Multi-dimensional financial risk early warning and dynamic management and control system based on AI drive

Abstract

The invention provides a multi-dimensional financial risk early warning and dynamic management and control system based on AI driving, and the system comprises a data collection and preprocessing module which is used for collecting internal and external multi-source heterogeneous financial data of an enterprise in real time, carrying out the cleaning, standardization and feature engineering processing, and generating a structured feature vector; the AI risk assessment module is used for constructing a multi-dimensional risk assessment model based on a federated learning framework, and the federated learning framework comprises a longitudinal federated learning sub-framework, a transverse federated learning sub-framework and a model optimization unit; according to the method, a self-adaptive feature alignment algorithm based on differential privacy is adopted, cross-mechanism feature distribution and semantic level alignment are automatically learned, a mapping relation does not need to be manually preset, the dynamic change of multi-source data can be quickly and accurately adapted, the feature alignment efficiency is greatly improved, and the accuracy of feature alignment is improved. Therefore, federal learning can more timely utilize multi-mechanism data to carry out risk assessment, and more timely financial risk early warning is provided for enterprises.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of financial risk early warning and control, in particular to an AI-driven multi-dimensional financial risk early warning and dynamic control system. BACKGROUND

[0002] In the digital era, enterprise financial risks are increasingly complex, and traditional management methods are difficult to cope with. The AI-driven multi-dimensional financial risk early warning and dynamic control system realizes intelligent management of financial risks through five modules: data collection, risk assessment, dynamic control, real-time monitoring and user interaction. However, existing technologies still have significant defects in practical application, especially in the vertical federated learning framework of the AI risk assessment module.

[0003] The data collection and preprocessing module in the prior art integrates enterprise internal and external multi-source heterogeneous data, and generates structured feature vectors through cleaning, standardization and feature engineering, providing high-quality data for subsequent analysis. The AI risk assessment module is based on a federated learning framework, combining graph neural networks and long short-term memory networks to analyze enterprise correlation and time series financial data, enabling joint training under cross-institution privacy protection. The dynamic control strategy generation module uses reinforcement learning to generate financing optimization, cost control and other strategies, dynamically adjusting enterprise financial decisions. The real-time monitoring and early warning module: through multi-level threshold early warning and closed-loop feedback mechanism, continuously optimizing risk assessment and control strategies. The user interaction module provides a visual interface, supporting risk display, parameter configuration and historical backtracking.

[0004] Despite the comprehensive system functions, there are the following problems in the feature alignment process of vertical federated learning: low feature alignment efficiency: the data fields, formats and granularity of different institutions differ greatly, and traditional rule-based alignment methods are difficult to adapt to dynamically changing data structures, resulting in large manual mapping workload and high maintenance cost, seriously affecting the efficiency of federated learning. High risk of privacy leakage: enterprise subject information may expose local feature distribution during feature alignment, violating privacy protection principles and even being exploited by competitors, threatening enterprise business security. Excessive communication overhead: secure multi-party computation requires frequent interaction of intermediate results, and the communication overhead increases exponentially as the data dimension increases, leading to long training time and difficulty in meeting real-time requirements, and being vulnerable to network delays. SUMMARY

[0005] In view of this, the present application proposes an AI-driven multi-dimensional financial risk early warning and dynamic control system to solve a series of technical problems faced by the vertical federated learning sub-framework of the AI risk assessment module in integrating enterprise subject information and invoice data from different institutions. The specific technical solution is as follows:

[0006] An AI-driven multi-dimensional financial risk early warning and dynamic management and control system, comprising a data acquisition and preprocessing module for real-time acquisition of enterprise internal and external multi-source heterogeneous financial data, and cleaning, standardization and feature engineering processing to generate structured feature vectors;

[0007] An AI risk assessment module, a multi-dimensional risk assessment model is constructed based on a federated learning framework, and the federated learning framework comprises a longitudinal federated learning sub-framework, a transverse federated learning sub-framework and a model optimization unit;

[0008] A dynamic management and control strategy generation module generates dynamic adjustment strategies using reinforcement learning algorithms;

[0009] A real-time monitoring and early warning module that monitors financial indicators in real time and triggers multi-level early warnings;

[0010] A user interaction module that provides a visual interface and parameter configuration functions;

[0011] A privacy-enhanced feature alignment module integrated into the longitudinal federated learning sub-framework to address the low efficiency and privacy leakage of cross-institutional data feature alignment;

[0012] The modules are connected through a data bus, the output of the privacy-enhanced feature alignment module is connected to the model training unit of the AI risk assessment module, and real-time aligned cross-institutional feature data is provided, and validation logs are synchronized to the audit tracking module.

[0013] Further, the privacy-enhanced feature alignment module comprises: a differential privacy perturbation unit that injects Laplace noise into each institution's local data to generate anonymized feature vectors; a cross-institutional feature mapping unit that constructs an unsupervised feature space alignment model based on a deep generative adversarial network (GAN), learns the target institution's feature distribution through a generator, and distinguishes between real data and generated data through a discriminator; a semantic-level alignment unit that dynamically identifies core fields (such as "transaction counterpart taxpayer identification number" and "product code") using an attention mechanism and performs semantic-level alignment; a hierarchical communication optimization unit that divides the feature alignment process into local preprocessing, regional aggregation and global coordination layers, realizes regional aggregation of lightweight feature summaries through secure multi-party computation, and constructs a cross-institutional feature mapping dictionary; and a homomorphic encryption verification unit that homomorphically encrypts the aligned feature vectors, verifies statistical consistency through zero-knowledge proof, and records blockchain evidence;

[0014] Further, the differential privacy perturbation unit first completes the implementation process by a noise injection subunit, which adds noise to sensitive fields in the original data based on the Laplace mechanism, thereby breaking the strong association between individual data and output. The Laplace mechanism introduces a random variable to perturb the value of the sensitive field, and its core formula is:

[0015]

[0016] where x represents the original field value, a Laplace distribution random variable with 0 as the center and scale of the function on adjacent data sets, and epsilon represents the privacy budget used to control the strength of privacy protection. The definition of sensitivity Delta f is as follows:

[0017] Delta f = max || f (D1) - f (D2) || 1;

[0018] where D1 and D2 are data sets that differ by only one record, f (·) is the query function to be released, and || · || 1 represents the L1 norm. The privacy budget epsilon controls the strength of the injected noise, and the smaller the value means the stronger the protection but the larger the disturbance. To solve the problem of uneven noise caused by the difference in sensitivity of different fields and data dimensions, the dynamic privacy budget allocation subunit dynamically adjusts the budget value epsilon i of each field i according to the importance score w i and the frequency p i of each field i, and optimizes it through a weighted allocation mechanism:

[0019]

[0020] Further, the cross-institution feature mapping unit performs unsupervised feature alignment based on a deep generative adversarial network. First, the generator network receives the feature vector of the source institution as input, learns the mapping relationship of the target institution feature distribution through a multi-layer fully connected neural network, and outputs the simulated generated target feature vector. The training objective of the generator is to make the output data have high distribution similarity in the target domain, so as to deceive the discriminator. The discriminator network adopts a convolutional neural network structure, receives real target institution data and generated data, outputs a binary classification result, and is used to judge whether the input is from the real target domain, and further outputs an alignment confidence score. The training of the generator and the discriminator is updated iteratively through an adversarial optimization mechanism, and the objective function is defined as:

[0021]

[0022] where G is the generator, D is the discriminator, x ~ P target represents the real data distribution of the target institution, and z ~ P sourceThe characteristic distribution of the source institution is represented, G(z) is the simulation target data output by the generator, and D(x) and D(G(z)) represent the confidence outputs of the discriminator for real and generated data, respectively. To further improve the accuracy of field-level semantic alignment, a multi-head attention mechanism is embedded in the generator to build a semantic attention subunit, which automatically identifies and focuses on the cross-domain correspondence of key semantic fields. The multi-head attention mechanism models multiple semantic dimensions through parallel calculation of different attention weights, and its calculation formula is:

[0023]

[0024] where Q, K, and V are query, key, and value matrices, respectively, d k is the key vector dimension, used for scaling to maintain stability. The generator uses the attention output to weight and fuse field semantic features during training, so that the generated results retain stronger semantic consistency in key fields. Finally, the adversarial training optimizer combines the loss functions of the generator and the discriminator, and uses the back propagation and gradient descent method to jointly optimize the parameters, dynamically approaching the optimal mapping function, so that the generated features approach the target institution data distribution in distribution and semantics, thereby realizing the unsupervised feature space alignment between the source and target institutions.

[0025] The hierarchical communication optimization unit includes a local preprocessing layer, where each participating institution deploys an independent data processing node to perform cleaning, format standardization, and preliminary alignment of feature fields on the original data, ensuring consistency of feature dimensions and semantic structure. After processing, the institution passes the perturbed feature summary to the regional aggregation layer to which it belongs. The regional aggregation layer is organized according to the sub-federal structure of industry or region, and uses secure multi-party computation methods to encrypt and aggregate the feature summaries uploaded by each institution within the sub-federal structure, generating regional-level statistics including mean and covariance matrix. During the regional aggregation process, each institution participates in aggregation after local perturbation processing, and the regional feature mean calculation formula is: i

[0026]

[0027] where μ represents the statistical mean of a field within a region, x i is the feature value of the i-th institution, and n is the number of institutions within the sub-federal structure.

[0028] Based on this, the regional covariance matrix calculation is:

[0029]

[0030] ​Here, Σ represents the feature covariance matrix, used to measure the correlation structure between fields. This process is completed through multi-party encrypted sharing, ensuring that participants can achieve collaborative computation without disclosing their local plaintext data. Regional-level statistical results are sent to the global coordination layer. The coordination layer receives the feature indexes and statistical matrices uploaded by all sub-federations and constructs a cross-institutional feature mapping dictionary based on the global field occurrence frequency, covariance structure, and semantic labels. This dictionary is used to identify the correspondence and mapping weights between fields. Based on this, instructions for fields that need to be supplemented or reconstructed are sent back to each institution to guide the secondary enhancement and completion of local data, thereby achieving closed-loop alignment. Through the design combining a hierarchical architecture and encrypted computation, this unit effectively improves the alignment efficiency and structural integrity of multi-source data while ensuring privacy isolation between institutions, providing a stable and consistent feature foundation for subsequent AI risk assessment model training.

[0031] Furthermore, the homomorphic encryption verification unit achieves privacy protection and consistency confirmation of the aligned feature vectors through encrypted computation and a verifiable mechanism. First, the homomorphic encryption processor encrypts the feature vectors that have completed cross-institutional alignment using the Paillier encryption algorithm. This algorithm possesses additive homomorphic properties, enabling the addition operation in joint training to be completed without decryption. The Paillier encryption process is defined as follows: for the plaintext feature value m, ciphertext c is generated under the public key (n, g), and its calculation formula is...

[0032] c = g m ·r n mod n 2 ;

[0033] Where g is a system parameter, and r is a parameter... The value is randomly selected from the key, where n is the product of two large prime numbers used in key generation, and c is the encryption result.

[0034] The encrypted ciphertext supports ciphertext addition, meaning that for two ciphertexts c1 = E(m1) and c2 = E(m2), the following holds:

[0035] E(m1)·E(m2)mod n 2 =E(m1+m2);

[0036] This feature is used to calculate the encrypted features without decryption during the model training phase. Subsequently, the zero-knowledge proof verifier verifies whether the encrypted features meet the statistical consistency required by the training model through an interactive proof protocol, without revealing any direct information about the plaintext features. The verifier implements range verification and mean variance verification using the Sigma protocol or the Bulletproofs protocol to prove that the feature encryption vector satisfies the statistical distribution constraints. To ensure the non-repudiation and auditability of the entire alignment and verification process, the blockchain storage subunit writes the parameters generated at each stage to the blockchain after they are summarized, including the mapping dictionary index used in the feature alignment process, the differential privacy noise amplitude, the Paillier encryption public key summary, and the intermediate hash of the zero-knowledge proof process. By invoking the distributed consensus mechanism, the system generates a block storage record with a timestamp and a node signature, effectively preventing post-facto tampering and forgery, and forming a complete audit log chain, providing a verifiable and traceable privacy protection mechanism for cross-institutional collaboration.

[0037] The privacy-enhanced feature alignment module forms a sequential collaboration relationship among the sub-units in the vertical federated learning sub-framework. First, the differential privacy perturbation unit completes the anonymization of the local sensitive features, outputting a feature vector perturbed by the Laplace mechanism. This output is directly passed to the cross-institutional feature mapping unit as input for the training process of the generative adversarial network. Ensuring that the input is available for distribution mapping learning under privacy conditions, the perturbation form is defined as:

[0038]

[0039] where x i is the original feature value, η i is a noise variable following the Laplace distribution, Δf is the field sensitivity, and ε is the privacy budget. After receiving the perturbed vector, the cross-institutional feature mapping unit performs spatial mapping and structural matching of the fields between different institutions through the generative adversarial network. In the generator output, semantic attention mechanisms are integrated to extract semantic feature weights of key fields such as taxpayer identification numbers and commodity codes. The semantic vector output is processed by the attention mechanism and then subjected to feature compression to construct a feature representation in the unified semantic space across institutions. This result is transmitted through a hierarchical communication optimization unit, first completing feature summary aggregation within the sub-federation, and then uploading to the global coordination layer. The global coordination layer aggregates the semantic alignment vectors and statistics output by each sub-federation, constructs a mapping dictionary through a feature index table, and judges field missing or alignment deviation. Based on regional mean and covariance calculations, feedback correction is made to the global alignment state. The mean vector is defined as

[0040]

[0041] wherein The disturbance vector submitted by each institution, and mu and Sigma are the global statistical mean and covariance matrix respectively. Finally, the homomorphic encryption verification unit receives the alignment result output by the global coordination layer, performs Paillier encryption processing on the result to generate ciphertext feature data And perform zero-knowledge proof protocol to verify that it meets the consistency requirements of the input distribution of the federated learning model. After verification, the homomorphic encryption ciphertext and the corresponding proof record are fed back to the federated server for vertical joint model training, and the disturbance parameters and verification hash are recorded in the blockchain to ensure the integrity and traceability of the audit process. This tandem working mechanism realizes the privacy-enhanced and semantically unified collaborative federated training process in the vertical data island structure.

[0042] The above technical scheme has the following beneficial effects:

[0043] The adaptive feature alignment algorithm based on differential privacy can automatically learn the cross-institution feature distribution and semantic-level alignment without manually presetting the mapping relationship, can quickly and accurately adapt to the dynamic changes of multi-source data, greatly improves the efficiency of feature alignment, enables the federated learning to more timely utilize multi-institution data for risk assessment, and provides more timely financial risk early warning for enterprises. In each link of feature alignment, strict privacy protection measures are taken from data disturbance, encryption to verification. Differential privacy disturbance makes it difficult to infer the original data, homomorphic encryption ensures the security of data in the transmission and verification process, blockchain evidence ensures the traceability and tamper resistance of the privacy protection process, and the risk of privacy leakage is reduced from all aspects to protect the sensitive business data of enterprises. The communication optimization strategy of hierarchical aggregation processes the feature alignment process in layers, each layer only transmits necessary feature indexes and statistics, avoiding the transmission of a large amount of original data, effectively reducing the communication overhead, reducing the time cost of joint training, improving the running efficiency of the system, enabling the system to run efficiently under limited network bandwidth conditions, meeting the real-time requirements of enterprises, and solving the key problem of cross-institution data feature alignment, the technical scheme of the present application improves the performance of the AI risk assessment module, and further improves the accuracy and reliability of the entire financial risk early warning and dynamic control system. More accurate risk assessment can help enterprises discover potential financial risks in time, develop more effective control strategies, enhance the risk response ability of enterprises, and improve the financial management level and competitiveness of enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0044] Fig. 1 is a system overall operation flowchart of an AI-driven multi-dimensional financial risk early warning and dynamic control system of the present application;

[0045] Fig. 2A system signaling diagram of an AI-driven multi-dimensional financial risk early warning and dynamic management and control system according to the present application;

[0046] Fig. 3 A privacy-enhanced feature alignment module architecture diagram of an AI-driven multi-dimensional financial risk early warning and dynamic management and control system according to the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0048] Embodiment 1, see Figs. 1-3 An AI-driven multi-dimensional financial risk early warning and dynamic management and control system shown in the figure includes a data acquisition and preprocessing module for real-time acquisition of internal and external multi-source heterogeneous financial data of an enterprise, and cleaning, standardization and feature engineering processing to generate a structured feature vector;

[0049] An AI risk assessment module constructs a multi-dimensional risk assessment model based on a federated learning framework, and the federated learning framework includes a longitudinal federated learning sub-framework, a horizontal federated learning sub-framework and a model optimization unit;

[0050] A dynamic management and control strategy generation module generates a dynamically adjusted strategy using a reinforcement learning algorithm;

[0051] A real-time monitoring and early warning module monitors financial indicators in real time and triggers multi-level early warning;

[0052] A user interaction module provides a visual interface and parameter configuration function;

[0053] A privacy-enhanced feature alignment module integrated in the longitudinal federated learning sub-framework is used to solve the problems of low cross-institutional data feature alignment efficiency and privacy leakage;

[0054] The modules are connected through a data bus, the output end of the privacy-enhanced feature alignment module is connected to the model training unit of the AI risk assessment module, real-time aligned cross-institutional feature data is provided, and verification logs are synchronized to the audit tracking module.

[0055] Embodiment 2, based on embodiment 1, see Figs. 1-3As shown, the semantic attention subunit in the embodiment is a key mechanism for improving field alignment accuracy in the cross-institution feature mapping unit, and its implementation process includes three stages of field embedding, similarity calculation, and attention allocation. First, in the field embedding layer, core semantic fields such as "taxpayer identification number" and "commodity code" are mapped to word vectors. Each field is mapped to a high-dimensional vector with consistent dimensions through an embedding matrix, denoted as e i ∈R d where d represents the embedding space dimension, and the embedding process can be expressed as e i =Wx i where W ∈ R d×n is a training parameter matrix, and x i ∈R n is a sparse vector representation of the field. After completing the embedding, the similarity calculation layer measures the semantic correlation of the same type of fields in different institutions. The cosine similarity is used to calculate the correlation between two field embedding vectors ei and ej, defined as

[0056]

[0057] where · represents the inner product of vectors, and ||·|| represents the L2 norm of vectors. The result value ranges from 0 to 1, reflecting the semantic closeness between fields. The similarity result is passed to the attention weight allocation layer, which normalizes the similarity between all field pairs through the Softmax function and allocates the alignment attention weight α ij of each pair of fields. The weight calculation formula is

[0058]

[0059] where α ij represents the alignment attention between fields i and j, and k iterates through all candidate alignment fields. The higher the weight, the closer the fields are in the semantic space, and the system prioritizes their matching relationship in alignment mapping. The final generator takes the semantic mapping result of the high-weight field as the key reference, implements precision alignment of cross-institution features, and provides semantic enhancement information for subsequent global mapping construction. This mechanism not only strengthens the semantic collaboration between heterogeneous structures, but also improves the alignment credibility and stability of high-value fields in federated learning.

[0060] The zero-knowledge proof verifier uses the zk-SNARK protocol to verify that the aligned feature distribution meets the statistical consistency requirements without revealing the plaintext information. The verification process includes three stages of statement generation, proof generation, and verification. First, the statement generator constructs a mathematical proposition for verification, aiming to prove that the aligned encrypted feature distribution is consistent with the global statistical distribution in the variance index. Let the encrypted feature vector be and its statistical variance satisfy the relationship:

[0061]

[0062] where μ denotes the mean of the disturbed feature, σ 2 The relationship is constructed by the circuit input constraint as a zero-knowledge proof proposition by the proof generator. Subsequently, the proof generator encrypts the feature value based on the Paillier homomorphic encryption characteristics, and converts the above statistical calculation process into a constraint system based on R1CS (Rank-1 Constraint System), and generates a concise zk-SNARK proof π through a trusted setting. The proof does not contain the original feature value, but only represents the logical path and calculation trajectory of the proposition. Finally, the verifier receives the encrypted feature data and the proof π, and verifies that it satisfies the following conditions through the public key and the circuit verification function V:

[0063] V(π, pk, inputs) = true;

[0064] where pk is the preset verification public key, and inputs is the public input data digest, including the mean, the disturbance amplitude and the blockchain digest hash. If the verification is passed, it means that the cross-institution aligned features meet the variance consistency constraint at the statistical level without revealing any original data of the participating institutions. All intermediate hashes and verification paths in the verification process are written into the blockchain at the same time, ensuring the traceability and non-repudiation of the verification process, providing a trusted input basis and compliance support for vertical federated learning training.

[0065] The high-quality aligned feature data output by the privacy-enhanced feature alignment module is synchronized and distributed to the knowledge graph unit in the user interaction module, the dynamic control strategy generation module and the horizontal federated learning sub-framework as the core data source, supporting cross-dimensional risk modeling and response mechanisms in the whole system. First, the output data is divided into corresponding industry subspaces as the data input of cross-industry horizontal federated learning after field semantic unification, statistical consistency verification and homomorphic encryption protection of the structured feature vector. The federated server aggregates different institutions' isomorphic fields with weights, improving the model's ability to identify industry common risk factors, and its aggregation form is defined as

[0066]

[0067] where is the aligned vector output by the kth institution, w k is the weight assigned to the institution, satisfying ∑w k= 1. Second, the feature alignment data is synchronized into the policy simulation unit in the dynamic control strategy generation module. The unit integrates a reinforcement learning model to simulate various market fluctuation scenarios by inputting the feature state set, iteratively generate optimal response action strategies under different interest rate changes, exchange rate shocks or industry default events, and evaluate their stability and robustness. The policy value function is

[0068]

[0069] where s t is the current feature state, r t+k is the income obtained by the strategy at future time t+k, and γ ∈ (0, 1) is the discount factor reflecting the importance of future income. Finally, the aligned data is also used to build the cross-institution knowledge graph in the user interaction module. The system uses the trading counterparties, contract amounts and time nodes in the aligned fields as the semantic basis for entities and edges, uses embedded graph neural network models to automatically extract potential risk propagation paths, and depicts complex graph relationships such as tax number association paths, industry linkage risk nodes and time chain transmission structures in the graph to form a multi-layered financial risk graph view, realizing risk visualization, traceability and decision support interfaces for regulators and enterprise users. The triple synchronization mechanism ensures that the feature alignment results form a consistent, effective and interpretable data foundation in all modules of the system, ensuring a closed-loop linkage from training to control to interaction.

[0070] Parameter setting: In the adaptive feature alignment algorithm based on differential privacy, first determine the relevant parameters for noise addition. For the addition of Laplace noise, set the scale parameter λ of the Laplace noise according to the sensitivity of the data and the desired privacy protection strength. For example, for the equity structure data in the enterprise subject information, since it is highly sensitive, set λ to a relatively large value to enhance privacy protection; for some low-sensitivity fields in the invoice data, appropriately reduce λ to minimize the impact of noise on data usability while ensuring privacy. In the deep generative adversarial network (GAN), set the network structure parameters of the generator and discriminator, such as the number of layers and the number of neurons in each layer. Typically, the generator and discriminator can use a multi-layer perceptron (MLP) structure, for example, the generator is set to 3 layers with 256, 128 and 64 neurons respectively; the discriminator is also set to 3 layers with 64, 128 and 1 neurons respectively. At the same time, set the training hyperparameters, such as the learning rate of 0.0001 and the training rounds of 500 rounds. These parameters can be adjusted according to the actual size and complexity of the data.

[0071] Operation steps: In the vertical federated learning initialization stage, each participating institution performs differential privacy perturbation on local data. Taking numerical data as an example, according to the Laplace mechanism, for the original data x, add noise noise, noise obeys Laplace distribution L(0, λ), get the perturbed data y = x + noise. After completing the data perturbation, input the anonymized feature vector of the source institution into the generator of the generative adversarial network (GAN). The generator generates a feature vector similar to the target institution's feature by learning the target institution's feature distribution; the discriminator distinguishes between the generated feature vector and the real feature data of the target institution. In the training process, the generator and the discriminator constantly oppose each other, the generator tries to generate more realistic feature vectors to deceive the discriminator, and the discriminator constantly improves its recognition ability. After multiple rounds of training, the feature vector generated by the generator gradually approaches the real feature of the target institution in the feature space, realizing unsupervised cross-institution feature space alignment. Introduce attention mechanism, calculate the attention weight of the core fields such as "transaction counterpart taxpayer identification number" and "commodity code" in tax invoice in feature alignment. For example, by calculating the correlation, frequency and other factors of these fields with other fields, dynamically allocate attention weight, perform semantic-level alignment on core fields, and improve the accuracy of feature alignment.

[0072] Application scenario: In the cooperation of financial risk assessment between enterprises, different enterprises act as participating institutions, and their financial data contains a large amount of sensitive information. For example, enterprise A and enterprise B participate in federated learning, and the financial statement data of enterprise A and the transaction record data of enterprise B need to be aligned. Through the adaptive feature alignment algorithm based on differential privacy, enterprise A performs differential privacy perturbation on its own financial statement data, protecting sensitive information such as cost structure and profit distribution; then use GAN for feature mapping, realize the feature alignment with enterprise B's transaction record data, so that on the premise of protecting data privacy, the data of both parties can be integrated to provide support for more accurate financial risk assessment.

[0073] Implementation process in communication strategy implementation: In actual cross-institutional data transmission and processing, the layered aggregated communication optimization strategy is implemented in the following process. Each institution completes data cleaning locally, removes noise data such as error values and duplicate values in the data, performs anonymization processing such as hash processing or replacement on sensitive fields such as enterprise name and customer information, and basic feature alignment, such as unifying time format to ISO 8601 standard format, normalizing numerical data to the range [0, 1], etc., to generate a lightweight feature summary. Divide several sub-federations according to industry or region, for example, in the financial industry, divide into bank sub-federation, securities sub-federation, etc. Within the sub-federation, each institution completes preliminary feature alignment through secure multi-party computation. Each institution only uploads the aligned feature index and statistics such as mean, covariance, etc. to the regional aggregation layer, rather than the original data. The regional aggregation layer processes these feature indexes and statistics to complete the preliminary feature alignment within the sub-federation. The global coordination layer collects the feature indexes uploaded by each sub-federation to construct a cross-institutional feature mapping dictionary. By analyzing the relationship between the feature indexes, the missing feature fields of each institution are deduced to form a closed-loop alignment instruction, which is issued to each institution for further feature alignment operation. For example, the global coordination layer finds that some institutions lack the "tax rate" feature field in the invoice data, and instructs the relevant institutions to supplement the field to achieve complete feature alignment.

[0074] Device configuration: In the local preprocessing layer, the devices of each institution mainly include local servers equipped with sufficient computing resources and storage resources to complete data cleaning, anonymization, and basic feature alignment, etc. The configuration of the server can be selected according to the data size and processing requirements of the institution, for example, for large enterprises with large data volume, high-performance multi-core servers with large memory and fast storage devices can be configured. In the regional aggregation layer, regional aggregation servers are set up to be responsible for data aggregation and preliminary feature alignment within the sub-federation. The regional aggregation server needs to have strong computing and network communication capabilities to process a large amount of feature index and statistical data, and to interact efficiently with the local servers of each institution. In the global coordination layer, a global coordination server is deployed to construct a cross-institutional feature mapping dictionary and issue alignment instructions. The global coordination server needs to have strong computing and storage capabilities, as well as stable network connection, to ensure that it can process a large amount of data from multiple sub-federations and send alignment instructions to each institution in a timely and accurate manner.

[0075] Data interaction mode: between the local preprocessing layer and the regional aggregation layer, the local servers of each institution upload the generated lightweight feature summary, feature index and statistical quantity to the regional aggregation server through a secure network connection. Between the regional aggregation layer and the global coordination layer, the regional aggregation server uploads the feature index preliminarily aligned within the sub-federation to the global coordination server, and the global coordination server processes and issues closed-loop alignment instructions to the regional aggregation server, which is then forwarded to the local servers of each institution. In the entire data interaction process, encryption transmission protocols such as SSL / TLS are used to ensure the security and integrity of data transmission.

[0076] Verification mechanism implementation, encryption operation: after feature alignment, each institution performs homomorphic encryption on the locally aligned feature vector. Taking the Paillier homomorphic encryption algorithm as an example, each institution first generates a pair of keys, including a public key and a private key. The public key is used to encrypt each element in the feature vector, converting the plaintext feature vector into ciphertext form. For example, for an element x in the feature vector, encryption is performed through the encryption function E(x) to obtain the ciphertext c = E(x), so that even if the data is obtained by a third party, the real feature value cannot be obtained.

[0077] Verification process: after the federal learning server receives the encrypted feature vectors uploaded by each institution, it verifies that the "aligned feature space meets the statistical consistency required for joint training" through zero-knowledge proof technology to each institution. The server first calculates the statistical quantities of the encrypted feature vectors, such as mean, variance, etc. These calculations are performed on ciphertext without decryption. Then, the server proves to each institution through the zero-knowledge proof protocol that these statistical quantities meet the conditions required for joint training without revealing the specific feature values. For example, the server can construct a proof to convince each institution that the mean of the encrypted feature vector is within a certain range and consistent with the statistical quantities of the feature vectors of other institutions, but will not disclose the specific value of the mean.

[0078] Evidence storage operation: introduce blockchain to record the algorithm parameters, perturbation amplitude and verification process of feature alignment. Each institution packages key information such as the noise parameter of differential privacy perturbation, the key parameter of homomorphic encryption, and the verification result of zero-knowledge proof into a transaction record during the feature alignment process. Send the transaction record to the blockchain network, and the nodes in the blockchain network verify and consensus the transaction, add the verified transaction record to the block of the blockchain, forming an unalterable audit log. Any modification to the feature alignment process will be recorded by the blockchain and can be traced back, ensuring the transparency and security of the entire process, while also facilitating subsequent auditing and regulation.

[0079] The above describes the basic principles and main features of the present application, and those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application, and various changes and improvements can be made without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application, and the scope of protection is defined by the appended claims and their equivalents.

Claims

1. An AI-driven multi-dimensional financial risk early warning and dynamic management and control system, characterized in that, The data acquisition and preprocessing module is used for real-time acquisition of multi-source heterogeneous financial data inside and outside the enterprise, and performs cleaning, standardization and feature engineering processing to generate a structured feature vector. The AI risk assessment module is based on a federal learning framework to build a multi-dimensional risk assessment model, and the federal learning framework includes a longitudinal federal learning sub-framework, a horizontal federal learning sub-framework and a model optimization unit. The dynamic control strategy generation module uses a reinforcement learning algorithm to generate a dynamically adjusted strategy. The real-time monitoring and early warning module monitors financial indicators in real time and triggers multi-level early warning. The user interaction module provides a visual interface and parameter configuration function. The privacy-enhanced feature alignment module is integrated into the longitudinal federal learning sub-framework to solve the problems of low efficiency and privacy leakage in cross-institutional data feature alignment. The privacy-enhanced feature alignment module includes a differential privacy perturbation unit that injects Laplace noise into each institution's local data to generate an anonymized feature vector; a cross-institutional feature mapping unit that builds an unsupervised feature space alignment model based on a deep generative adversarial network (GAN), learns the target institution feature distribution through a generator, and distinguishes between real data and generated data through a discriminator; a semantic-level alignment unit that dynamically identifies core fields using an attention mechanism for semantic-level alignment; a hierarchical communication optimization unit that divides the feature alignment process into local preprocessing, regional aggregation and global coordination layers, and realizes regional aggregation of lightweight feature summaries through secure multi-party computation and constructs a cross-institutional feature mapping dictionary; and a homomorphic encryption verification unit that homomorphically encrypts the aligned feature vector, verifies statistical consistency through zero-knowledge proof, and records blockchain evidence.

2. The AI-driven multi-dimensional financial risk early warning and dynamic management and control system according to claim 1, characterized in that, The differential privacy perturbation unit first completes the implementation process by a noise injection subunit that adds noise to sensitive fields in the original data based on the Laplace mechanism to break the strong association between individual data and output. The Laplace mechanism introduces a random variable to perturb the sensitive field value, and its core formula is: 3.The AI-driven multi-dimensional financial risk early warning and dynamic management and control system according to claim 2, characterized in that, Delta f = max || f (D1) - f (D2) || 1. where x represents the original field value, represents a Laplace distribution random variable with 0 as the center and scale ; Δf represents the global sensitivity of the function on adjacent data sets, and ε represents the privacy budget used to control the strength of privacy protection, and the sensitivity Δf is defined as follows: The cross-institutional feature mapping unit performs unsupervised feature alignment based on a deep generative adversarial network. First, the generator network receives the feature vector of the source institution as input, learns the mapping relationship of the target institution feature distribution through a multi-layer fully connected neural network, and outputs the simulated target feature vector. The training objective of the generator is to make the output data have high distribution similarity in the target domain, thereby deceiving the discriminator. The discriminator network uses a convolutional neural network structure to receive real target institution data and generated data, outputs a binary classification result to distinguish whether the input comes from the real target domain, and further outputs an alignment confidence score. The training of the generator and the discriminator is updated iteratively through an adversarial optimization mechanism, and the objective function is defined as: Where D1 and D2 are data sets that differ by only one record, f(·) is the query function to be released, ||·||1 represents the L1 norm, the privacy budget ε controls the strength of the injected noise, the smaller the value means the stronger the protection, but the larger the disturbance, in order to solve the problem of uneven noise caused by different field sensitivity and data dimension difference, the dynamic privacy budget allocation subunit allocates the budget according to the importance score w of the field i And the frequency of occurrence p i Adjust the budget value ε of each field i dynamically i Optimized through a weighted allocation mechanism:

4. The AI-driven multi-dimensional financial risk early warning and dynamic management and control system according to claim 2 or 3, characterized in that, ​ where G is the generator, D is the discriminator, x ~ P target Ptargetrepresents the real data distribution of the target institution, z ~ P source Psource represents the feature distribution of the source institution, G(z) is the simulated target data output by the generator, and D(x) and D(G(z)) represent the confidence outputs of the discriminator on real and generated data, respectively.

5. The AI-driven multi-dimensional financial risk early warning and dynamic management and control system according to claim 4, characterized in that, In the generator, a multi-head attention mechanism is embedded to construct a semantic attention subunit, which automatically identifies and focuses on the cross-domain corresponding relationship of key semantic fields. The multi-head attention mechanism models multiple semantic dimensions through parallel calculation of different attention weights, and its calculation formula is: Wherein, Q, K, V are query, key, value matrix respectively, d k is the key vector dimension, used to scale the stability, the generator uses the attention output to weight and fuse the field semantic features during the training process, so that the generated results retain stronger semantic consistency on the key fields. Finally, the loss function of the generator and the discriminator is optimized by the adversarial training optimizer, and the parameters are optimized by the combination of back propagation and gradient descent method, and the optimal mapping function is dynamically approximated, so that the generated features approach the target mechanism data distribution in distribution and semantics, thereby realizing the unsupervised feature space alignment between the source and target mechanisms.

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