiple times a day or even within a day, and generate stress test and scenario analysis reports, which show in detail the potential performance of the portfolio under historical crises or hypothetical extreme scenarios, providing the most authoritative risk signals for the real-time monitoring in step 6.

[0034] This step builds upon the asset allocation strategy in Step 4, applying sophisticated statistics for professional risk quantification to address the issue of traditional risk models ignoring extreme events. Operationally, the digital human uses Extreme Value Theory (EVT) and Copula functions to construct a multivariate risk model. First, the marginal distribution of asset returns is calculated; then, a GARCH model is used to capture volatility clustering; and finally, a Copula function is used to model the inter-asset dependency structure. The working principle is based on the following statistical formula: Let the portfolio return be... ;

[0035] in It is the weight of asset i. It is its rate of return. Value at Risk (VaR) is calculated as follows: ,in It is the expected return of the portfolio. It is the standard deviation. It is the inverse function of the standard normal distribution. This is the confidence level (e.g., 95%). However, to capture tail risk, conditional value at risk (CVaR) is introduced:

[0036] The symbols have the following meanings:

[0037] At confidence level Value at risk (VaR) represents the probability that a loss will not exceed this value. .

[0038] Expected return of the portfolio, derived from historical data or digital predictions.

[0039] Standard deviation of portfolio returns measures volatility.

[0040] : The inverse cumulative distribution function of the standard normal distribution, used to transform quantiles.

[0041] Conditional Value at Risk (VaR) represents the average loss under conditions where the loss exceeds VaR, and is better suited to capturing extreme risks.

[0042] u: Integral variable, representing the probability level. In the operation, the digital human uses the POT (Peaks Over Threshold) method to extract extreme values ​​from the return series and fits a generalized Pareto distribution (GPD) to estimate CVaR. Simultaneously, a time-varying Copula (such as the Student-t Copula) is applied to model the nonlinear dependencies between assets, avoiding underestimation of correlations during market crises. The output is a risk report, including VaR, CVaR, and stress test results. This step is updated in real-time through high-performance computing to ensure timely warnings during market upheavals. Only after this step is completed can risk indicators be provided for step 6; otherwise, real-time monitoring lacks a basis.

[0043] Step S6: Detailed description of real-time monitoring and adaptive adjustment

[0044] This step is the system's "autonomic nervous system," responsible for translating the planning of previous steps into real-time actions. It constructs an event-driven, complex event processing architecture. Data streams (such as tick-level prices, news flashes, social sentiment indices, and macroeconomic data releases) are continuously injected through stream processing platforms like Apache Kafka. The digital human internally maintains a "market state machine" that determines the market's state in real time—"calm," "tense," "panic," or "collapse"—based on indicators such as volatility, liquidity, and correlation. For key risk indicators calculated in step 5, such as CVaR, a dynamic threshold is set. This threshold is not fixed but automatically adjusted according to the market state (in a "panic" state, the CVaR threshold triggering rebalancing decreases). When a trigger condition is detected, the digital human does not simply execute the complete optimization of step 4 (computationally expensive and potentially leading to overtrading), but instead initiates a reinforcement learning-based fine-tuning controller. This controller takes the current portfolio state and market state as input and outputs a set of small-scale weight adjustments (e.g., "reduce stock A by 0.5% and increase treasury bond futures by 0.5%"). Through long-term simulation training, it has learned to achieve risk control objectives with minimal transaction costs and market shocks. Simultaneously, the digital human instantly generates brief explanations of the reasons for adjustments and temporarily stores them in an interaction queue, awaiting communication with the client in step 7. This ensures the entire system is both responsive and robust.

[0045] Step S7: Detailed description of personalized customer interaction and report generation

[0046] This step directly reflects the value of the digital human, aiming to transform complex financial decisions into warm, trustworthy, and personalized communication. Based on the monitoring results and adjustments made in step 6, the digital human leverages its AIGC capabilities to dynamically generate multimodal interactive content. Its working principle is based on an advanced content generation framework: First, the planner determines the core message and logical structure of the communication (e.g., "explaining the market downturn -> informing about hedging measures already taken -> calming emotions -> reiterating long-term goals"). Then, the generator selects the most appropriate language style and media format based on the customer profile from step 3. For younger, tech-savvy customers, a short video with dynamic charts might be generated; for older customers, a clearly structured audio report with enlarged fonts might be generated. During generation, the digital human embeds specific evidence extracted from the data, such as "We noticed that the proportion of tech stocks in your portfolio exceeded the target by 3% due to recent gains, so we took some profits," which greatly enhances trust. Finally, a style transferor ensures the output content is in the desired tone (e.g., "professional yet caring"). All interactions are bidirectional. The digital human can understand the customer's follow-up questions and retrieve information from the knowledge graph to provide answers, forming a complete and valuable dialogue. The interaction records themselves become feedback data, flowing back to the data lake in step 1 for model optimization in step 8.

[0047] Step S8: Detailed description of system performance optimization and model update

[0048] This step ensures the system is "immune" and can continuously evolve. It is a closed-loop, data-driven optimization process. The digital human performs a comprehensive "check-up" on its performance regularly (e.g., monthly). Check-up metrics include: the portfolio's ex-post Sharpe ratio, maximum drawdown, and tracking error against the benchmark; customer satisfaction scores, interaction duration, and problem resolution rate; and model-level prediction accuracy. Optimization occurs at two levels: at the strategy level, the weight parameters of the multi-objective optimization function in step 4 are adjusted through backpropagation. For example, if recent portfolio volatility is found to be too high, the risk aversion coefficient is automatically increased. At the model level, online learning technology is used to incrementally update the digital human model using the latest interaction and market data, enabling it to quickly adapt to new market paradigms (e.g., switching from a low-interest-rate environment to a high-interest-rate environment). To avoid "catastrophic forgetting" of the model, elastic weight consolidation technology is used to protect the memory of important old knowledge (such as compliance rules) while learning new knowledge. The entire update process is A / B tested in a sandbox environment. Only after verification of effectiveness is it gradually released to the production environment to ensure the safety and stability of system evolution.

[0049] Step S9: Detailed description of automated compliance checks

[0050] This step elevates compliance from a post-event audit to an embedded, proactive intelligent safeguard. The digital human internally constructs a regulatory knowledge graph that automatically extracts entities (such as "insider trading" and "accredited investors") and relationships (such as "prohibited" and "mandatory disclosure") from massive amounts of regulatory documents using NLP technology, forming a machine-readable rule network. Automated checks are performed at three key nodes: Before a transaction, each proposed transaction is matched against the graph to check for violations of restrictive provisions (such as purchasing stock of companies on the sanctioned entity list). During a transaction, trading patterns are monitored in real time, and anomaly detection algorithms are used to identify potential violations, such as suspected market manipulation through "frequent order placement followed by rapid order cancellation." After a transaction, all generated client reports are reviewed to ensure they do not contain misleading statements or promises of returns. When a potential risk is detected, the system does not simply reject the transaction but initiates a "compliance inquiry" process: the digital human generates an inquiry report clearly pointing out suspicious points and their corresponding regulatory provisions, which is submitted to a human compliance officer for final adjudication. This improves efficiency while leaving the final decision-making power to humans, complying with regulatory requirements.

[0051] Step S10: Detailed description of continuous learning and ecosystem integration

[0052] As the highest stage of the methodology, this step aims to break down system boundaries and achieve symbiotic evolution with the broader financial ecosystem. The digital human is designed as an open "ecosystem participant." It seamlessly connects with external data sources (such as alternative data providers), research platforms (such as sell-side research report repositories), and trade execution systems through standardized APIs. More importantly, it possesses meta-learning capabilities—that is, "learning how to learn." When entering a new market (such as cryptocurrency) or encountering a new financial instrument, it can quickly identify knowledge patterns in that field and leverage existing relevant knowledge for transfer learning, significantly shortening the adaptation cycle. Furthermore, within the framework of federated learning, digital humans from multiple institutions can collaboratively train a more powerful model without sharing raw client data, thereby improving the risk management level and investment decision-making capabilities of the entire industry. Ultimately, this methodology is no longer a closed tool, but a living ecosystem capable of continuously drawing nourishment from the environment, self-improving, and driving the intelligent upgrade of the entire asset management industry.

[0053] According to one embodiment of this application, the asset management method based on AIGC digital human technology solves several key technical problems in the field of asset management, totaling over 300 words. First, it addresses the problem of insufficient personalization: traditional methods rely on static customer classification, while this method dynamically generates customer profiles through AIGC digital humans, integrating emotional and behavioral data to provide truly personalized advice, such as adjusting investment plans based on real-time life events, avoiding customer churn caused by a one-size-fits-all strategy. Second, it addresses the problem of inaccurate risk prediction: traditional models such as VaR ignore tail risk, while this method introduces extreme value theory and Copula functions to professionally quantify the risk of extreme events, such as providing early warnings through CVaR during market crashes, reducing huge losses. Third, it improves efficiency and scalability: traditional human advisors are costly and slow to respond, while this method, through automated interaction and monitoring by digital humans, can handle massive numbers of customers while maintaining low costs, such as completing rebalancing within seconds, adapting to high-frequency trading environments. Fourth, it enhances compliance and adaptability: traditional system updates lag behind regulatory changes, while this method automatically checks compliance through a real-time rule engine, reducing legal risks. Furthermore, this method addresses the issue of limited learning capacity by continuously learning and optimizing the model from new data, thus preventing it from becoming outdated. Overall, this method utilizes AIGC digital human technology to transform asset management from a reactive to a proactive predictive approach, significantly improving customer satisfaction, risk control, and operational efficiency, and driving the industry towards intelligent and human-centered development.

[0054] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. A method for asset management based on AIGC digital human technology, characterized in that, Includes the following steps: S1: A detailed description of the steps involved in multi-source data collection and preprocessing; S2: A detailed description of the steps involved in constructing the AIGC digital human model; S3: Steps for generating a detailed description of personalized customer profiles; S4: Steps for generating a detailed description of dynamic asset allocation strategies; S5: Steps for detailed description of risk modeling and assessment; S6: A detailed description of the steps for real-time monitoring and adaptive adjustment; S7: Detailed description of the steps for personalized customer interaction and report generation; S8: A detailed description of the steps involved in system performance optimization and model updates; S9: A detailed description of the steps involved in performing automated compliance checks; S10: Steps for a detailed description of continuous learning and ecosystem integration.

2. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S1 specifically includes: Data collection needs to cover five dimensions: customer, market, public opinion, compliance, and digital human interaction. In the preprocessing stage, differentiated strategies are adopted for different types of data: for numerical market data, wavelet transform is used for noise reduction instead of simple smoothing to retain key fluctuation features; for unstructured text data, a BERT-based pre-trained model is used for named entity recognition and sentiment analysis to accurately extract events and their impact on market sentiment; for missing values, instead of simply using the mean to fill in, generative adversarial networks are used to simulate the possible distribution of missing data and generate filling that is more in line with reality. The entire process is built on a cloud-native data pipeline, using Apache NiFi for data flow orchestration and Apache Spark for distributed processing. Data security and privacy are embedded in the design. All personal identity information is anonymized when entering the database and some calculations are performed in an encrypted state through homomorphic encryption technology.

3. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S2 specifically includes: The underlying general-purpose language model provides powerful language understanding and generation capabilities. The middle layer is a domain-specific adaptation layer, which, through supervised fine-tuning using massive amounts of financial text, enables the digital human to master financial terminology, logical reasoning, and compliant expression. The top layer is the personalization and interaction layer, shaped through a reinforcement learning framework: the digital human acts as an intelligent agent, generating linguistic responses or investment advice for customers. Its state is the current dialogue context and customer profile. The reward is a comprehensive function, including customer satisfaction survey scores, dialogue completion rates, and ex-post risk-adjusted returns on investment advice. Through millions of simulated dialogue training sessions, the digital human learns which communication strategies to use in different market environments and with different customer personalities. The model integrates multimodal fusion capabilities, simultaneously handling tremors in customer speech and questions in text, generating empathetic and unified responses.

4. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S3 specifically includes: Leveraging digital humans with deep reasoning capabilities, we conduct in-depth mining of clean data. First, we discover natural customer groups through traditional machine learning clustering. Then, we introduce deep sequence models to characterize changes in customer behavior over time. The profiles are extremely rich, including quantitative indicators, psychological characteristics, lifecycle stages, and the digital humans proactively initiate cognitive interviews, filling data gaps by generating open-ended questions. Customer answers are analyzed in real time and updated in the profiles. Each customer profile is a living entity, stored in a complex, timestamped attribute graph structure. Any external or internal event triggers the recalculation of its attributes, ensuring that strategy generation is always based on the latest and most comprehensive understanding of customers.

5. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S4 specifically includes: Based on high-fidelity customer profiles, highly personalized asset allocation is executed. This involves a deep modification and extension of the classic Black-Litterman model. The digital human first uses the market equilibrium return rate as a priori view, then uses its own generated private view as a posterior adjustment. This private view does not come from human analysts, but rather is formed by the digital human through analyzing public opinion data, macroeconomic models, and market technical indicators, simulating tens of thousands of market paths using Monte Carlo simulations to generate probability distributions of future returns for various assets. The allocation process is a multi-objective optimization problem, with the objective function simultaneously pursuing maximum expected return, minimum risk, control of downside risk, and maximum alignment with customer values. The optimization algorithm employs multi-objective particle swarm optimization, which can find the Pareto optimal frontier and present customers with multiple allocation schemes under different risk-return trade-offs. Instead of being based on a fixed time frame, it is based on a series of dynamic conditions: including portfolio weight drift exceeding a threshold, market volatility regime switching, significant changes in the customer profile, or the digital human predicting that the short-term momentum of a certain asset class is about to exhaust. Once the trigger is activated, the digital human initiates a lightweight optimization process, fine-tuning the allocation weights to achieve true dynamic management.

6. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S5 specifically includes: The Digital Human executes a multi-layered and rigorous risk measurement process. It performs detailed distribution fitting on the return series of each individual asset in the portfolio, uses GARCH family models to accurately describe the time-varying nature and clustering phenomenon of volatility, and focuses on modeling the inter-asset dependencies. This is where traditional correlation coefficients fail in extreme cases. The Digital Human uses a time-varying Copula function. After calculating the overall loss distribution of the portfolio, it shifts its focus to tail risk measurement. Extreme value theory is used to specifically model the extreme losses at the tail of the distribution. A high threshold is set, and all losses exceeding the threshold are modeled and fitted with a generalized Pareto distribution. The entire calculation process is completed in parallel by a high-performance computing cluster. The Digital Human can update the risk indicators of the entire portfolio daily or even multiple times a day, and generate stress test and scenario analysis reports, showing in detail the potential performance of the portfolio under historical crises or hypothetical extreme scenarios. Based on asset allocation strategies, this system applies complex statistics to professionally quantify risk, addressing the issue of traditional risk models ignoring extreme events. The digital human uses extreme value theory and Copula functions to construct a multivariate risk model, calculating the marginal distribution of asset returns, using a GARCH model to capture volatility clustering, and finally modeling the inter-asset dependency structure through Copula functions. Its working principle is based on the following statistical formula: Let the portfolio return be... ; in It is the weight of asset i. Its rate of return, calculated as: ,in With a confidence level of 95%, conditional value of risk is introduced to capture tail risk: ； The symbols have the following meanings: At confidence level Value at risk (VaR) represents the probability that a loss will not exceed this value. ; The expected rate of return of the asset portfolio is derived from historical data or digital predictions. The standard deviation of portfolio returns measures volatility. The inverse cumulative distribution function of the standard normal distribution, used to transform quantiles; Conditional Value at Risk (VaR) represents the average loss under conditions where the loss exceeds VaR, and is better suited to capturing extreme risks. u: Integral variable, representing the probability level; Digital Human uses the POT method to extract extreme values ​​from the return series, fits a generalized Pareto distribution to estimate CVaR, and applies time-varying Copula to model nonlinear dependencies between assets to avoid underestimating correlations during market crises. The output is a risk report that includes VaR, CVaR, and stress test results.

7. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S6 specifically includes: An event-driven complex event processing architecture was constructed. Data streams are continuously injected through stream processing platforms such as Apache Kafka. The digital human maintains a market state machine, which determines in real time whether the market is calm, tense, panicked, or collapsing based on indicators such as volatility, liquidity, and correlation. For the calculated key risk indicators, it automatically adjusts according to the market state. When a trigger condition is detected, the digital human does not simply perform a full optimization, but starts a fine-tuning controller based on reinforcement learning. This controller takes the current portfolio state and market state as input and outputs a set of small weight adjustment actions. Through long-term simulation training, it has learned to achieve risk control objectives with minimal transaction costs and market impact. The digital human will immediately generate a brief explanation of the adjustment reason and temporarily store it in the interaction queue.

8. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S7 specifically includes: Based on monitoring results and adjusted actions, the digital human dynamically generates multimodal interactive content using its AIGC capabilities. This is based on an advanced content generation framework: a planner determines the core information and logical structure of the communication, a generator creates customer profiles, and selects the most suitable language style and media format. For young, tech-savvy customers, it generates a short video with dynamic charts; for older customers, it generates a clearly structured audio report with enlarged fonts. During the generation process, the digital human embeds specific evidence extracted from the data. All interactions are bidirectional; the digital human can understand subsequent questions from customers and retrieve information from the knowledge graph to provide answers, forming a complete and valuable dialogue.

9. The asset management method based on AIGC digital human technology according to claim 1, characterized in that, Step S8 specifically includes: The digital human regularly undergoes comprehensive performance checks, with indicators including: the portfolio's ex-post Sharpe ratio, maximum drawdown, and tracking error relative to the benchmark; customer satisfaction scores, interaction duration, and problem resolution rate; and model prediction accuracy. Optimization is performed at two levels: at the strategy level, the weight parameters of the multi-objective optimization function are adjusted through backpropagation; at the model level, online learning technology is used to incrementally update the digital human model with the latest interaction and market data, enabling it to quickly adapt to new market paradigms. To avoid catastrophic forgetting of the model, elastic weight consolidation technology is used to protect the memory of important old knowledge while learning new knowledge. The entire update process is A / B tested in a sandbox environment, and only after verification of effectiveness is it released to the production environment in a canary manner to ensure the safety and stability of system evolution. Step S9 specifically includes: Internally, a regulatory knowledge graph has been built. This graph uses NLP technology to automatically extract entities and relationships from massive amounts of regulatory documents, forming a machine-readable rule network. Automated checks are performed at three key nodes: Before a transaction, each proposed transaction is matched against the graph to check for violations of restrictive provisions; during a transaction, transaction behavior patterns are monitored in real time, and anomaly detection algorithms are used to identify potential violations; all generated customer reports are reviewed to ensure they do not contain misleading statements or promises of benefits; when a potential risk is detected, a compliance inquiry process is initiated: the digital human generates an inquiry report, clearly pointing out the suspicious points and their corresponding regulatory provisions, and submits it to a human compliance officer for final adjudication.

10. Step S10 specifically includes: Digital humans are designed as open ecosystem participants, seamlessly connecting with external data sources, research platforms, and trading execution systems through standardized APIs. They possess meta-learning capabilities, meaning they learn how to learn. When entering a new market or facing a new financial instrument, they can quickly identify knowledge patterns in that field and leverage existing relevant knowledge for transfer learning, significantly shortening the adaptation cycle. Under the framework of federated learning, digital humans from multiple institutions can jointly train a more powerful model without sharing original customer data, thereby improving the risk management level and investment decision-making capabilities of the entire industry.

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