AI-based digital value-added service method for credit and debt demand analysis

Abstract

The invention discloses an AI-based digital value-added service method for claim and debt demand analysis, and the method comprises the steps: receiving a claim and debt original file set uploaded by a client, and generating a structured clause element set which comprises clause types, core elements and expression mode features; generating a risk quantification label of each key term based on the structured term element set; inputting the structured clause element set and the risk quantification label into a pre-trained clause value-risk collaborative optimization model, and generating a group of optimization clause revision suggestion sets for maximizing a user set value target on the premise of risk controllability; revising the suggestion set for the optimization terms, and generating a feasible revision strategy sequence sorted according to the adaptation degree; and based on the feasible revision strategy sequence, determining a final term revision scheme and matched risk slow release measure suggestions, and taking the final term revision scheme and the matched risk slow release measure suggestions as output of the digital value-added service. According to the embodiment of the invention, the accuracy and decision-making efficiency of credit and debt management can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of AI, in particular to a digital value-added service method for creditor-debtor demand analysis based on AI. BACKGROUND

[0002] With the complication of creditor-debtor relationship, the traditional manual auditing method has obvious deficiencies in efficiency, accuracy and risk prediction. Existing digital tools focus on text recognition or static risk assessment, lacking the ability of deep deconstruction of clause semantics, dynamic risk quantification and value-risk collaborative optimization. Especially in cross-industry and multi-scenario applications, the potential risks of clauses are strongly related to market environment, counterparty credit and other factors, and the existing technology is difficult to realize real-time data-driven dynamic analysis. In addition, contract revision suggestions usually rely on experience rules, lack of interactive reasoning and personalized adaptation based on AI, resulting in insufficient executable solutions. SUMMARY

[0003] The purpose of the application is to provide a digital value-added service method for creditor-debtor demand analysis based on AI, to solve the deficiencies in the prior art and improve the accuracy and decision-making efficiency of creditor-debtor management.

[0004] One embodiment of the present application provides a digital value-added service method for creditor-debtor demand analysis based on AI, which comprises:

[0005] Receiving a set of creditor-debtor original files uploaded by a client, performing semantic deconstruction and element extraction on key clauses in the files through a deep semantic parser based on adversarial training, and generating a structured clause element set containing clause type, core element and expression manner characteristics;

[0006] Based on the structured clause element set, calling a dynamically associated industry risk event library, performing clause potential risk probability calculation and risk impact degree assessment processing according to the similarity matching of element characteristics and risk event cases, and generating risk quantization labels for each key clause;

[0007] Inputting the structured clause element set and its risk quantization labels into a pre-trained clause value-risk collaborative optimization model, performing multi-round iterative optimization simulation processing according to the current clause element state and the preset target, and generating a set of optimization clause revision suggestions that maximize the user's set value target under the premise of risk controllability, wherein the collaborative optimization model is based on a reinforcement learning framework, and is trained by simulating the acceptance of counterparty to clause modification and the final execution result under different negotiation strategies;

[0008] The optimization clause revision suggestion set is combined with the real-time acquired counterparty public credit image and market benchmark data, and is subjected to personalized adaptation degree scoring processing through a lightweight dynamic matching engine to generate a feasible revision strategy sequence sorted by adaptation degree;

[0009] Based on the feasible revision strategy sequence, interactive counterfactual reasoning is performed, and the customer is visually shown the debt performance probability change curve and the expected loss and gain distribution under a preset external scenario after adopting different revision strategies, and a final clause revision scheme and supporting risk mitigation measure suggestion are determined according to the user feedback selection, serving as the output of the digital value-added service.

[0010] Optionally, the creditor's rights and debts original file set uploaded by the customer is subjected to semantic deconstruction and element extraction of key clauses in the file through a deep semantic parser based on adversarial training to generate a structured clause element set containing clause type, core element, and expression manner features, including:

[0011] Multi-modal adversarial feature extraction is performed on the creditor's rights and debts original file set uploaded by the customer to obtain an adversarial enhanced feature vector set that fuses text semantics and document layout;

[0012] According to the adversarial enhanced feature vector set, clause boundary detection is performed through a sequence labeling model driven by a bidirectional attention mechanism to obtain a preliminary clause segmentation graph;

[0013] The graph convolution network is applied to model the element relationship of the preliminary clause segmentation graph to generate a clause element relationship graph;

[0014] The semantic parser optimized through adversarial training is applied to deconstruct the clause element relationship graph to generate a structured clause element set.

[0015] Optionally, based on the structured clause element set, a dynamically associated industry risk event library is called to perform clause potential risk probability calculation and risk impact degree assessment processing according to the similarity matching of element features and risk event cases to generate a risk quantization label for each key clause, including:

[0016] According to the structured clause element set, heterogeneous graph embedding representation learning is performed to obtain a multi-dimensional vector representation of the clause elements;

[0017] The dynamic risk event library is called to perform event similarity graph matching on the multi-dimensional vector representation of the clause elements to obtain a similar risk event case set;

[0018] Based on the similar risk event case set, Bayesian-Monte Carlo risk probability modeling is performed to obtain a potential risk probability value;

[0019] According to the potential risk probability value and the event case influence data, multi-dimensional risk influence fusion evaluation is performed to generate a risk quantization label of each key clause.

[0020] Optionally, the structured clause element set and its risk quantization label are input into a pre-trained clause value-risk collaborative optimization model, and multi-round iteration optimization simulation processing is performed according to the current clause element state and the preset target to generate a set of optimization clause revision suggestion sets that maximize the user-set value target under the premise of controllable risk, wherein the collaborative optimization model is based on a reinforcement learning framework, and is trained by simulating the acceptance of the opposite party to the clause modification and the final execution result under different negotiation strategies, including:

[0021] According to the structured clause element set and its risk quantization label, an multi-objective reinforcement learning state space is initialized to obtain an initial strategy space;

[0022] According to the initial strategy space, a double-agent generative adversarial network is used to simulate an adversarial negotiation process to obtain an acceptance prediction value of the opposite party;

[0023] According to the acceptance prediction value of the opposite party, value-risk Pareto frontier optimization is performed to obtain a set of alternative revision scheme prototypes;

[0024] The set of alternative revision scheme prototypes is filtered by a risk threshold constraint to screen out a preliminary optimization suggestion set;

[0025] According to the preliminary optimization suggestion set, an optimization clause revision suggestion set is generated through multi-round strategy gradient reinforcement learning iteration.

[0026] Optionally, the optimization clause revision suggestion set is combined with the real-time acquired public credit portrait of the opposite party and market benchmark data to perform personalized adaptation degree scoring processing through a lightweight dynamic matching engine to generate a feasible revision strategy sequence sorted by adaptation degree, including:

[0027] The optimization clause revision suggestion set is executed for distributed feature vectorization to obtain a revision suggestion feature matrix;

[0028] According to the revision suggestion feature matrix and the real-time credit portrait of the opposite party, dynamic weighted vector similarity calculation is performed to obtain a credit adaptation degree score;

[0029] The credit adaptation degree score and the market benchmark data are integrated to run a scenario-aware weight optimization algorithm to calculate a weighted comprehensive adaptation degree score;

[0030] According to the weighted comprehensive adaptation degree score, strategy priority is sorted to obtain a preliminary sorting sequence;

[0031] The lightweight dynamic matching engine is applied to the preliminary sorting sequence for real-time feedback optimization to output a feasible revision strategy sequence.

[0032] Optionally, interactive counterfactual deduction is conducted based on the sequence of feasible revision strategies to visually present to the customer the debt performance probability change curve and expected profit and loss distribution under preset external scenarios after adopting different revision strategies. Based on the user's feedback on the deduction results, the final terms revision plan and supporting risk mitigation measures are determined as the output of the digital value-added service, including:

[0033] Perform multi-scenario counterfactual generation for a sequence of feasible revision strategies to create a set of deduction scenarios;

[0034] Based on the deduction scenario set, Monte Carlo simulation of debt performance probability is carried out to obtain probability change curve data;

[0035] Based on the probability change curve data and market benchmark data, dynamic modeling of expected profit and loss is carried out to obtain a profit and loss distribution chart;

[0036] Based on the profit and loss distribution chart and real-time user feedback, interactive decision optimization is carried out to determine the final terms revision plan and supporting risk mitigation measures.

[0037] Another embodiment of the present application provides a digital value-added service system for AI-based debt demand analysis, the system comprising:

[0038] The receiving module is used to receive the original debt and credit document set uploaded by the customer. It uses a deep semantic parser based on adversarial training to perform semantic deconstruction and element extraction on the key terms in the document, and generate a structured term element set containing the term type, core elements, and expression characteristics.

[0039] An evaluation module is used to call a dynamically associated industry risk event library based on the structured clause element set, calculate the potential risk probability of the clause and evaluate the risk impact based on the similarity between the element characteristics and the risk event cases, and generate a risk quantification label for each key clause;

[0040] An optimization module, configured to input the structured clause element set and its risk quantification labels into a pre-trained clause value-risk collaborative optimization model, perform multiple rounds of iterative optimization simulation based on the current clause element status and preset objectives, and generate a set of optimized clause revision suggestions that maximize the user-set value objectives while maintaining controllable risks. The collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the counterparty's acceptance of clause modifications and the final execution results under different negotiation strategies;

[0041] The matching module is used for performing personalized adaptation degree scoring processing on the optimization clause revision suggestion set in combination with the real-time acquired counterparty public credit image and market benchmark data through a lightweight dynamic matching engine, and generating a feasible revision strategy sequence sorted by adaptation degrees;

[0042] The determining module is used for performing interactive counterfactual deduction based on the feasible revision strategy sequence, visualizing a debt performance probability change curve and an expected loss and gain distribution under a preset external scenario after different revision strategies are adopted for a customer, and determining a final clause revision scheme and a supporting risk mitigation measure suggestion as an output of the digital value-added service according to feedback selection of the user on the deduction result.

[0043] Another embodiment of the present application provides a storage medium having a computer program stored therein, wherein the computer program is configured to execute the method described in any one of the above embodiments when running.

[0044] Another embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory has a computer program stored therein, and the processor is configured to execute the computer program to execute the method described in any one of the above embodiments.

[0045] Compared with the prior art, the digital value-added service method for AI-based creditor-debtor demand analysis provided by the present application receives a creditor-debtor original file set uploaded by a customer, generates a structured clause element set containing clause types, core elements and expression manner characteristics; based on the structured clause element set, generates a risk quantization label of each key clause; inputs the structured clause element set and the risk quantization label into a pre-trained clause value-risk collaborative optimization model to generate an optimization clause revision suggestion set that maximizes a user-set value target under the premise of controllable risk; generates a feasible revision strategy sequence sorted by adaptation degrees from the optimization clause revision suggestion set; and based on the feasible revision strategy sequence, determines a final clause revision scheme and a supporting risk mitigation measure suggestion as an output of the digital value-added service, thereby improving the accuracy and decision-making efficiency of creditor-debtor management. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A hardware structure block diagram of a computer terminal of the digital value-added service method for AI-based creditor-debtor demand analysis provided by the embodiment of the present application is provided;

[0047] Figure 2 A flowchart of the digital value-added service method for AI-based creditor-debtor demand analysis provided by the embodiment of the present application is provided;

[0048] Figure 3A structure schematic diagram of a digital value-added service system for AI-based creditor-debtor demand analysis provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application and cannot be explained as a limitation of the present application.

[0050] The embodiment of the present application first provides a digital value-added service method for AI-based creditor-debtor demand analysis, which can be applied to electronic devices such as computer terminals, specifically, common computers and the like.

[0051] The following will be described in detail taking a computer terminal as an example. Figure 1 A hardware structure block diagram of a computer terminal of a digital value-added service method for AI-based creditor-debtor demand analysis provided by an embodiment of the present application is shown in the figure. Figure 1 As shown in the figure, the computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory can include a non-volatile storage medium and an internal memory.

[0052] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, which, when executed, can make the processor execute any kind of digital value-added service method for AI-based creditor-debtor demand analysis.

[0053] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0054] The internal memory provides an environment for the running of the computer program in the non-volatile storage medium, which, when executed by the processor, can make the processor execute any kind of digital value-added service method for AI-based creditor-debtor demand analysis.

[0055] The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art can understand that Figure 1 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or less components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0056] It should be appreciated that the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0057] Referring to Figure 2 Embodiments of the present application provide an AI-based digital value-added service method for creditor's right and debt demand analysis, which can include the following steps:

[0058] S201, receiving a set of creditor's right and debt original files uploaded by a client, and performing semantic deconstruction and element extraction on key clauses in the files through a deep semantic parser based on adversarial training to generate a set of structured clause elements including clause types, core elements and expression manner characteristics;

[0059] Specifically, multi-modal adversarial feature extraction can be performed on the set of creditor's right and debt original files uploaded by the client to obtain a set of adversarial enhanced feature vectors that fuse text semantics and document layout;

[0060] Multi-modal data fusion processing

[0061] When the client uploads a set of original files (such as PDF contracts, scanned images, and electronic documents), the system first starts a multi-modal data fusion engine. The engine adopts a dual-channel feature extraction architecture:

[0062] Text semantic channel: parse the literal content in the file through a pre-trained language model (e.g., BERT-Base, the base version of Bidirectional Encoder Representations from Transformers) to output a word embedding sequence (WES). Each word vector is a 768-dimensional floating-point array that captures the contextual semantics of the word.

[0063] Document Layout Channel: Analyze the visual structure of the file using a convolutional neural network (e.g., ResNet-50, a 50-layer version of the Residual Network). The input is the tiled regions of the document image (divided into 224x224 pixel blocks), and the output is a Patch Feature Vector (PFV) containing physical layout information such as table locations, seal regions, paragraph indents, etc.

[0064] The outputs of the two channels are aligned through a Cross-Modal Attention Fusion Module (CMAFM): the text word vectors and the corresponding image block features are used to calculate attention weights (AW), which range from 0 to 1 and reflect the strength of the text-image association. For example, if the textual description of a "penalty clause" is located in the lower right corner of the contract with a seal, its text-image attention weight can reach 0.92, while the weight of the title text in the header may only be 0.15.

[0065] Adversarial training enhances robustness

[0066] To improve the adaptability of features to fuzzy scanned documents and handwritten annotations, the system introduces a Generative Adversarial Training Framework (GATF):

[0067] Generator (Gen): Receives noisy original features (e.g., adds Gaussian noise with a standard deviation of 0.1) and attempts to reconstruct the fused feature vector. The generator structure is a 5-layer Fully Connected Neural Network (FCNN) with 1024, 512, 256, 128, and 64 neurons in each layer.

[0068] Discriminator (Dis): Determines whether the input feature comes from the original data or the generator's reconstructed data. The discriminator uses a 3-layer Convolutional Neural Network (CNN) with a 3x3 kernel size and a stride of 1.

[0069] Both are trained alternately by Minimax Game (MMG): the generator aims to minimize the recognition accuracy of the discriminator (targeting 50%), while the discriminator maximizes the accuracy (targeting 100%). After 200 training epochs, the system outputs the Adversarial Enhanced Feature Vector Set (AEFVS), each vector with a dimension of 1024, which integrates robust representations of semantics and layout.

[0070] Dynamic feature normalization and compression

[0071] To address the feature scale differences caused by different file sizes, the system performs Dynamic Range Normalization (DRN):

[0072] Calculate the mean (μ) and standard deviation (σ) of all feature vectors, and perform a linear transformation on each vector: (vector value - μ) / σ, so that the data distribution conforms to the standard normal distribution (mean 0, standard deviation 1).

[0073] Reduce the dimensionality to 256 dimensions through Principal Component Analysis (PCA), retaining 95% of the original information (determined by the cumulative contribution rate of eigenvalues). Finally, generate a unified scale Adversarial Enhanced Feature Vector Set for downstream processing.

[0074] According to the Adversarial Enhanced Feature Vector Set, perform clause boundary detection through a sequence labeling model driven by a bidirectional attention mechanism to obtain a preliminary clause segmentation graph;

[0075] Sequence labeling model construction

[0076] The system uses Bidirectional Long Short-Term Memory (BiLSTM) as the basic architecture, which captures context dependencies through Forward LSTM (F-LSTM) and Backward LSTM (B-LSTM):

[0077] The input layer receives the Adversarial Enhanced Feature Vector Set (a sequence of 256-dimensional vectors), and the Time Step (TS) is equal to the number of words in the file.

[0078] The F-LSTM processes the sequence from left to right, with a hidden layer dimension of 128; the B-LSTM processes the sequence from right to left, with a hidden layer dimension of 128. The outputs of both are concatenated into a 256-dimensional state vector (SV) at each step.

[0079] Attention mechanism drives boundary recognition

[0080] Superimpose the Self-Attention Mechanism (SAM) on the BiLSTM output layer:

[0081] For each time step, the state vector is used to calculate the query vector (QV), key vector (KV), and value vector (VV), all of which have a dimension of 64.

[0082] The attention weight is calculated using the Scaled Dot-Product Attention (SDPA) formula: Weight = Softmax( (Q·K^T) / √64 ), where √64=8 is the scaling factor.

[0083] Output a weighted value vector sequence to highlight key position features (for example, the attention weight of the clause’s starting word “this agreement” can reach 0.85).

[0084] Conditional Random Field Optimization Label Sequence

[0085] Finally, the conditional random field (CRF) layer is used to generate the term boundary labels:

[0086] Define the tag set: B-Bound (start of clause), I-Bound (inside clause), O (non-clause).

[0087] CRF learns the rules for inter-label transitions (for example, B-Bound must be followed by I-Bound or O, and B-Bound is prohibited from jumping directly to O).

[0088] Outputs a Preliminary Clause Segmentation Map (PCSM), storing the start and end indexes (SI and EI) of each clause in JSON format. For example, if "payment terms" is detected to be between words 120 and 215,

[0089] Apply graph convolutional networks to model the element relationships of the preliminary clause segmentation graph and generate a clause element relationship graph;

[0090] Graph node and edge definition

[0091] Convert the clause segmentation graph into a heterogeneous graph structure (HGS):

[0092] Node (N): divided into three categories - clause subject (such as "Party A"), action predicate (such as "shall pay"), numerical constraint (such as "100 million yuan in RMB"). Each node is initialized with a word vector.

[0093] Edge (E): defined by syntactic dependency relations, including:

[0094] Subject-Verb (SV) relationship, weight 1.0;

[0095] Verb-Object (VO) relationship, weight 0.9;

[0096] Attribute-Modification (AM) relationship, weight 0.7;

[0097] Logical-Connection (LC) relationship, weight 0.8.

[0098] Graph convolutional network feature propagation

[0099] Use a two-layer graph convolutional network (GCN) for relationship modeling:

[0100] First layer GCN: input node feature dimension 256, output dimension 128. The aggregation function (AF) is the weighted average of the neighbor node features, and the weight is determined by the edge type. For example, when the "Party A" node aggregates the "shall pay" feature through the SV edge, the weight is taken as 1.0.

[0101] Second layer GCN: input dimension 128, output dimension 64. Introduce gating mechanism (GM): use Sigmoid function to calculate information retention probability (range 0-1), filter noise connections (such as AM edges separated by more than 5 nodes may be assigned a 0.2 retention probability).

[0102] Relationship graph generation and compression

[0103] Output clause element relation graph (CERG):

[0104] The node feature is a 64-dimensional vector, representing the semantic of the fused context relationship.

[0105] Pruning sparse edges: remove edges with weights lower than threshold 0.3 (e.g. weakly related LC edges).

[0106] Stored as Adjacency Matrix (AM) and Feature Matrix (FM), with size N x N and N x 64 (N is the total number of nodes).

[0107] The semantic parser optimized by adversarial training disassembles the clause element relationship graph and generates structured clause element sets.

[0108] Semantic parser architecture design

[0109] The semantic parser (SP) is based on a graph-to-sequence model (G2S):

[0110] Encoder: two layers of GCN are used to process the input relationship graph (CERG), outputting enhanced node vectors (ENV) with a dimension of 64.

[0111] Decoder: a unidirectional LSTM (Uni-LSTM) is used to generate structured fields step by step. The hidden layer dimension is 128, and the initial state is the average of all node vectors.

[0112] Adversarial training optimizes generalization ability

[0113] Domain adversarial training (DAT) is introduced to improve adaptability across different contract types:

[0114] Main task classifier: predicts the element type (e.g. clause type, core element, etc.), 3-layer fully connected network, output dimension equal to the number of label categories (e.g. 20 categories).

[0115] Adversarial task classifier: attempts to determine the input from which contract field (e.g. loan contract, supply chain finance contract), structure similar to the main task classifier.

[0116] During training, the gradient direction of the adversarial task is reversed through the gradient reversal layer (GRL): the main task minimizes the classification error (target error <5%), and the adversarial task maximizes the field classification error (target error >40%), forcing the encoder to generate field-independent features.

[0117] Structured Element Set Generation

[0118] Final Output: Structured Clause Element Set (SCES), contains three sub-structures:

[0119] Clause Type: Automatically categorized into predefined labels (e.g., "Payment Clause", "Default Liability Clause") with a Confidence Score (CS) ranging from 0 to 1.

[0120] Core Elements: Extract key entities (e.g., "USD 500,000", "30 business days") and associate their roles (e.g., "Creditor", "Debtor").

[0121] Expression Features: Record legal expression styles (e.g., "shall" vs "must"), ambiguity word frequencies (e.g., "reasonable period" occurrences), and other statistical features.

[0122] Data is stored in a structured JSON format, for example, the output of a "Late Payment Fee Clause" is:

[0123] {

[0124] "clause_type": "Default Liability Clause",

[0125] "core_elements": [

[0126] {"entity": "interest rate", "value": "0.05%", "role": "penalty standard"},

[0127] {"entity": "starting date", "value": "the next day after the due date", "role": "trigger condition"}

[0128] ],

[0129] "expression_features": {

[0130] "modal_verb": "shall",

[0131] "ambiguity_count": 0

[0132] }

[0133] }。

[0134] The method first processes the creditor's rights and obligations file uploaded by the customer through a deep semantic parser trained by adversarial training. The parser can identify and deconstruct the key clauses in the contract and convert them into structured data elements. Through adversarial training, the parser has stronger generalization ability and can accurately identify contract clauses of different formats and expressions, extract clause types, core rights and obligations elements, and unique expression characteristics, and convert unstructured contract text into standardized and computable structured data, laying a foundation for subsequent risk analysis and clause optimization. Adversarial training ensures that the parser has strong adaptability to various contract texts and avoids the limitations of traditional rule engines.

[0135] In S202, based on the structured clause element set, a dynamically associated industry risk event library is called, and according to the similarity matching of element characteristics and risk event cases, a clause potential risk probability calculation and risk impact degree evaluation processing is performed to generate a risk quantization label for each key clause.

[0136] Specifically, according to the structured clause element set, heterogeneous graph embedding representation learning can be performed to obtain a multidimensional vector representation of the clause elements.

[0137] The system receives the structured clause element set (SCES) generated in the previous step. This dataset contains three types of core information: clause type (such as payment conditions, liability for breach), core elements (such as interest rate value, penalty ratio), and expression characteristics (such as the absolute time limit expression in "should be paid within 30 days"). To capture the complex relationships between elements, a heterogeneous graph (HG) is constructed: each clause element is treated as a node (Node), and the node attributes include element type and feature value; the logical association between elements (such as the subordinate relationship between "interest rate" and "compound interest calculation"), and semantic similarity (such as the synonymous relationship between "compensation" and "reimbursement") are treated as edges (Edge). Graph neural network (GNN) is used for embedding learning: at the initialization stage, each node generates a 128-dimensional base vector (Base Vector, BV) through a word embedding layer (such as BERT); through a 3-layer graph convolutional network (Graph Convolutional Network, GCN), the neighbor node information is aggregated to learn the dependence between nodes. For example, the "penalty clause" node will aggregate the features of its associated "compensation ratio", "trigger condition", and other neighbor nodes. Finally, a 256-dimensional multidimensional vector representation (Multidimensional Vector Representation, MVR) is output, with dimensions 0-63 encoding clause type semantics, 64-127 encoding core element numerical characteristics, and 128-255 encoding expression implicit risk tendency.

[0138] To solve the information fusion problem caused by the diversity of edge types in the heterogeneous graph (such as logical association edges and semantic similarity edges), a meta-path attention mechanism is adopted. Key meta-path patterns are defined, such as "clause type -> core element -> expression method" (path type PT1) and "core element -> similar element -> historical case" (path type PT2). For each meta-path instance (such as a "interest rate clause" associated with the "annualized 5%" element through PT1), the attention weight (AW) between nodes is calculated, which is determined by learnable parameters and node feature similarity. For example, the PT2 path between the "floating interest rate" node and the "LIBOR benchmark" node has an AW of 0.9 due to high semantic relevance, while the PT1 path between the "floating interest rate" node and the "fixed mortgage" node has an AW of only 0.2 due to type differences. By weighted aggregation of neighbor information under different meta-paths, a path-aware vector (PAV) is generated. Finally, the base vector BV and all PAVs are concatenated, and a fully connected layer is used for dimensionality reduction and fusion, outputting an MVR that combines local structure and global semantics. This process is distributed on an NVIDIA DGX server cluster, and a single A100 GPU can handle a heterogeneous graph with 10,000 nodes.

[0139] To improve the robustness of the vector representation, adversarial regularization (AR) is introduced. During training, adversarial samples are constructed: random noise edges are added to the input graph (such as incorrectly associating the "installment payment" node with the "cross-border jurisdiction" clause), or node features are perturbed (such as replacing the interest rate value 5% with a missing value). By minimizing the vector distance (using cosine similarity constraints) between clean samples and adversarial samples, the model is forced to ignore irrelevant interference and focus on key features. After training, the MVR of each clause element satisfies the following characteristics: elements of the same type (such as "force majeure" clauses in different contracts) are close in vector space (Euclidean distance less than 0.3); high-risk elements (such as "no upper limit compensation") are significantly activated in specific dimensions (such as dimension 201) (scalar value greater than 0.8). This MVR will serve as the basis for subsequent risk matching.

[0140] Call the dynamic risk event library to perform event similarity graph matching on the multi-dimensional vector representation of the clause elements, obtaining a set of similar risk event cases;

[0141] The Dynamic Risk Event Library (DREL) stores three types of data: historical judicial cases (e.g., a real estate company triggers a chain of debt crises due to "cross-default clauses"), industry regulatory penalties (e.g., the CBRC fines "villainous clauses"), and market public default events (e.g., a company loses billions due to "exchange rate fluctuation clauses"). Each event in the library is abstracted as an "Event Graph" (EG): nodes represent event elements (e.g., parties involved, clause content, loss amount), and edges represent causal relationships between elements (e.g., "interest rate rises → cash flow breaks → default"). DREL accesses external data sources (e.g., Judgments, Central Bank Credit Investigation System) through an API interface and updates approximately 5,000 events daily. When receiving MVRs of clause elements, the matching engine performs Graph Similarity Matching (GSM) with the node vectors of all EGs in DREL.

[0142] GSM consists of two stages:

[0143] Node-level matching: Calculate the vector similarity between the MVR of the clause element to be analyzed and the nodes in the event graph. Use the improved DTW algorithm (Dynamic Time Warping, DTW) to treat the vector dimension as a time series, allowing local dimension misalignment comparison. For example, the DTW distance between the MVR of a "grace period clause" and the "delay trigger condition" node in the "A company debt delay event" is 0.15 (within the threshold of 0.2, it is considered a match).

[0144] Graph structure matching: For subgraphs with successful node matching, further calculate the Graph Edit Distance (GED). By adding / deleting edges, the minimum operation cost to make the analyzed clause graph isomorphic to the event subgraph is the GED value. For example, the GED of a "pledge guarantee clause" graph and a "B bank pledge devaluation event" graph is 2 (1 "devaluation rate correlation edge" needs to be added and 1 "irrelevant third party edge" needs to be deleted).

[0145] Combine the DTW distance (weight 0.6) and GED (weight 0.4) to generate the Comprehensive Similarity Score (CSS). Filter events with CSS > 0.7, generate the Similar Risk Event Case Set (SRECS) in descending order of score, and return an average of 12 cases per query.

[0146] To improve efficiency, a hierarchical indexing strategy is adopted:

[0147] First layer: Hash bucketing based on clause type (e.g. "Guarantee" clauses only match events related to guarantees in DREL).

[0148] Second layer: Use HNSW algorithm (Hierarchical Navigable Small World, HNSW) to build a nearest neighbor index on the event graph's center node vectors, quickly recalling the top 200 candidate events.

[0149] Third layer: Perform precise GSM calculations on candidate events in parallel.

[0150] The system updates the index every hour to ensure that new events can be matched in real time. The case set SRECS contains key metadata: event original text, loss amount (in ten thousand yuan), and occurrence probability statistical value (e.g. the historical default rate of a certain type of clause in the real estate industry is 15%).

[0151] Based on the similar risk event case set, perform Bayesian-Monte Carlo risk probability modeling to obtain potential risk probability values.

[0152] For each case in SRECS, extract its risk trigger condition (e.g. "trigger compensation clause when raw material price rise > 30%") and result data (e.g. actual default probability 22%). Build a Bayesian Network (BN) model: network nodes include clause element variables (e.g. "price fluctuation threshold"), external environment variables (e.g. "bulk commodity index"), and risk result variables (e.g. "debt default"). The dependency relationship between nodes is built based on the causal relationship in the case, for example, "threshold set too high (parent node) → easy to trigger compensation (child node) → cash flow pressure increases (grandchild node)". Use Maximum Likelihood Estimation (MLE) to calculate the Conditional Probability Table (CPT). For example, when "price fluctuation threshold > 25%", the conditional probability P(Trigger|Threshold) of "trigger compensation" is 0.65.

[0153] Based on the Bayesian network, perform Monte Carlo Simulation (MCS):

[0154] Parameter Sampling: Assign probability distributions to uncertain variables (e.g., commodity price increase in the next 3 years). If historical data is available, use Kernel Density Estimation (KDE) to fit the distribution (e.g., Gamma(α=2.1, β=0.8)). If no data is available, use an expert-predefined uniform distribution (e.g., increase 5%-50%).

[0155] Propagation Calculation: Perform 10,000 random samplings. In each sampling, generate random states based on CPTs and variable distributions (e.g., "increase=28%, threshold=25% → trigger compensation=yes"), and finally count the frequency of risk event outcomes (e.g., "default" state).

[0156] Probability Output: Calculate the frequency of risk event occurrence as the Potential Risk Probability Value (PRPV). For example, a "currency-linked clause" appears 2,150 times in 10,000 simulations with loss exceeding the limit, then PRPV=21.5%.

[0157] To improve the reliability of PRPV, introduce Sensitivity Analysis (SA):

[0158] Key Parameter Perturbation: Take the high-impact nodes in the Bayesian network (e.g., "industry sentiment index") ±10%, and re-run MCS.

[0159] Scenario Comparison: Calculate the PRPV difference between optimistic scenarios (e.g., GDP growth 7%) and pessimistic scenarios (e.g., GDP growth 3%).

[0160] Final output probability value with confidence interval, such as "base probability 21.5%, 95% confidence interval [19.2%, 23.8%]". All calculations are distributed on a cloud computing platform (e.g., AWS Batch), and the simulation time of a single clause is less than 8 seconds.

[0161] According to the Potential Risk Probability Value and event case impact data, perform multi-dimensional risk impact fusion assessment to generate risk quantification labels for each key clause.

[0162] Risk Impact Assessment covers four dimensions:

[0163] Financial Impact (FI): According to the actual loss data in the case (unit: ten thousand yuan), combined with the current contract size for linear scaling. For example, a "compensation clause" in a case resulted in a loss of 500,000 yuan, and the current contract amount is 10 times that, then the baseline FI=5,000,000 yuan.

[0164] Legal Impact (LI): Based on the grading of judicial case outcomes. For example, "Clause Invalid" is assigned 10 points (highest risk), "Partial Support" is assigned 5 points.

[0165] Reputational Impact (RI): Calculated through a public opinion analysis model. For example, a certain "Bundling Sales Clause" triggered a 200% increase in negative media reports, so RI = 8 points.

[0166] Contagion Impact (CI): Assessing the risk of chain reactions. For example, a certain "Cross Default Clause" led to the bankruptcy of 3 associated enterprises, CI = 9 points.

[0167] Use Dempster-Shafer Theory (DST) for multi-source data fusion:

[0168] Convert the impact evidence of each dimension into Basic Probability Assignment (BPA). For example, Financial Impact FI = 50 million yuan corresponds to BPA: {Low Risk: 0.2, Medium Risk: 0.6, High Risk: 0.2}.

[0169] Merge BPA of different dimensions through synthesis rules (Dempster's Rule). For example, the high-risk BPA of the financial dimension (0.2) and the medium-risk BPA of the legal dimension (0.7) are synthesized to generate a joint probability distribution.

[0170] Calculate the Belief Function (Bel) and Plausibility Function (Pl) to determine the confidence interval of the risk level.

[0171] Finally generate the Risk Quantification Label (RQL), including the following fields:

[0172] Risk Probability: PRPV value (such as 21.5%);

[0173] Composite Risk Index (CRI): Range 0-100, mapped from the fused DST result (such as CRI = 73);

[0174] Main impact dimensions: sorted by impact value (such as finance > law > reputation);

[0175] Sensitivity summary: Key sensitive parameters and their impact magnitude (e.g. "GDP per 1% drop, probability +2.3%").

[0176] Label visualized by color coding: CRI <30 green (low risk), 30-70 yellow (medium risk), >70 red (high risk). This label will serve as the core input for the next stage of clause optimization.

[0177] The system intelligently matches structured clauses with a dynamically updated industry risk event library, identifies risk patterns in historical cases through similarity algorithms, and quantifies the risk occurrence probability and potential impact of the current clause using probability and statistics methods. The risk event library continuously incorporates the latest judicial cases, regulatory penalties, and market default events, achieving objective quantification and forward-looking early warning of contract risks, helping users identify hidden legal and business risks in clauses. The dynamically updated risk library ensures that the evaluation results reflect the latest market environment and judicial practice.

[0178] S203, input the set of structured clause elements and their risk quantification labels into a pre-trained clause value-risk collaborative optimization model, and perform multiple rounds of iterative optimization simulation processing according to the current clause element state and the preset target, to generate a set of optimization clause revision suggestions that maximize the user's set value target under the premise of controllable risk, wherein the collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the acceptance of opponents to clause modifications and the final execution results under different negotiation strategies;

[0179] Specifically, the multi-objective reinforcement learning state space can be initialized according to the set of structured clause elements and their risk quantification labels to obtain an initial strategy space;

[0180] The system receives the structured clause element set (SCES) generated by the front-end processing and the risk quantification label (RQL) corresponding to each clause. The SCES contains three types of core data: 1) clause type (such as payment period, default liability, and guarantee method); 2) core elements (such as monetary value, time node, and trigger condition); and 3) expression characteristics (such as ambiguity word density and passive voice proportion). The RQL contains two dimensions: potential risk probability (PRP, value range 0-1) and risk impact coefficient (RIC, value range 1-5 levels). The initialization process first maps the SCES and RQL into a machine-understandable state vector (SV). Specifically: each clause type is encoded as a one-hot vector (OHV, for example, the payment period type is encoded as [1, 0, 0...]), the numerical parameters in the core elements (such as the annual interest rate of 8%) are directly normalized (NOR, for example, mapped to the [0, 1] interval), and the text expression characteristics are converted into a 128-dimensional dense vector through a word embedding layer (WEL). The PRP and RIC of the RQL are concatenated into a 2-dimensional risk vector (RV). Finally, the state vector SV of a contract containing N key clauses is formed by concatenating the vectors of all clauses in order, forming the starting point of the high-dimensional state space (SS).

[0181] Preset Objectives (PO) are set by users at the beginning of a service, usually containing three categories: 1) Value Objectives (VO, such as shortening the payment cycle by 20%, reducing the financing cost by 15%); 2) Risk Tolerance Objectives (RTO, such as controlling the overall contract risk probability below 10%); 3) Constraints (CONS, such as not increasing the collateral, negotiation rounds ≤ 3 times). The system quantifies these objectives as components of a Multi-objective Reward Function (MORF). For example, the reward function corresponding to shortening the payment cycle can be designed as: actual shortened days / target shortened days x weight coefficient WV (Value Weight). At the same time, the penalty function for risk overrun is: (actual risk probability-risk tolerance threshold) x penalty coefficient WP (Penalty Weight). The key in the initialization stage is to combine the state vector SV with the preset objectives PO to define the Action Space (AS). The action space is composed of all feasible clause revision operations, for example: 1) modify the payment period (action code A01); 2) adjust the penalty proportion (A02); 3) increase the performance guarantee margin clause (A03). Each action is accompanied by a modification parameter (such as changing the payment period from 90 days to 60 days). The final Initial Policy Space (IPS) is the Cartesian product of the state space SS and the action space AS, providing a search range for subsequent optimization simulation.

[0182] To improve the optimization efficiency, the system adopts a hierarchical state representation (HSR). The first layer is the contract global state (GS), which contains aggregated indicators such as the overall risk probability mean and weighted value score. The second layer is the clause cluster state (CCS), which groups clauses according to functional relevance (e.g., all payment-related clauses are a cluster). The third layer is the single clause state (SCS). This structure allows the reinforcement learning agent to grasp the macro contract situation and focus on local clause optimization. After the initial policy space IPS is constructed, the system runs a round of random policy sampling (RPS) to generate 500-1000 random revision schemes as the initial population (IPOP) to start the subsequent optimization process. All state vectors and action encodings are compressed in dimension through feature hashing (FH) to ensure computational efficiency.

[0183] According to the initial policy space, a dual-agent generative adversarial network is used to simulate the adversarial negotiation process to obtain the opponent's acceptance prediction value.

[0184] The dual-agent generative adversarial network (DA-GAN) is the core engine for simulating negotiations, consisting of two deep neural network agents: 1) the proposer agent (PA), responsible for generating clause revision schemes; 2) the opponent agent (OA), responsible for evaluating and feeding back the acceptance probability. The PA uses a conditional generator (CG) structure, with input being the current state vector SV and the proposer's goal PO, and output being the revision action sequence (e.g., [A01: 60 days, A02: +5%]). The OA uses a convolutional-attention hybrid encoder (CAHE), with input being the revision scheme generated by PA, the opponent's historical contract database, and the real-time acquired opponent credit profile (OCP, including credit rating, industry status, recent litigation records, etc.). The OCP is dynamically updated through a public data crawler (PDC) to ensure the authenticity of the simulation.

[0185] The counterparty negotiation simulation process is iterative: after the PA generates a revised proposal, the OA predicts the opponent's acceptance probability (OAP) based on three dimensions: 1) Economic Feasibility (EF): calculates the impact of the revised proposal on the opponent's cash flow (e.g., the value of the decrease in the cash conversion cycle due to early payment); 2) Risk Compatibility (RC): assesses the opponent's historical tolerance for the risk of the clause (e.g., the average number of RICs accepted by the industry in the past 6 contracts); 3) Strategic Consistency (SC): analyzes the opponent's recent contract modification tendencies for similar clauses (e.g., the probability of accepting a shortened payment period in the past 6 negotiations). The output layer of the OA uses a Sigmoid Activation Function (SAF) to convert the composite score into an acceptance probability prediction value OAP between 0 and 1. For example, when the PA proposes "payment period reduced from 90 days to 60 days," the OA may combine the opponent's cash flow model to calculate OAP = 0.65 (indicating a 65% probability of acceptance).

[0186] To improve the simulation of reality, the DA-GAN introduces a dynamic adversarial training (DAT) mechanism. After each round of simulation, the OA evaluates the authenticity of the proposal generated by the PA based on real historical negotiation data (stored in the Case KnowledgeBase (CKB)). If the PA generates a proposal that is judged by the OA to be false (e.g., a clause that the industry cannot accept), the policy gradient (PG) of the PA will be penalized. At the same time, the prediction accuracy of the OA is continuously optimized through historical case retrospective validation (HCRV): compare the OA's acceptance prediction value OAP_predicted with the actual signing result OAP_actual, and update the OA network weights using the Cross-Entropy Loss Function (CELF). After tens of thousands of adversarial training, the DA-GAN can accurately simulate the negotiation behavior patterns of different industries and different credit rating opponents.

[0187] Based on the predicted acceptance value of the opponent, the value-risk Pareto frontier is optimized to obtain a set of candidate revised proposal prototypes;

[0188] The OAP output by the DA-GAN is combined with the Value Achievement Degree (VAD) and the Risk Control Level (RCL) to construct a Three-dimensional Optimization Objective Space (TOOS):

[0189] X-axis (value dimension): VAD = ∑(clause value improvement amount × value weight WV);

[0190] Y-axis (risk dimension): RCL = 1 - (current contract risk probability / risk tolerance threshold);

[0191] Z-axis (acceptance dimension): directly use OAP.

[0192] The system uses the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for a Pareto Optimal Solution Set (POSS) in this space. The initial population comes from the random strategy sampling IPOP of the previous step, and each individual represents a complete revision plan (such as a sequence of 5 clause modification actions).

[0193] The core operations of NSGA-II include:

[0194] 1) Fast Non-dominated Sorting (FNS): According to the performance of individuals in the three objectives of TOOS, they are divided into different frontier ranks (Frontier Rank, FR). For example, if a certain plan is better than 90% of other plans in terms of value, risk, and acceptance, it is classified as Rank1 frontier.

[0195] 2) Crowding Distance Calculation (CDC): Within the same frontier rank, the distribution density of individuals in the target space is calculated. Individuals with high crowding distance (such as plan A with no similar plans around) are preferred to be retained, ensuring the diversity of the solution set.

[0196] 3) Elitism Strategy (ES): In each generation, the top 20% of optimal individuals are directly entered into the next generation to avoid the loss of high-quality solutions.

[0197] 4) Simulated Binary Crossover (SBX): Swap the actions of two parent solutions with probability PC=0.85 (Crossover Probability). For example, swap the "shorten payment period" action of parent 1 with the "reduce penalty" action of parent 2.

[0198] 5) Polynomial Mutation (PM): Randomly adjust the action parameters with probability PM=0.15 (Mutation Probability). For example, mutate the payment period from 60 days to 45 days or 75 days.

[0199] After 100-200 generations of evolution, the algorithm outputs a Candidate Revision Prototype Set (CRPS) distributed on the Pareto frontier, typically containing 50-100 non-dominated solutions.

[0200] To accelerate convergence, the system introduces an Objective Space Dimensionality Reduction (OSDR) technique. When a strong correlation exists between value and acceptance (e.g., correlation coefficient Corr > 0.7), the three-dimensional space is compressed into a "risk-comprehensive benefit" two-dimensional plane using Principal Component Analysis (PCA). Meanwhile, a Reference Point Guidance (RPG) strategy is employed: based on the user's preset target priority (e.g., "acceptance > value > risk"), set the reference point coordinates (e.g., [OAP=0.9, VAD=1.2, RCL=0.8]), and the algorithm preferentially approaches the solution set region in the direction of the reference point. All CRPS solutions are accompanied by a three-dimensional score report, for example:

[0201] Solution X: value increased by 25% (VAD=1.25), risk probability 8% (RCL=0.92), acceptance 72% (OAP=0.72);

[0202] Solution Y: value increased by 18% (VAD=1.18), risk probability 5% (RCL=0.95), acceptance 85% (OAP=0.85).

[0203] Filter the Candidate Revision Prototype Set (CRPS) with risk threshold constraints to screen out the preliminary optimization suggestion set;

[0204] The core of this stage is to perform Hard Risk Filtering (HRF). The system extracts the Overall Contract Risk Probability (OCPRP) of each proposal from the CRPS, which is calculated as follows: OCPRP = 1 - Π(1 - PRP_i).

[0205] According to the user's preset Risk Tolerance Threshold (RTT, such as ≤10%), proposals with OCPRP > RTT (such as a proposal with OCPRP = 12% will be discarded). At the same time, Single Clause Risk Review (SCRR) is carried out: even if the overall risk is controllable, if the risk impact coefficient RIC ≥ 4 (such as may cause significant loss) and the risk probability PRP ≥ 0.3, the proposal will be marked as a warning.

[0206] The second layer of filtering is based on the Risk-Value Balance Coefficient (RVBC). This coefficient is defined as:

[0207] RVBC = (proposal value score VAD) / (risk impact weighted value ∑(PRP_i × RIC_i))

[0208] The system sets a dynamic RVBC threshold (RVBC Threshold, RVBCT), which is dynamically adjusted according to the user's risk preference:

[0209] Risk-averse users: RVBCT = 2.0 (requires the value brought by unit risk to be ≥ 2 times);

[0210] Risk-neutral users: RVBCT = 1.2;

[0211] Risk-seeking users: RVBCT = 0.8;

[0212] For example, a proposal VAD = 1.25, total risk impact value = 0.6, then RVBC = 2.08 > RVBCT = 2.0, and is retained; if another proposal VAD = 1.40 but risk impact value = 1.8, RVBC = 0.78 < RVBCT = 1.2, then it is discarded.

[0213] Finally, Legal Compliance Verification (LCV) is performed. The system accesses the Regulatory Knowledge Graph (RKG) to check whether the revised proposal violates mandatory regulations:

[0214] 1) Principal qualification constraint: e.g., whether the revised guarantee clause exceeds the counterparty's business scope;

[0215] 2) Interest rate legality: e.g., whether the revised financing cost exceeds 4 times the LPR (Loan Prime Rate);

[0216] 3) Industry-specific requirements: e.g., the payment period for construction engineering contracts cannot exceed the industry regulatory upper limit.

[0217] After three layers of filtering, the remaining proposals form the Preliminary Optimization Proposal Set (POPS), usually reduced to 20-30 high-quality proposals. Each proposal is accompanied by a Risk Compliance Certificate (RCC) indicating the passing status of key indicators.

[0218] Based on the Preliminary Optimization Proposal Set, through multiple rounds of policy gradient reinforcement learning iterations, the Optimization Clause Revision Proposal Set is generated.

[0219] In this phase, the Proximal Policy Optimization (PPO) algorithm is used to deeply optimize the POPS. Each revised proposal is considered as a policy π, and the optimization goal is to maximize the Expected Cumulative Reward (ECR): ECR = E[Value Reward R_val + Risk Penalty R_risk + Acceptance Reward R_acc].

[0220] Where: R_val = WV × (Actual Value Improvement / Target Value Improvement); R_risk = -WP × max(0, Actual Risk Probability - Risk Tolerance Threshold); R_acc = WA × OAP (Acceptance Prediction Value). Parameter explanation: WV = Value Weight, WP = Risk Penalty Coefficient, WA = Acceptance Weight.

[0221] The core iteration process of PPO includes:

[0222] 1) Experience Collection (EC): Based on the current policy π_old, execute POPS scheme in the simulation environment, interact with the opponent agent of DA-GAN, and record the trajectory data τ = {state s, action a, reward r, new state s'}.

[0223] 2) Advantage Estimation (AE): Calculate the advantage value A_t of each action using the Generalized Advantage Estimator (GAE);

[0224] 3) Policy Update (PU):

[0225] Maximize the clipped objective function: L(θ) = E[min( r_t(θ)A_t, clip(r_t(θ), 1-ε, 1+ε) A_t )];

[0226] Parameter explanation: r_t(θ) = new policy probability / old policy probability, ε = clipping coefficient 0.2;

[0227] Prevent training oscillation by restricting the amplitude of policy update.

[0228] After K = 50 rounds of PPO iteration, the system performs Elite Policy Distillation (EPD):

[0229] 1) Select the Top5 scheme with the highest ECR from the final policy pool as the elite set;

[0230] 2) Use Knowledge Distillation (KD) technology to train a lightweight policy network (Policy Network, PN) to imitate the decision-making mode of the elite policy;

[0231] 3) Regenerate a batch of schemes with PN and verify their performance through DA-GAN.

[0232] The final output of the Optimized Clause Revision Proposal Set (OCRPS) contains 10-15 schemes, each with:

[0233] Three-dimensional scoring radar chart: normalized scores of value, risk, and acceptability;

[0234] Clause change comparison table: difference markers between original clauses and revised clauses;

[0235] Simulation execution report: predicted probability of performance improvement, expected bad debt reduction.

[0236] For example: "Plan #7: change payment period from 90 days to 60 days + increase 3% prepayment terms → expected collection speed improvement 25%, bad debt rate from 5.2% to 3.1%, counterparty acceptance probability 81%".

[0237] Based on reinforcement learning optimization model simulates real business negotiation scenarios, learns optimal clause revision strategy in millions of virtual negotiations. The model balances user value targets (such as cash recovery rate) and risk control requirements, generates revision plans that both improve business value and ensure legal safety, breaking through the limitations of traditional manual revision, exploring the optimal clause combination through intelligent algorithms. Reinforcement learning framework enables the model to evolve continuously, adapting to different counterparty negotiation styles and market environment changes.

[0238] S204, for the optimization clause revision suggestion set, combined with the real-time acquisition of the counterparty public credit portrait and market benchmark data, through the lightweight dynamic matching engine, the individualized adaptation degree scoring processing is carried out, and the feasible revision strategy sequence sorted by adaptation degree is generated;

[0239] Specifically, distributed feature vectorization can be performed on the optimization clause revision suggestion set to obtain a revision suggestion feature matrix;

[0240] The system receives the set of optimization clause revision suggestions generated by the preceding steps (containing multiple revision schemes such as "extend the payment period to 180 days", "add a raw material price fluctuation compensation clause", etc.). To achieve efficient computation, distributed feature vectorization technology is used: first, the suggestion set is distributed in parallel in a computing cluster (such as an Apache Spark cluster), and each revision suggestion is broken down into atomic feature units (for example, "clause type = payment period", "revision direction = extend", "value = 180", "risk level = medium"). Each feature unit is converted into a 256-dimensional dense vector (Dense Vector) by a pre-trained financial semantic encoder (using a BERT architecture fine-tuned), for example, "extend" is encoded as [0.78, -0.12,..., 0.45]. Subsequently, the atomic vectors are aggregated by level through a feature fusion layer: numerical features (such as "180") are directly concatenated after standardization; categorical features (such as "risk level = medium") are mapped to vectors through an embedding layer (Embedding Layer); and relational features (such as "the correlation strength between the addition of a compensation clause and the extension of the payment period") are generated into correlation vectors through a graph attention network (Graph Attention Network, GAT). Finally, each revision suggestion is represented as a fixed-length (such as 1024-dimensional) feature vector (FeatureVector), and all vectors form a revision suggestion feature matrix (Revision Feature Matrix, RFMatrix) with dimensions [N x 1024] (N is the number of suggestions).

[0241] To ensure the real-time and scalability of vectorization, the system uses a distributed vector computation pipeline:

[0242] Data Sharding: The suggestion set is sharded according to hash rules to cluster nodes, and each node independently processes the local subset of suggestions;

[0243] Vector Parallel Generation: Each node calls a locally deployed lightweight encoding model (such as distilled Mini-BERT) to avoid central model bottlenecks;

[0244] Dynamic Dimension Alignment: Through a shared feature alignment service (Feature Alignment Service, FAS), the vector dimensions of each node are unified, for example, zero vectors are automatically filled for missing features (such as a suggestion not involving liquidated damages);

[0245] Matrix Aggregation: Collect vectors across nodes by AllReduce Algorithm to form a global RFMatrix. During the process, the system monitors vector quality indicators (e.g., Sparsity Ratio, SR; Cosine Similarity Threshold, CST) in real-time. If an anomaly is detected (e.g., SR > 30%), a feature reconstruction process is triggered, and the semantic encoder is called again to generate additional features.

[0246] Finally, the robustness of processing unstructured revision suggestions. For example, when the suggestion contains ambiguous expressions (e.g., "moderate interest rate increase"), the system performs the following operations:

[0247] Call the Fuzzy Semantic Parser (FSP) to quantify "moderate" into a numerical interval (e.g., +1.5%~2.0%);

[0248] Based on the historical revision case library, complete the missing features (e.g., the typical risk value corresponding to this interval) through Collaborative Filtering (CF);

[0249] Mark the Confidence Score (CS) in the RFMatrix for subsequent steps to use weighted. The final output of the RFMatrix is the basic data structure of the adaptation score, and each row corresponds to a machine-computable revision suggestion.

[0250] According to the revision suggestion feature matrix and the real-time counterparty credit profile, the dynamic weighted vector similarity calculation is performed to obtain the credit adaptation score;

[0251] The real-time counterparty credit profile (CCP) is dynamically constructed by an external data pipeline, including three types of core data:

[0252] Business credit data: such as enterprise credit rating (e.g., AAA / BB), administrative penalty count (APC), judicial enforcement amount (JEA);

[0253] Market behavior data: such as contract fulfillment rate (CFR) in the past year, supply chain stability index (SCSI);

[0254] Public sentiment data: such as news negative sentiment score (NSS, based on LSTM sentiment analysis model). These data are converted into structured vectors (such as 512-dimensional CCP vectors) by a heterogeneous data fusion engine. The system inputs RFMatrix and CCP vectors into a dynamic weighted vector similarity calculation module, the core of which is to calculate the matching degree of each revision suggestion feature vector and CCP vector. An improved weighted cosine similarity algorithm (WCS) is used, and the formula logic is as follows:

[0255] Basic similarity: calculate the cosine value (CV) of the feature vector and the CCP vector, range [-1, 1];

[0256] Dynamic weight allocation: according to the current risk preference, allocate weights to different sub-features of CCP (such as the weight of “judicial execution amount” is automatically increased to 0.6 in the high litigation period);

[0257] Nonlinear calibration: map the CV to the credit adaptation score (CAS) in the [0, 100] interval through the Sigmoid function.

[0258] Weight allocation depends on the context-aware rule engine (CARE):

[0259] Rule base: predefine hundreds of weight rules, such as “if the industry is in a downward cycle (triggered by market benchmark data), increase the weight of ‘contract performance rate’ by 40%”;

[0260] Real-time feedback loop: record the subsequent behavior of the counterparty (such as whether to accept the suggestion) every time the CAS is calculated, and adjust the weight rules through incremental learning. For example, if historical data shows that a counterparty has a low acceptance rate for “high CAS suggestions”, the weight of “public sentiment data” will be automatically reduced. The calculation process uses a streaming processing framework (such as Apache Flink) to ensure that the full CAS refresh is completed within 5 seconds after the counterparty data is updated.

[0261] To handle data conflicts (such as high credit rating but negative public sentiment), the system introduces a conflict resolution mechanism:

[0262] Step 1: Calculate the confidence of each data source (such as credit data confidence = 0.9, public sentiment data = 0.7);

[0263] Step 2: Bayesian Probability Correction (BPC) on conflicting sub-features, for example, downgrading the probability of "AAA rating" to "AA" under negative public sentiment;

[0264] Step 3: Recalculate CAS based on the corrected feature values and mark a conflict flag (CF) in the output. Each revised proposal finally gets a CAS accurate to two decimal places (e.g., 82.35), representing its matching degree with the opponent's credit status.

[0265] Integrate credit adaptation score and market benchmark data, run scenario-aware weight optimization algorithm to calculate weighted comprehensive adaptation score;

[0266] Market Benchmark Data (MBD) real-time access to external financial information sources (such as Bloomberg, central bank interest rate bulletins), core includes:

[0267] Macroeconomic indicators: GDP growth rate, industry prosperity index (IPI);

[0268] Financial market parameters: Interbank lending rate (such as SHIBOR), bond default rate (BDR);

[0269] Regulatory policy changes: such as newly enacted debt regulation strictness score (RSS). System inputs CAS and MBD into scenario-aware weight optimization algorithm (SAWOA), which dynamically allocates the weight ratio of "credit adaptation" and "market adaptation". Its execution process is:

[0270] Scenario classification: classify the current market state into preset scenarios (such as "industry expansion period" "policy tightening period") through random forest model (RFM);

[0271] Weight decision table: look up the predefined weight allocation table according to the scenario category (for example, the weight of "market factors" is increased to 0.7 in the tightening period);

[0272] Nonlinear optimization: use gradient descent (GD) to fine-tune the weight values to maximize the acceptance rate of proposals in similar historical scenarios.

[0273] The core innovation of SAWOA lies in the multi-source data coupling mechanism:

[0274] Time series alignment: For non-synchronous data (such as quarterly GDP and real-time interest rates), time series interpolation (TSI) is used to generate aligned timestamp data points;

[0275] Cross-dimensional correlation: For example, when "bond default rate rises" and "regulatory strictness increases", the "high-risk scenario" mode is triggered, increasing the market weight limit to 0.9;

[0276] Black swan event handling: Regular weight rules are dynamically covered through event detectors (such as NLP keyword capture "war" "pandemic"). The algorithm output is a dynamic weight pair (DWP) for each scenario, such as (credit weight: 0.4, market weight: 0.6).

[0277] Weighted comprehensive adaptation score (WCAS) is calculated according to the following logic:

[0278] Market adaptation score (MAS) is calculated separately: input MBD and revised recommendation features into a regression model (such as XGBoost) to predict the feasibility probability (0~100) of the recommendation in the current market;

[0279] Weighted fusion: WCAS = CAS × W_credit + MAS × W_market, where W_credit + W_market = 1;

[0280] Extreme value correction: if WCAS exceeds the historical quantile threshold (such as >95%), robustness verification (RV) is started, and Monte Carlo simulation (MCS) is used to generate 1000 times of perturbation data for re-calculation, and the median is taken as the final WCAS. This score directly reflects the comprehensive adaptation degree of the revised recommendation under the dual constraints of "counterparty credit" and "market environment".

[0281] According to the weighted comprehensive adaptation score, the strategy priority is sorted, and the preliminary sorting sequence is obtained;

[0282] The system ranks the revision suggestions according to the Weighted Composite Adaptation Score (WCAS). To avoid local optimization caused by simply ranking by score in descending order, a Multi-Criteria Sorting Framework (MCSF) is adopted:

[0283] Primary sorting key: WCAS score (descending order), e.g., a suggestion with WCAS=92.1 is preferred over 85.4;

[0284] Secondary sorting keys:

[0285] Implementation Cost (IC): estimates the resources required to implement the suggestion (such as legal costs, time costs), the lower the cost, the higher the ranking;

[0286] Risk Mitigation Gain (RMG): predicts the risk value after adoption (e.g., from "high risk" to "medium risk" gain +30%);

[0287] Strategy Novelty (SN): based on historical database duplication check, to avoid recommending overly similar old strategies. Secondary sorting keys are compared in the order of preset priority (e.g., RMG > IC > SN) to form a preliminary ranked sequence (PRSeq).

[0288] The key challenge lies in handling the sorting ambiguity of suggestions with similar scores. Solutions include:

[0289] Fuzzy Clustering (FC): suggestions with a WCAS difference of <5 are clustered into the same level group, and the risk gain is sorted again within the group;

[0290] Pareto Frontier Filtering (PFF): for suggestions with the same score, select the scheme that is on the Pareto Optimal Frontier (POF) in the "WCAS-Implementation Cost-Risk Gain" three-dimensional space for priority display;

[0291] Manual rule injection: allows users to predefine forced sorting rules (e.g., "suggestions involving payment cycles must be ranked in the top 3"). The sorting process generates a sequence with a hierarchical structure, for example:

[0292] Tier 1 (WCAS≥90): Suggestion A (WCAS=92.1, RMG=+40%) → Suggestion B (WCAS=90.5, RMG=+35%);

[0293] Tier 2 (85≤WCAS<90): Recommendation C (WCAS=87.2, RMG=+28%).

[0294] Each recommendation comes with a Ranking Justification Report (RJR) to enhance interpretability.

[0295] The system implements a sorting stability guarantee mechanism:

[0296] Input perturbation analysis: add ±5% noise to the original data, repeat the sorting 10 times, and mark it as an "unstable sequence" if the ranking change rate is greater than 20%;

[0297] Retrospective recalibration: For unstable sequences, the Borda Count Method (BCM) is used to synthesize multiple sequencing results to generate a consensus sequence;

[0298] Real-time caching optimization: For frequently accessed counterparty data (such as large corporate CCPs), their PRSeq is cached and a time-to-live (TTL = 300 seconds) is set to reduce repeated calculations. The output sequence provides baseline input for subsequent dynamic matching engines.

[0299] A lightweight dynamic matching engine is applied to perform real-time feedback optimization on the preliminary sorting sequence and output a feasible revision strategy sequence.

[0300] The Lightweight Dynamic Matching Engine (LDME) is the core optimization component that iteratively improves PRSeq through a three-pronged mechanism:

[0301] Mechanism 1: User Implicit Feedback Learning

[0302] Tracking user behavior: Recording the user's dwell time (DT), the number of expansion recommendations (EC), and manual rank adjustment (MRA).

[0303] The implicit feedback model (IFM) converts behavior into weighted signals (e.g., DT > 30 seconds is considered a "positive signal" with a weight of +0.1);

[0304] Dynamically improve the ranking of the suggestions that are being followed (for example, a suggestion originally ranked 5th will be promoted to 2nd after EC=3 times).

[0305] Mechanism 2: Real-time response to external events

[0306] Establish an event listener (EventListener) to subscribe to key data sources (such as industrial and commercial changes, judicial announcements);

[0307] When a relevant event is detected (such as new execution information from the counterparty), a sequence re-evaluation (SR) is triggered immediately:

[0308] Update the Judicial Enforcement Amount (JEA) in the CCP;

[0309] Recalculate the CAS and WCAS of the affected proposals;

[0310] A Change Flag (CF) is inserted for suggestions with a ranking change of >3 positions.

[0311] For example, if a proposal's WCAS deteriorates from 85 to 72 due to counterparty credit deterioration, its ranking will automatically drop from 3rd to 8th.

[0312] Mechanism 3: Cross-client collaborative filtering

[0313] Anonymous Strategy Pool (ASP): collects historically adopted revision proposals and their results (e.g., "accepted / rejected" and "fulfillment success rate");

[0314] When a new customer scenario matches a similar case (KNN nearest neighbor algorithm, k=5), the strategy with a high success rate is injected into the current sequence;

[0315] For example: if it is detected that the current customer has an 80% similarity with "a manufacturing enterprise" and the fulfillment rate of the enterprise's "raw material compensation clause" is 95%, the ranking of similar suggestions in the current sequence will be improved.

[0316] The engine ultimately outputs a Feasible Revision Strategy Sequence (FRSSeq), which has the following characteristics:

[0317] Labels with dynamic weights (e.g., "Recommendation Index: ★★★★☆ (92.1)");

[0318] Supports tiered folding display (divided into Tier 1 / 2 / 3 according to WCAS interval);

[0319] Each strategy is accompanied by an optimization path trace (e.g., "improved by 2 positions due to user attention"). This sequence is directly used by the subsequent counterfactual reasoning module.

[0320] The system integrates real-time data of counterparty's credit rating, historical performance record, etc., combined with current market interest rates, industry sentiment, and other benchmark indicators, to assess the matching degree of each revision proposal with a specific counterparty. The lightweight engine ensures millisecond-level response speed, avoids "one-size-fits-all" standardized proposals, and provides personalized solutions tailored to specific trading partners and market environments. Real-time data access ensures the timeliness and relevance of the proposals.

[0321] S205, based on the sequence of feasible revision strategies, interactive counterfactual reasoning is carried out, and the customer is visually displayed the change curve of debt performance probability and the expected loss distribution under the preset external scenario after adopting different revision strategies, and according to the feedback selection of the user to the reasoning result, the final clause revision scheme and the supporting risk mitigation measures are determined as the output of the digital value-added service.

[0322] Specifically, multi-scenario counterfactual generation can be implemented for the sequence of feasible revision strategies, creating a set of reasoning scenarios;

[0323] The system receives a sorted sequence of feasible revision strategies (Feasible Revision Strategy Sequence, FRSS), which includes clause revision schemes (such as interest rate adjustment range, guarantee method change, repayment period reset, etc.) arranged in descending order of fitness. The Multi-Scenario Counterfactual Generation (MSCG) module first analyzes the core revision point (Core Revision Point, CRP) of each strategy and associates it with the preset external scenario parameter library (External Scenario Parameter Library, ESPL). The ESPL stores macroeconomic and industry risk factor combinations predefined by the financial engineering team, including three types of scenario dimensions:

[0324] Baseline Scenario (BS): based on the current market consensus forecast (such as GDP growth rate CPI_BS=2.5%, CPI refers to the consumer price index);

[0325] Stress Scenario (SS): such as "interest rate surges by 300 basis points" (BPS, basis point unit, 1 basis point = 0.01%), "industry demand shrinks by 20%";

[0326] Extreme Scenario (ES): such as "counterparty credit rating downgraded by three levels" (e.g., from AA to BBB), "commodity price volatility doubles" (Vol_ES=40%, Vol refers to volatility);

[0327] Each scenario is assigned a unique scenario code (SC), such as BS01, SS05, ES12.

[0328] The counterfactual generation engine employs a conditional variational autoencoder (CVAE) technique, with inputs being the revision strategy feature vector (RSFV) and the scenario parameter vector (SPV). The encoder of the CVAE compresses the RSFV and SPV into a latent variable (LV), and the decoder reconstructs a counterfactual scenario description (CSD) based on the LV. For example, for the strategy of "extending the repayment period to 36 months," in the interest rate surge scenario (SS05), the generated counterfactual scenario description is: "If the central bank benchmark interest rate rises by 300BPS, the counterparty's liquidity coverage ratio (LCR_SS=80%, LCR refers to liquidity coverage ratio) falls below the warning line, which may lead to repayment delays starting from the 24th month." The scenario generation process introduces a semantic consistency validator (SCV) to ensure economic logic rationality (such as the increase in financing costs for enterprises when interest rates rise).

[0329] The final output of the deduction scenario set (SSD) adopts a standardized data structure: each record contains the scenario ID (SID), the associated revision strategy ID (StID), the scenario description text (SDT), and the quantitative parameter vector (QPV). For example, in the record with SID=SS05_St023, the QPV contains key parameters: interest rate increase ΔR=3.0%, counterparty revenue decrease ΔRev=-15%, and industry default rate baseline PD_Base=5.2% (PD refers to default probability). The SSD is de-duplicated through a scenario similarity clustering (SSC) algorithm, ensuring that the difference between scenarios is greater than 30% (calculated by Jaccard distance), and finally forms a deduction set containing 50-200 unique scenarios.

[0330] Based on the set of scenarios, Monte Carlo simulation of debt performance probability is performed to obtain the probability curve data.

[0331] The Debt Performance Probability Simulator (DPPS) reads scenario data from the SSD one by one, and constructs a dynamic performance assessment model (PAM). The core of this model is a three-layer computing architecture:

[0332] Cash Flow Layer (CFL): According to the revised repayment schedule (such as the principal repayment table), counterparty operating forecasts (such as quarterly revenue growth rate g_Q), and cost structure (fixed cost ratio FCR=35%), monthly net cash flow sequence (NCFS) is generated;

[0333] Risk Transmission Layer (RTL): Risk factors in QPV (such as interest rate ΔR, commodity price volatility Vol) are mapped to counterparty solvency indicators through a Vector Autoregression Model (VAR), such as interest coverage ratio (ICR_t=EBIT_t / Interest_t, EBIT refers to Earnings Before Interest and Taxes);

[0334] Default Criterion Layer (DCL): Set ICR<1.5 or current ratio (CR_t)<1.0 as the technical default trigger condition.

[0335] Monte Carlo Simulation (MCS) engine performs 10,000 random samplings for each scenario. Key random variables include:

[0336] Counterparty revenue volatility (RV) ~ normal distribution N(μ=ΔRev, σ=RV_Std), where RV_Std is taken from industry historical data;

[0337] Market interest rate changes (Δr_t) ~ Jump-Diffusion Process (JDP), with a base diffusion coefficient σ_diff=0.2 and a jump frequency λ_jump=0.05 (i.e. an expected 5 times of significant fluctuations per year);

[0338] Each sample generates a Performance Status Path (PSP) that records the ICR_t, CR_t values and Default Flag (DF) at each point from the contract start month (t=0) to the expiration month (t=T).

[0339] After the simulation, all PSPs are aggregated to generate Probability Curve Data (PCD). This data contains two core curves:

[0340] Cumulative Performance Probability Curve (CPPC): calculates the proportion of paths that have not defaulted by month t, formula CPPC(t)=1-∑DF_t / 10,000;

[0341] Marginal Default Probability Curve (MDPC): calculates the proportion of new default paths added in month t, MDPC(t)=DF_t / 10,000.

[0342] Each curve is stored as a time series array according to the scenario number (SID) and strategy number (StID), for example, the CPPC data for SID=SS05_St023 is: [t=1:99.2%, t=2:97.8%,..., t=36:82.3%]. The data is accurate to one decimal place.

[0343] Based on the Probability Curve Data and market benchmark data, dynamic modeling of expected profit and loss is performed to obtain a profit and loss distribution chart;

[0344] The Expected Profit-Loss Engine (EPLE) interfaces PCD with real-time market benchmark data (MBD). MBD includes three types of input:

[0345] Risk-Free Rate Curve (RFRC): taken from government bond yields (e.g. 1-year YTM_1y=2.3%, YTM refers to yield to maturity);

[0346] Credit Risk Premium (CRP): based on counterparty industry classification (e.g. industry sector premium CRP_Ind=150BPS);

[0347] Funding Cost Parameter (FCP): includes Funding Spread (FS=80BPS), Operational Cost Ratio (OCR=0.7%);

[0348] The engine first computes a sequence of Discount Factors (DF_t): DF_t = 1 / (1+RFRC_t+CRP+FS)^(t / 12).

[0349] The dynamic modeling process is executed in three steps:

[0350] Step 1: Discounted Cash Flow (DCF)

[0351] Normal performance path: discount each cash inflow (e.g. principal repayment P_t, interest I_t) in NCFS by DF_t;

[0352] Default path: discount Recovery Value (RV) by DF_{t_d} at default time point t_d, RV = Collateral Value x Recovery Rate (RR), RR takes industry average 45%;

[0353] Step 2: P&L Calculation

[0354] Single-sampled P&L value (P&L_i) = ∑(discounted cash inflow) - Outstanding Principal (OP);

[0355] Step 3: Distribution Construction

[0356] Aggregate P&L_i from 10,000 samples to form P&L Distribution Histogram (P&DH), Bin Count (BC) is set to 50.

[0357] The final generated Profit-Loss Distribution Plot (PLDP) contains three key visualization elements:

[0358] Probability Density Curve (PDC): smooth fitting of P&DH, showing the probability of different P&L amounts

[0359] Risk Metric Annotation (RMA): Marking on the chart:

[0360] Expected P&L (EP=Σ(P&L_i×Prob_i)), e.g. EP=$+1.2M;

[0361] Value at Risk (VaR): Maximum loss at 95% confidence, e.g. VaR95=-$0.8M;

[0362] Expected Shortfall (ES): Average loss beyond VaR95, e.g. ES95=-$1.5M;

[0363] Scenario Comparison Layer (SCL): Overlaying PDCs of multiple scenarios with different colors, e.g. blue curve for base scenario (EP_BS=$+1.5M) and red curve for stress scenario (EP_SS=-$0.3M).

[0364] Based on the P&L distribution chart and real-time user feedback, interactive decision optimization is conducted to determine the final revision of the contract and the recommended risk mitigation measures.

[0365] The system visualizes the PLDP and CPPC curves to the client through the Interactive Deduction Console (IDC). The console provides three types of feedback tools:

[0366] Parameter Adjustment Slider (PAS): Allows users to modify key assumptions in real time, such as:

[0367] Recovery Rate (RR) from 45% to 60%;

[0368] Counterparty Revenue Growth Rate (RGR) from -15% to -10%;

[0369] Strategy Weight Selector (SWS): Reorders the strategies in FRSS, e.g. increases the weight of "Increase Margin Clause" from 0.3 to 0.7;

[0370] Scenario Attention Marker (SAM): marks scenarios of particular concern (e.g., marking SID=ES12 as a high-risk scenario);

[0371] User operations trigger real-time recalculation (RTR), and the response delay is controlled within 2 seconds.

[0372] The Decision Optimization Engine (DOE) performs a three-stage optimization based on feedback data:

[0373] Phase 1: Constraint Update

[0374] Based on the high-risk scenarios marked by the user (such as ES12), add constraints to the optimization model: VaR95 under the ES12 scenario must be ≥ -$1M;

[0375] Phase 2: Target Function Reconstruction

[0376] Substitute the user-adjusted weights (such as the margin clause weight of 0.7) into the multi-objective function:

[0377] Max [α×EP +β×performance probability-γ×VaR95], where α, β, and γ are weight parameters;

[0378] Stage 3: Pareto front search

[0379] A non-dominated sorting genetic algorithm (NSGA-II) is used to search for a Pareto optimal set (POS) that satisfies the new constraints in the revised strategy solution space.

[0380] The engine output Final Revision Plan (FRP) is determined by the following logic:

[0381] Filter the three candidate solutions with the highest EP from POS;

[0382] Calculate the scenario robustness score (SRS) of each solution based on user-labeled scenarios (SAM data);

[0383] SRS = Σ(situational attention × probability of fulfillment under the scenario), where attention is determined by the number of times the user marks the situation;

[0384] The scheme with SRS ≥ threshold (e.g., 80 points) and the highest EP ranking was selected.

[0385] The final solution contains specific clause amendments (e.g., "Adjust interest rate from LIBOR + 200BPS to LIBOR + 150BPS, add 30% cash margin") and automatically generates a revised proposal using a legal clause template.

[0386] The final output is a Risk Mitigation Package (RMP) containing:

[0387] Core Hedging Measures (CHM): 3-5 cost-effective measures (e.g., "Purchase counterparty 1-year CDS, notional $10M, cost $150K");

[0388] Contingency Trigger Clause (CTC): Suggest adding automated response clauses to the contract, such as: "If counterparty credit rating falls to BB+, automatically initiate collateralization process";

[0389] Continuous Monitoring Plan (CMP): Specify indicators to track (e.g., counterparty liquidity ratio monthly data), monitoring frequency, and threshold alert rules;

[0390] All outputs are integrated into a Digital Service Report (DSR) and delivered to the client through blockchain notarization, completing the value-added service loop.

[0391] Through Monte Carlo simulation technology, the system can show the probability of performance and financial impact of each revised solution under different economic scenarios (e.g., interest rate fluctuations, industry recession). Users can interactively adjust parameters to intuitively compare the pros and cons of each solution, transforming complex legal clauses into intuitive business impact analysis, assisting users in making data-driven decisions. Interactive design enhances the transparency and user engagement of the service.

[0392] It can be seen that the creditor's rights and debts original file set uploaded by the client is received, a structured clause element set containing clause type, core element and expression manner characteristics is generated, the risk quantization label of each key clause is generated based on the structured clause element set, the structured clause element set and its risk quantization label are input into the pre-trained clause value-risk collaborative optimization model, an optimization clause revision suggestion set maximizing the user's set value target under the premise of controllable risk is generated, the optimization clause revision suggestion set is processed to generate a feasible revision strategy sequence sorted by adaptation degree, and finally the final clause revision scheme and the supporting risk mitigation measure suggestion are determined based on the feasible revision strategy sequence as the output of the digital value-added service, so that the accuracy and decision efficiency of the creditor's rights and debts management can be improved.

[0393] Another embodiment of the application provides an AI-based digital value-added service system for creditor's rights and debts demand analysis, which is shown in Figure 3 The system can include:

[0394] The receiving module 301 is configured to receive the creditor's rights and debts original file set uploaded by the client, perform semantic deconstruction and element extraction on the key clauses in the file through the deep semantic parser based on the adversarial training, and generate a structured clause element set containing clause type, core element and expression manner characteristics.

[0395] The evaluation module 302 is configured to call the dynamically associated industry risk event library based on the structured clause element set, perform clause potential risk probability calculation and risk impact degree evaluation processing according to the similarity matching of element characteristics and risk event cases, and generate the risk quantization label of each key clause.

[0396] The optimization module 303 is configured to input the structured clause element set and its risk quantization label into the pre-trained clause value-risk collaborative optimization model, perform multi-round iteration optimization simulation processing according to the current clause element state and the preset target, and generate an optimization clause revision suggestion set maximizing the user's set value target under the premise of controllable risk, wherein the collaborative optimization model is based on the reinforcement learning framework and is trained by simulating the acceptance of the counterparty to the clause modification and the final execution result under different negotiation strategies.

[0397] The matching module 304 is configured to process the optimization clause revision suggestion set through the lightweight dynamic matching engine for personalized adaptation degree scoring processing in combination with the real-time acquired counterparty public credit portrait and market benchmark data, and generate a feasible revision strategy sequence sorted by adaptation degree.

[0398] The determining module 305 is configured to perform interactive counterfactual reasoning based on the feasible revision strategy sequence, visualize the change curve of debt performance probability and the expected loss and benefit distribution under a preset external scenario after adopting different revision strategies for the customer, and determine a final clause revision scheme and a supporting risk mitigation measure recommendation according to the feedback selection of the user on the reasoning result, as the output of the digital value-added service.

[0399] The embodiment of the present application also provides a storage medium in which a computer program is stored, wherein the computer program is configured to execute the steps in any of the method embodiments when running.

[0400] Specifically, in the embodiment, the storage medium can be configured to store a computer program for executing the following steps:

[0401] S201, receiving a set of original credit and debt files uploaded by a client, performing semantic deconstruction and element extraction on key clauses in the files through a deep semantic parser based on adversarial training, and generating a structured clause element set containing clause types, core elements and expression manner features;

[0402] S202, based on the structured clause element set, calling a dynamically associated industry risk event library, performing clause potential risk probability calculation and risk impact degree evaluation processing according to the similarity matching of element features and risk event cases, and generating a risk quantization label for each key clause;

[0403] S203, inputting the structured clause element set and the risk quantization label thereof into a pre-trained clause value-risk collaborative optimization model, performing multi-round iterative optimization simulation processing according to the current clause element state and a preset target, and generating a set of optimization clause revision suggestions that maximize the user-set value target under the premise of controllable risk, wherein the collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the acceptance of the counterparty to the clause modification and the final execution result under different negotiation strategies;

[0404] S204, for the optimization clause revision suggestion set, combining the real-time acquired public credit portrait of the counterparty and market benchmark data, performing personalized adaptation degree scoring processing through a lightweight dynamic matching engine, and generating a feasible revision strategy sequence sorted by adaptation degree;

[0405] S205, based on the feasible revision strategy sequence, performing interactive counterfactual reasoning, visualizing the change curve of debt performance probability and the expected loss and benefit distribution under a preset external scenario after adopting different revision strategies for the customer, and determining a final clause revision scheme and a supporting risk mitigation measure recommendation according to the feedback selection of the user on the reasoning result, as the output of the digital value-added service.

[0406] The embodiment of the present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps in any of the above method embodiments.

[0407] Specifically, the electronic device can further comprise a transmission device connected with the processor and an input / output device connected with the processor.

[0408] Specifically, in the embodiment, the processor can be configured to perform the following steps through the computer program:

[0409] S201, receiving a set of original files uploaded by a client, performing semantic deconstruction and element extraction on key clauses in the files through a deep semantic parser based on adversarial training, and generating a set of structured clause elements containing clause types, core elements and expression manner features;

[0410] S202, based on the set of structured clause elements, calling a dynamically associated industry risk event library, performing clause potential risk probability calculation and risk impact degree evaluation processing according to the similarity matching of element features and risk event cases, and generating risk quantization labels for each key clause;

[0411] S203, inputting the set of structured clause elements and the risk quantization labels into a pre-trained clause value-risk collaborative optimization model, performing multi-round iterative optimization simulation processing according to the current clause element state and the preset target, and generating a set of optimization clause revision suggestions that maximize the user-set value target under the premise of controllable risk, wherein the collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the acceptance of the counterparty to clause modification and the final execution result under different negotiation strategies;

[0412] S204, for the set of optimization clause revision suggestions, combining the real-time acquired public credit portrait of the counterparty and market benchmark data, performing personalized adaptation degree scoring processing through a lightweight dynamic matching engine, and generating a feasible revision strategy sequence sorted by adaptation degree;

[0413] S205, based on the feasible revision strategy sequence, performing interactive counterfactual reasoning, visualizing the debt performance probability change curve and expected loss and gain distribution under a preset external scenario after adopting different revision strategies for the client, and determining a final clause revision scheme and supporting risk mitigation measure suggestion as the output of the digital value-added service according to the feedback selection of the user to the reasoning result.

[0414] The above detailed description of the structure, features and effects of the present application is based on the embodiments shown in the drawings. The above description is only the preferred embodiments of the present application, but the present application is not limited to the embodiments shown in the drawings. Any changes or modifications made in accordance with the concept of the present application, or equivalent embodiments with equivalent changes, are still within the scope of the present application.

Claims

1. An AI-based digital value-added service method for analyzing creditor-debtor needs, characterized by, The method comprises: receiving a set of original credit and debt documents uploaded by a client, performing semantic deconstruction and element extraction on key clauses in the documents through a deep semantic parser based on adversarial training, and generating a set of structured clause elements containing clause types, core elements, and expression characteristics; based on the set of structured clause elements, calling a dynamically associated industry risk event library, matching the element characteristics with the similarity of risk event cases, performing clause potential risk probability calculation and risk impact degree assessment processing, and generating risk quantization labels for each key clause; inputting the set of structured clause elements and their risk quantization labels into a pre-trained clause value-risk collaborative optimization model, performing multi-round iterative optimization simulation processing according to the current clause element state and the preset target, and generating a set of optimization clause revision suggestions that maximize the user-set value target under the premise of controllable risk, wherein the collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the acceptance of opponents to clause modifications and the final execution results under different negotiation strategies; for the set of optimization clause revision suggestions, combining the real-time acquired public credit portrait of the opponent and market benchmark data, performing personalized adaptation degree scoring processing through a lightweight dynamic matching engine, and generating a feasible revision strategy sequence sorted by adaptation degree; based on the feasible revision strategy sequence, performing interactive counterfactual reasoning, visualizing the debt performance probability change curve and the expected loss and gain distribution under the preset external scenario after adopting different revision strategies for the client, and determining the final clause revision scheme and the supporting risk mitigation measure suggestion according to the user feedback selection, as the output of the digital value-added service.

2. The method of claim 1, wherein, The receiving a set of original credit and debt documents uploaded by a client, performing semantic deconstruction and element extraction on key clauses in the documents through a deep semantic parser based on adversarial training, and generating a set of structured clause elements containing clause types, core elements, and expression characteristics comprises: performing multi-modal adversarial feature extraction on the set of original credit and debt documents uploaded by the client to obtain a set of adversarial enhanced feature vectors that fuse text semantics and document layout; performing clause boundary detection through a sequence labeling model driven by a bidirectional attention mechanism based on the set of adversarial enhanced feature vectors to obtain a preliminary clause segmentation graph; applying a graph convolution network to model element relationships in the preliminary clause segmentation graph to generate a clause element relationship graph; performing element deconstruction on the clause element relationship graph through a semantic parser optimized by adversarial training to generate a set of structured clause elements.

3. The method of claim 2, wherein, The based on the set of structured clause elements, calling a dynamically associated industry risk event library, matching the element characteristics with the similarity of risk event cases, performing clause potential risk probability calculation and risk impact degree assessment processing, and generating risk quantization labels for each key clause comprises: performing heterogeneous graph embedding representation learning based on the set of structured clause elements to obtain a multi-dimensional vector representation of the clause elements; calling a dynamic risk event library to perform event similarity graph matching on the multi-dimensional vector representation of the clause elements to obtain a set of similar risk event cases; Based on the similar risk event case set, the Bayesian-Monte Carlo risk probability modeling is performed to obtain a potential risk probability value; According to the potential risk probability value and the event case influence data, multi-dimensional risk influence fusion evaluation is performed to generate a risk quantization label of each key clause.

4. The method of claim 3, wherein, The structured clause element set and its risk quantization label are input into a pre-trained clause value-risk collaborative optimization model, and according to the current clause element state and the preset target, a plurality of rounds of iterative optimization simulation processing are performed to generate a set of optimization clause revision suggestion set which maximizes the user-set value target under the premise of controllable risk, wherein the collaborative optimization model is based on a reinforcement learning framework, and is trained by simulating the acceptance of the opposite party to the clause modification and the final execution result under different negotiation strategies, including: According to the structured clause element set and its risk quantization label, a multi-objective reinforcement learning state space is initialized to obtain an initial strategy space; According to the initial strategy space, a double-agent generative adversarial network is used to simulate the process of an adversarial negotiation to obtain an acceptance prediction value of the opposite party; According to the acceptance prediction value of the opposite party, a value-risk Pareto frontier optimization is performed to obtain a set of alternative revision scheme prototypes; The set of alternative revision scheme prototypes is filtered by a risk threshold constraint to screen out a set of preliminary optimization suggestions; According to the set of preliminary optimization suggestions, a set of optimization clause revision suggestions is generated through a plurality of rounds of strategy gradient reinforcement learning iterations.

5. The method of claim 4, wherein, The set of optimization clause revision suggestions is combined with the real-time acquired public credit portrait of the opposite party and market benchmark data to perform personalized adaptation degree scoring processing through a lightweight dynamic matching engine to generate a feasible revision strategy sequence sorted by adaptation degree, including: Performing distributed feature vectorization on the set of optimization clause revision suggestions to obtain a revision suggestion feature matrix; According to the revision suggestion feature matrix and the real-time credit portrait of the opposite party, a dynamic weighted vector similarity is calculated to obtain a credit adaptation degree score; Integrating the credit adaptation degree score and the market benchmark data, a scenario-aware weight optimization algorithm is run to calculate a weighted comprehensive adaptation degree score; According to the weighted comprehensive adaptation degree score, a strategy priority is sorted to obtain a preliminary sorting sequence; A lightweight dynamic matching engine is applied to the preliminary sorting sequence for real-time feedback optimization to output a feasible revision strategy sequence.

6. The method of claim 5, wherein, Based on the feasible revision strategy sequence, an interactive counterfactual reasoning is performed to visually display the debt performance probability change curve and the expected loss and benefit distribution under the preset external scenario after adopting different revision strategies for the customer, and according to the feedback selection of the user to the reasoning result, a final clause revision scheme and supporting risk mitigation measure suggestion are determined as the output of the digital value-added service, including: A plurality of scenario counterfactuals are generated for the feasible revision strategy sequence to create a set of reasoning scenarios; Based on the set of reasoning scenarios, a debt performance probability Monte Carlo simulation is performed to obtain probability change curve data; According to the probability change curve data and the market benchmark data, an expected loss dynamic modeling is performed to obtain a loss and benefit distribution graph; According to the loss and benefit distribution graph and the real-time feedback of the user, an interactive decision optimization is performed to determine a final clause revision scheme and supporting risk mitigation measure suggestion.

7. An AI-based digital value-added service system for analyzing creditor-debtor needs, characterized by, The system comprises: The receiving module is configured to receive a set of original credit and debt documents uploaded by a client, perform semantic deconstruction and element extraction on key clauses in the documents by using a deep semantic parser based on adversarial training, and generate a set of structured clause elements including clause types, core elements, and expression manner features; The evaluation module is configured to, based on the set of structured clause elements, call a dynamically associated industry risk event library, perform similarity matching between element features and risk event cases, and perform risk probability calculation and risk impact assessment processing on each key clause to generate a risk quantization label for each key clause; The optimization module is configured to input the set of structured clause elements and the risk quantization label into a pre-trained clause value-risk collaborative optimization model, perform multi-round iterative optimization simulation processing based on a current clause element state and a preset target, and generate a set of optimization clause revision suggestions that maximize a user-set value target under controllable risk, wherein the collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the acceptance of a counterparty to clause modification and the final execution result under different negotiation strategies. The matching module is configured to perform personalized adaptation scoring processing on the set of optimization clause revision suggestions by using a lightweight dynamic matching engine in combination with real-time acquired counterparty public credit portraits and market benchmark data, and generate a feasible revision strategy sequence sorted by adaptation degrees. The determination module is configured to perform interactive counterfactual reasoning based on the feasible revision strategy sequence, visually display a debt performance probability change curve and an expected loss and gain distribution under a preset external scenario after adopting different revision strategies to the client, and determine a final clause revision scheme and a supporting risk mitigation measure suggestion based on user feedback selection, as an output of the digital value-added service.

8. The system of claim 7, wherein, The receiving module is specifically configured to: perform multi-modal adversarial feature extraction on the set of original credit and debt documents uploaded by the client to obtain a set of adversarial enhanced feature vectors that fuse text semantics and document layout; perform clause boundary detection by using a sequence labeling model driven by a bidirectional attention mechanism based on the set of adversarial enhanced feature vectors to obtain a preliminary clause segmentation graph; perform element relationship modeling on the preliminary clause segmentation graph by using a graph convolution network to generate a clause element relationship graph; perform element deconstruction on the clause element relationship graph by using a semantic parser optimized by adversarial training to generate a set of structured clause elements.

9. A storage medium, characterized by The storage medium stores a computer program, and the computer program is configured to execute the method in any one of claims 1-6 when running.

10. An electronic device comprising a memory and a processor, characterized in that, The storage medium stores a computer program, and the computer program is configured to execute the method in any one of claims 1-6 when running.

Images