A bank flow data intelligent analysis and risk identification method

Abstract

The present application relates to the technical field of financial data intelligent processing and risk control, in particular to a bank flow data intelligent analysis and risk identification method. The method comprises the following steps: performing visual feature vectorization processing and semantic context reconstruction processing on multi-source heterogeneous bank flow data to obtain adaptive structured transaction data; performing multi-dimensional data verification processing and dynamic label injection processing on the adaptive structured transaction data to obtain enhanced structured data with risk labels; performing business reality quantitative analysis processing and risk pattern identification processing on the enhanced structured data to obtain a standardized due diligence report; inputting the standardized due diligence report into an intelligent agent analysis engine to obtain an intelligent decision analysis report; and performing natural language interactive interpretation processing based on the intelligent decision analysis report to obtain a flow risk interpretation result. The present application effectively solves the problems of cross-page line breaks, invisible table line interference and multi-account merging that the traditional OCR technology faces when processing multi-source heterogeneous bank flow data.

Description

Technical Field

[0001] This invention relates to the field of intelligent processing and risk control technology for financial data, specifically a method for intelligent analysis and risk identification of bank transaction data. Background Technology

[0002] With the digital development of financial services, bank transaction data has become increasingly diverse and heterogeneous, encompassing standard data within the bank, heterogeneous data across banks, and manually uploaded image files. This complexity poses a significant challenge to automated data processing and in-depth value mining. Currently, financial risk control systems typically use rigid coordinate templates for data parsing and rely on traditional rule engines for linear judgments. Existing technologies are usually limited to mechanical extraction of specific layouts and rely on pre-set static rule bases for risk screening. However, traditional parsing methods are unable to cope with semantic fragmentation caused by layout drift and physical pagination, and cannot effectively convert high-entropy unstructured documents into adaptive structured data. In addition, rule engines based on linear judgments lack chain reasoning capabilities, making it difficult to deduce the causal relationships of deep risks such as hidden liabilities and idle funds from discrete transaction phenomena, and they cannot provide interactive logical tracing, making it difficult to meet the needs of a fully automated closed loop. Therefore, how to overcome the limitations of rigid templates to achieve adaptive parsing of heterogeneous data and establish an intelligent decision-making mechanism with causal reasoning capabilities has become an urgent problem to be solved in this field. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention discloses a method for intelligent analysis and risk identification of bank transaction data. Specifically, the technical solution of this invention includes: Visual feature vectorization and semantic context reconstruction are performed on multi-source heterogeneous bank transaction data to obtain adaptive structured transaction data. The adaptive structured transaction data is subjected to multi-dimensional data verification and dynamic label injection to obtain enhanced structured data with risk labels. The enhanced structured data is subjected to quantitative analysis of business substance and risk pattern identification to obtain a standardized due diligence report; The standardized due diligence report is input into the intelligent agent analysis engine, and the standardized due diligence report is processed by a large language model to construct a panoramic profile and perform causal risk reasoning to obtain an intelligent decision analysis report. Based on the intelligent decision analysis report, natural language interactive interpretation processing is performed to obtain the flow risk interpretation results.

[0004] Preferably, the step of performing visual feature vectorization and semantic context reconstruction processing on multi-source heterogeneous bank transaction data to obtain adaptive structured transaction data includes: Computer vision technology is used to perform layout analysis on the multi-source heterogeneous bank transaction data to extract visual feature vectors, which include text line features, invisible table line features, and header and footer features. Natural language processing techniques are used to perform text flow semantic coherence analysis on the visual feature vectors to identify cross-page line break positions and multi-account merged records, thereby obtaining semantic break position information. Based on the semantic breakage location information and the transaction summary context logic, the transaction records corresponding to the visual feature vector are automatically spliced ​​and restored to obtain the repaired semantic data. By combining the visual feature vectors with the repaired semantic data, a dynamic data mapping rule is constructed to transform unstructured data into comma-separated value format, thereby obtaining adaptive structured transaction data.

[0005] Preferably, the step of performing multi-dimensional data verification and dynamic label injection processing on the adaptive structured transaction data to obtain enhanced structured data with risk labels includes: The adaptive structured transaction data is subjected to account balance continuity cascade verification to obtain the balance continuity verification result; When the balance continuity verification result shows an anomaly, automatic backtracking and error correction processing for similar-looking characters is performed by combining semantic information to obtain balance verification data; Based on the interest settlement records and the current account interest rate pattern, the balance verification data is processed by interest reverse calculation, the theoretical average daily balance is calculated and compared with the actual balance, and the result of the judgment of false fund accumulation is obtained. The adaptive structured transaction data is subjected to file metadata inspection and pixel-level discontinuity analysis to identify modification traces and page replacement behavior, thereby obtaining file digital fingerprints and tamper detection results. Based on the balance verification data, the judgment result of the false fund deposit, and the digital fingerprint and tampering detection result of the document, multi-dimensional risk label injection processing is performed to obtain enhanced structured data with risk labels.

[0006] Preferably, the step of obtaining enhanced structured data with risk labels further includes: Based on a preset rule base and a dynamic semantic model, the adaptive structured transaction data is processed to classify transaction behavior, identify suspected splitting behavior characteristics and bridging fund behavior characteristics, and obtain transaction behavior characteristic data. The transaction behavior feature data is integrated into the multi-dimensional risk label injection processing step to obtain the enhanced structured data with risk labels.

[0007] Preferably, the step of performing business substance quantitative analysis and risk pattern identification processing on the enhanced structured data to obtain a standardized due diligence report includes: The enhanced structured data is evaluated for data confidence based on the parsing completeness rate and the verification pass rate to obtain a data quality score; The enhanced structured data is processed for effective inflow determination, and combined with the analysis of the matching degree between cash flow and operating cycle, the inflated cash flow is eliminated to obtain the true cash-generating capacity data of the enterprise. Based on the risk tag library, the enhanced structured data is scanned for high-risk patterns to identify hidden debt patterns, related-party transaction patterns, and idle fund patterns, thus obtaining risk pattern identification results. By integrating the data quality score, the enterprise's actual cash flow generation capacity data, and the risk pattern identification results, a standardized due diligence report is generated.

[0008] Preferably, the step of inputting the standardized due diligence report into the intelligent agent analysis engine, and performing panoramic profile construction and causal risk reasoning processing on the standardized due diligence report through a large language model to obtain an intelligent decision analysis report includes: The standardized due diligence report is analyzed using an intelligent agent analysis engine to perform industry position analysis, upstream and downstream dependence analysis, and fund collection characteristic analysis, generating panoramic customer profile data. The causal risk reasoning module performs chain reasoning on the risk points in the standardized due diligence report, establishes a logical chain between transaction phenomena, related party relationships and potential risk conclusions, and obtains risk causal inference data. By combining quantitative models and qualitative analysis, credit strategy decision-making calculations are performed on the panoramic customer profile data to generate credit limit suggestions and pricing suggestions. Based on the data mining of fund flow, supply chain finance demand and wealth management demand are analyzed to generate cross-marketing recommendation data. By integrating the risk causal inference data, the credit limit and pricing recommendations, and the cross-marketing recommendation data, an intelligent decision analysis report is obtained.

[0009] Preferably, the step of performing natural language interactive interpretation processing based on the intelligent decision analysis report to obtain the flow risk interpretation result includes: Receive natural language query commands from users for the intelligent decision analysis report; The intelligent agent is invoked to trace back the reasoning process of the causal risk reasoning processing, and natural language explanation data for suspicious transactions is generated. Based on the natural language interpretation data and the natural language query instructions, a multi-round dialogue interaction process is performed to dynamically adjust the analysis script and obtain the interpretation result of the transaction risk.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention effectively solves the problems faced by traditional OCR technology in processing multi-source heterogeneous bank transaction records, such as cross-page line breaks, invisible table line interference, and multi-account merging, by integrating computer vision and natural language processing technologies. By utilizing visual feature vector extraction and text flow semantic coherence analysis, the system can accurately identify semantic breakpoints and automatically splice them back. Combined with account balance continuity verification and similar-looking character backtracking error correction mechanism, it significantly corrects character recognition errors. This dual repair from visual layout to semantic logic transforms unstructured image data into high-precision adaptive structured transaction data, laying a solid foundation for subsequent analysis. 2. This invention breaks through the single text verification mode and introduces pixel-level discontinuity analysis and interest reverse verification technology. On the one hand, by identifying file metadata and modification traces, it accurately combats PS tampering and page replacement behaviors. On the other hand, by combining the current account interest rate to calculate the theoretical daily average balance, it can effectively identify false fund accumulation. On this basis, the system further eliminates bridge loans and inflated cash flow, quantifies the company's true cash generation capacity, and automatically scans high-risk patterns such as hidden liabilities, idle funds, and related-party transactions, greatly reducing the risk of credit fraud caused by cash flow fraud. 3. This invention utilizes an intelligent agent analysis engine to replace traditional rule stacking, and performs deep reasoning on standardized due diligence reports through a large language model; the system can not only construct a panoramic customer profile including industry position and upstream and downstream dependence, but also establish a logical chain between transaction appearance and potential risks through a causal risk reasoning module, providing interpretable qualitative risk analysis; combined with a quantitative model, the system can automatically generate scientific credit limits, pricing suggestions, and supply chain finance cross-marketing strategies, achieving a qualitative leap from data display to intelligent decision-making; 4. Addressing the pain points of traditional risk control systems being opaque and difficult to understand, this invention designs a natural language interactive interpretation mechanism. Credit personnel can directly submit query commands for suspicious points, and the system can call upon the intelligent agent to trace the causal reasoning process and generate easy-to-understand natural language explanations. This multi-turn dialogue interaction mode allows users to dynamically adjust their analytical perspective, quickly obtain the risk logic for specific transactions, break down the barriers between technology and business, assist business personnel in efficiently and accurately understanding complex transaction risk reports, and improve the efficiency of human-machine collaboration. Attached Figure Description

[0011] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0013] Example 1:

[0014] Please see Figure 1 A method for intelligent analysis and risk identification of bank transaction data, comprising: Visual feature vectorization and semantic context reconstruction are performed on multi-source heterogeneous bank transaction data to obtain adaptive structured transaction data. Multidimensional data verification and dynamic label injection are performed on adaptive structured transaction data to obtain enhanced structured data with risk labels; Enhanced structured data is subjected to quantitative analysis of business substance and risk pattern identification to obtain a standardized due diligence report; The standardized due diligence report is input into the intelligent agent analysis engine. The standardized due diligence report is processed by a large language model to construct a panoramic profile and perform causal risk reasoning to obtain an intelligent decision analysis report. Based on the intelligent decision analysis report, natural language interactive interpretation processing is used to obtain the flow risk interpretation results; In the detailed technical solution described in this embodiment, addressing the technical bottlenecks of existing financial risk control systems that struggle to automate the processing of multi-source heterogeneous data and lack deep causal reasoning, this invention proposes a systematic method based on visual semantic fusion and intelligent agent reasoning. This method begins with the ingestion of multi-source heterogeneous bank transaction data, encompassing standard intra-bank data, cross-bank heterogeneous data, and manually uploaded PDF or image files. The system abandons the traditional parsing path relying on rigid coordinate templates, instead employing visual feature vectorization and semantic context reconstruction. In this processing logic, the system deploys a deep learning layout analysis algorithm to extract high-dimensional geometric features of the document and collaborates with a natural language processing model to repair semantic fragmentation caused by physical layout, thereby converting high-entropy unstructured documents into logically rigorous and uniformly formatted adaptive structured transaction data. To ensure financial-grade data availability, the system performs multi-dimensional data verification and dynamic tag injection. This step not only executes rigorous mathematical logic checks but also introduces an OCR error correction mechanism based on semantic similarity. After the data passes verification, risk attribute tags are dynamically assigned to each transaction record according to a pre-set risk control rule base, outputting enhanced structured data with risk tags. On this basis, the system performs quantitative analysis of business substance and risk pattern recognition, aiming to restore the company's true operating capabilities through data appearances, quantify the effective cash flow after eliminating inflated cash flow, and identify complex risk patterns such as hidden liabilities, generating a standardized intermediate product: a standardized due diligence report. As the core of decision-making, the intelligent agent analysis engine receives the aforementioned reports and drives the large language model to perform panoramic profile construction and causal risk reasoning. Unlike the linear judgment of traditional rule engines, this engine has chain reasoning capabilities, enabling it to deduce deep-seated risk causality from discrete transaction phenomena and generate intelligent decision analysis reports containing specific credit limits and pricing strategy suggestions. The system establishes a human-machine collaboration channel through natural language interactive interpretation processing, allowing users to trace the logical basis of risk judgment through natural language. The system instantly backtracks the reasoning chain and generates flowing risk interpretation results, thereby achieving a fully automated closed loop from raw data input to intelligent decision output.

[0015] Example 2:

[0016] The steps for performing visual feature vectorization and semantic context reconstruction on multi-source heterogeneous bank transaction data to obtain adaptive structured transaction data include: Computer vision technology is used to perform layout analysis on multi-source heterogeneous bank transaction data and extract visual feature vectors, which include text line features, invisible table line features, and header and footer features. Natural language processing techniques are used to perform semantic coherence analysis on visual feature vectors to identify page break locations and merged records of multiple accounts, thereby obtaining semantic break location information. Based on semantic breakage location information and transaction summary context logic, the transaction records corresponding to the visual feature vectors are automatically spliced ​​and restored to obtain the repaired semantic data. By combining visual feature vectors with repaired semantic data, dynamic data mapping rules are constructed to transform unstructured data into comma-separated value format, resulting in adaptive structured transaction data. In this specific configuration, visual feature vectorization processing constitutes the core technical means to solve the layout drift problem; the system uses computer vision technology based on convolutional neural networks to perform pixel-level scanning and layout segmentation on the input document image and extract visual feature vectors; the visual feature vectors here not only include the bounding box coordinates of the visible text lines, but also specifically include the invisible table line features based on the vertical pixel histogram. Specifically, the system processes the binarized document image. Perform vertical projection and calculate the cumulative value of each column of pixels. The system sets a blanking gap threshold. With minimum column width When a continuous region is detected satisfy And width At that time, the center line of the area Marked as invisible table column dividers, which are virtual row and column dividers derived from text alignment in borderless tables, as well as header and footer features used to define valid data areas; To address the technical challenge of data fragmentation caused by physical pagination in long-tail documents, the system utilizes natural language processing techniques to perform semantic coherence analysis on the extracted text sequence; the system then extracts the text sequence from the bottom of the page. Input a pre-trained BERT sequence classification model and output a syntactic integrity score through a sigmoid activation function. ,in The hidden layer vectors output by the model; simultaneously detecting the last character of the text. Does it belong to the set of terminating punctuation marks? When the judgment condition is met At that time, among them With a preset threshold, such as 0.3, the system accurately locates the line break position across pages or the dividing point of merged records of multiple accounts, generating semantic break position information. Based on this position information, the system performs automatic splicing and restoration processing. This processing follows the context logic of the transaction summary, merging physically truncated text fragments and returning the separated amount fields to the same transaction record, thereby generating logically complete and repaired semantic data. To achieve template-free parsing, the system combines visual feature vectors with the repaired semantic data and constructs dynamic data mapping rules through a column attribute mapping algorithm based on semantic vector similarity. The algorithm has a pre-built semantic vector library for standard fields. Calculate the embedding vector generated from the current document header text. and Cosine similarity of elements When the maximum similarity exceeds the threshold, the current column is mapped to the corresponding standard field attribute, thereby outputting adaptive structured transaction data in comma-separated value format; this process ensures that the system can adaptively process thousands of different versions of bank statements without human intervention.

[0017] Example 3:

[0018] The steps for performing multi-dimensional data verification and dynamic label injection on adaptive structured transaction data to obtain enhanced structured data with risk labels include: Perform continuous account balance verification on adaptive structured transaction data to obtain the balance continuity verification result; When the balance continuity verification result shows an anomaly, the semantic information is combined to automatically backtrack and correct errors of similar-looking characters to obtain the balance verification data. Based on the interest settlement records and the current account interest rate pattern, the balance verification data is processed by interest reverse calculation, the theoretical average daily balance is calculated and compared with the actual balance, and the result of judging false fund accumulation is obtained. The adaptive structured transaction data is subjected to file metadata inspection and pixel-level discontinuity analysis to identify modification traces and page replacement behavior, and to obtain file digital fingerprints and tamper detection results. Based on balance verification data, results of fraudulent fund deposit judgment, and document digital fingerprint and tampering detection results, multi-dimensional risk label injection processing is performed to obtain enhanced structured data with risk labels; In the verification logic of this embodiment, the multidimensional data verification process is designed as a cascaded automated cleaning pipeline. The process starts with the continuous cascaded verification of account balances. The system performs mathematical operations line by line to verify whether the algebraic sum of the previous period's balance and the current period's transaction amount is strictly equal to the current period's balance. When the calculation results do not match, i.e., the balance continuity verification result shows an anomaly, the system does not immediately determine that it is a data error, but triggers automatic backtracking error correction processing for similar-looking characters. This processing uses the OCR confusion matrix and semantic information to calculate the edit distance between the recognized character and the possible true value, and prioritizes trying to replace high-frequency similar-looking characters, and re-substitutes them into the formula for verification. If the verification passes after replacement, the error correction is confirmed and the corrected balance verification data is generated. To identify deep-seated fraud risks, the system introduces business logic verification, namely interest reverse calculation verification. This step extracts the quarterly interest settlement amount from the transaction records and, based on the bank's current account interest rate parameters, reverse-calculates the theoretical accumulation and average daily balance for the quarter. The system compares this theoretical value with the actual average daily balance on the books. If the deviation exceeds a preset confidence threshold, a judgment result is generated indicating that the account may have fraudulent fund accumulation. Simultaneously, at the physical level, the system performs file metadata inspection and pixel-level discontinuity analysis to detect whether the creation source identifier of the PDF file is abnormal and whether there are unnatural pixel jumps in the background noise of the image, thereby identifying modification traces and page replacement behavior, and generating file digital fingerprints and tampering detection results. The system aggregates the conclusions from all the above verification dimensions and performs multi-dimensional risk label injection processing. This processing maps each verification result to a specific risk metadata label and attaches these labels to the original transaction records, forming enhanced structured data with risk labels. This provides a cleaned and high-quality data foundation with risk warnings for subsequent intelligent analysis.

[0019] Example 4:

[0020] The steps to obtain enhanced structured data with risk labels also include: Based on a pre-defined rule base and a dynamic semantic model, adaptive structured transaction data is classified to identify suspected splitting behavior characteristics and bridging fund behavior characteristics, thereby obtaining transaction behavior characteristic data. By integrating transaction behavior feature data into the multi-dimensional risk label injection processing step, enhanced structured data with risk labels is obtained; In this embodiment, the transaction behavior classification process aims to identify concealed abnormal fund operations by mining time-series and amount patterns. The system runs a hybrid engine based on a preset rule base and a dynamic semantic model. For the identification of suspected order splitting behavior characteristics, the system sets time window parameters and amount aggregation thresholds. When the same counterparty is detected within the time window... Internal occurrence When a transaction is transferred out and the cumulative amount approaches a whole number or a specific regulatory limit, a splitting alert is triggered; the system sets a residence time threshold to identify the characteristics of bridging funds. With the threshold of capital outflow ratio For any inflow amount of The system tracks the set of subsequent outflowing transactions. If all outgoing transaction timestamps With inflow timestamp satisfy And the cumulative outflow amount satisfy If so, it is determined to be bridge financing; The transaction behavior feature data generated by the above identification process not only includes the behavior type, but also includes confidence scores and evidence snapshots of triggering rules. The system integrates the transaction behavior feature data into the multi-dimensional risk label injection processing step, which means that in the final delivered structured data table, each row of the involved transaction has been expanded with explicit behavioral semantic fields. This mechanism transforms complex fund manipulation methods that originally required human experts to review based on experience into explicit risk labels that can be further processed by machines.

[0021] Example 5:

[0022] The process of performing business substance quantitative analysis and risk pattern identification on enhanced structured data to obtain a standardized due diligence report includes: The enhanced structured data is evaluated for data confidence based on the parsing completeness rate and the verification pass rate, resulting in a data quality score. The enhanced structured data is effectively processed to determine inflows, and combined with the analysis of the matching degree between cash flow and operating cycle, the inflated cash flow is eliminated to obtain the true cash-generating capacity data of the enterprise. Based on the risk label library, high-risk pattern scanning is performed on the enhanced structured data to identify hidden debt patterns, related-party transaction patterns, and idle fund patterns, thus obtaining risk pattern identification results. Integrate data quality scores, data on the company's actual cash-generating capacity, and risk pattern identification results to generate a standardized due diligence report; Within the quantitative analysis framework of this embodiment, the system establishes a data credibility benchmark, namely, performing data confidence assessment processing. This processing comprehensively considers the non-empty rate of field parsing, the average confidence level of character recognition, and the success rate of balance logic verification, and calculates a normalized data quality score through a weighted algorithm. This score serves as a weighting coefficient for subsequent analysis, and low-scoring data will be prominently marked in the report to prompt manual review. To accurately reflect the true operational status of enterprises, the system performs effective inflow determination processing. This step utilizes a transaction summary semantic classification model to distinguish between operating income and non-operating inflows, and performs cash flow and operating cycle matching analysis. The system pre-retrieves a historical sample set of high-quality enterprises in the same industry category from the database, extracts the average daily cash inflow data of these sample enterprises within a complete fiscal year, and uses a Gaussian mixture model to fit and cluster the time series, generating a theoretical time distribution probability that reflects the general pattern of capital recovery in the industry. And statistical analysis of the target company at each time point within the observation period. Actual cash inflow amount ; To eliminate the influence of monetary units and meet probability distribution requirements, the system... Perform normalization processing and calculate the time distribution probability of actual capital inflows. ,in The total inflow amount during the observation period ensures The deviation between the two is calculated using the Kullback-Leibler divergence formula: ; If the calculated result The value exceeds the preset abnormal threshold. If the actual cash inflow pattern deviates significantly from the industry norm, it is determined to be an inflated cash flow and is removed, thereby calculating the company's true cash-generating capacity data that reflects the company's core profitability. In terms of risk detection, the system uses a deep graph mining algorithm based on a risk tag library to scan for high-risk patterns. The system constructs a fund flow graph to detect whether there are periodic fixed-amount outflows to identify hidden debt patterns; it detects whether funds circulate within a closed-loop group of related accounts to identify related-party transaction patterns; and it detects whether funds exhibit rapid in-and-out characteristics without substantial transaction background to identify idle fund patterns. The system then structurally assembles the above data quality scores, data on the company's actual cash flow generation capabilities, and risk pattern identification results to generate a standardized due diligence report containing charts, indicators, and conclusions.

[0023] Example 6:

[0024] The standardized due diligence report is input into the intelligent agent analysis engine. Through a large language model, the standardized due diligence report undergoes comprehensive profile construction and causal risk reasoning to obtain an intelligent decision analysis report. The steps include: The intelligent agent analysis engine is used to perform industry position analysis, upstream and downstream dependence analysis and fund collection characteristic analysis on standardized due diligence reports to generate panoramic customer profile data. The causal risk reasoning module performs chain reasoning on the risk points in the standardized due diligence report, establishes a logical chain between transaction phenomena, related party relationships and potential risk conclusions, and obtains risk causal inference data. By combining quantitative models and qualitative analysis, credit strategy decision-making is performed on panoramic customer profile data, generating credit limit suggestions and pricing suggestions. Based on the data mining of fund flow, supply chain finance demand and wealth management demand are analyzed to generate cross-marketing recommendation data. By integrating risk causal inference data, credit limit and pricing recommendation data, and cross-marketing recommendation data, an intelligent decision analysis report is obtained. In the advanced analysis phase of this embodiment, the agent analysis engine serves as the reasoning hub of the system, utilizing the generalized knowledge base and logical reasoning capabilities of the large language model. The system uses the agent analysis engine to deeply interpret the transaction topology in the report, assess the company's bargaining power in the supply chain, calculate the proportion of capital interaction with a single supplier or customer, and analyze the periodic patterns of capital recovery, thereby constructing a multi-dimensional panoramic customer profile data. The core innovation lies in the introduction of a chain-like thinking mechanism through the causal risk reasoning module; this module processes discrete anomalies based on path search using a knowledge graph, and the system constructs the knowledge graph. , where the set of nodes Includes {transaction events, account entities, natural persons, kinship}; when a large personal transfer event is detected. At that time, the system executes a breadth-first search algorithm to find the source of the transfer from the recipient. To key personnel of enterprises The connection path between them; If a path exists And path length The system automatically constructs a logical path of phenomenon-relationship-conclusion, and derives the conclusion of suspected profit transfer or capital withdrawal; the intelligence produced by establishing a logical chain between transaction phenomena, related party relationships and potential risk conclusions is risk causal inference data. In the decision-making output stage, the system combines quantitative models to calculate the basic risk exposure and integrates LLM's qualitative analysis of industry macro trends to comprehensively generate credit limit and pricing recommendations. In addition, the system analyzes unmet needs in the flow of funds, such as large prepayments to specific upstream suppliers, to uncover potential supply chain financing opportunities and generate cross-marketing recommendation data. The system integrates all the above reasoning results to generate an intelligent decision analysis report with an expert perspective.

[0025] Example 7:

[0026] The steps for obtaining flow risk interpretation results based on natural language interactive interpretation of intelligent decision analysis reports include: Receive natural language query commands from users regarding intelligent decision analysis reports; The intelligent agent is invoked to trace back the reasoning process of causal risk reasoning and generate natural language explanation data for suspicious transactions; Based on natural language interpretation data and natural language query commands, multi-round dialogue interaction is performed to dynamically adjust the analysis script and obtain the interpretation results of transaction risk. In the human-computer interaction scenario of this embodiment, in order to solve the black box problem of complex algorithm models, the system is designed with a natural language interaction interpretation and processing module; when the credit approval personnel have questions while reading the report, the system receives the user's natural language query instructions for the intelligent decision analysis report, such as asking why the 2 million yuan in revenue is judged as non-operating income; The system responds to commands by invoking an agent to retrieve the thought chain log data of the large language model during the generative decision-making process, and then performs gradient-based attribution analysis. Specifically, the system calculates the value of each token in the input sequence. Output conclusions integral gradient fraction ,in The system uses the zero vector as a baseline to select scores. Exceeding the preset significance threshold The system extracts the original transaction summary text and amount field corresponding to the token index set as key evidence to support the judgment; the system converts this structured evidence into easy-to-understand natural language and generates natural language explanation data for suspicious transactions. Based on this, the system supports multi-turn dialogue interaction; if the user provides additional contextual information, the system will inject the new information into the reasoning context, reassess the risk logic, dynamically adjust the analysis language, and output the revised or further detailed interpretation results of the transaction risk; this mechanism significantly improves the transparency of risk control decisions and the user's sense of trust.

[0027] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent analysis and risk identification of bank transaction data, characterized in that, include: Visual feature vectorization and semantic context reconstruction are performed on multi-source heterogeneous bank transaction data to obtain adaptive structured transaction data. The adaptive structured transaction data is subjected to multi-dimensional data verification and dynamic label injection to obtain enhanced structured data with risk labels. The enhanced structured data is subjected to quantitative analysis of business substance and risk pattern identification to obtain a standardized due diligence report; The standardized due diligence report is input into the intelligent agent analysis engine, and the standardized due diligence report is processed by a large language model to construct a panoramic profile and perform causal risk reasoning to obtain an intelligent decision analysis report. Based on the intelligent decision analysis report, natural language interactive interpretation processing is performed to obtain the flow risk interpretation results.

2. The intelligent analysis and risk identification method for bank transaction data according to claim 1, characterized in that, The step of performing visual feature vectorization and semantic context reconstruction processing on multi-source heterogeneous bank transaction data to obtain adaptive structured transaction data includes: Computer vision technology is used to perform layout analysis on the multi-source heterogeneous bank transaction data to extract visual feature vectors, which include text line features, invisible table line features, and header and footer features. Natural language processing techniques are used to perform text flow semantic coherence analysis on the visual feature vectors to identify cross-page line break positions and multi-account merged records, thereby obtaining semantic break position information. Based on the semantic breakage location information and the transaction summary context logic, the transaction records corresponding to the visual feature vector are automatically spliced ​​and restored to obtain the repaired semantic data. By combining the visual feature vectors with the repaired semantic data, a dynamic data mapping rule is constructed to transform unstructured data into comma-separated value format, thereby obtaining adaptive structured transaction data.

3. The intelligent analysis and risk identification method for bank transaction data according to claim 1, characterized in that, The step of performing multi-dimensional data verification and dynamic label injection processing on the adaptive structured transaction data to obtain enhanced structured data with risk labels includes: The adaptive structured transaction data is subjected to account balance continuity cascade verification to obtain the balance continuity verification result; When the balance continuity verification result shows an anomaly, automatic backtracking and error correction processing for similar-looking characters is performed by combining semantic information to obtain balance verification data; Based on the interest settlement records and the current account interest rate pattern, the balance verification data is processed by interest reverse calculation, the theoretical average daily balance is calculated and compared with the actual balance, and the result of the judgment of false fund accumulation is obtained. The adaptive structured transaction data is subjected to file metadata inspection and pixel-level discontinuity analysis to identify modification traces and page replacement behavior, thereby obtaining file digital fingerprints and tamper detection results. Based on the balance verification data, the judgment result of the false fund deposit, and the digital fingerprint and tampering detection result of the document, multi-dimensional risk label injection processing is performed to obtain enhanced structured data with risk labels.

4. The intelligent analysis and risk identification method for bank transaction data according to claim 3, characterized in that, The step of obtaining enhanced structured data with risk labels further includes: Based on a preset rule base and a dynamic semantic model, the adaptive structured transaction data is processed to classify transaction behavior, identify suspected splitting behavior characteristics and bridging fund behavior characteristics, and obtain transaction behavior characteristic data. The transaction behavior feature data is integrated into the multi-dimensional risk label injection processing step to obtain the enhanced structured data with risk labels.

5. The intelligent analysis and risk identification method for bank transaction data according to claim 1, characterized in that, The steps for performing business substance quantitative analysis and risk pattern identification on the enhanced structured data to obtain a standardized due diligence report include: The enhanced structured data is evaluated for data confidence based on the parsing completeness rate and the verification pass rate to obtain a data quality score; The enhanced structured data is processed for effective inflow determination, and combined with the analysis of the matching degree between cash flow and operating cycle, the inflated cash flow is eliminated to obtain the true cash-generating capacity data of the enterprise. Based on the risk tag library, the enhanced structured data is scanned for high-risk patterns to identify hidden debt patterns, related-party transaction patterns, and idle fund patterns, thus obtaining risk pattern identification results. By integrating the data quality score, the enterprise's actual cash flow generation capacity data, and the risk pattern identification results, a standardized due diligence report is generated.

6. The intelligent analysis and risk identification method for bank transaction data according to claim 1, characterized in that, The step of inputting the standardized due diligence report into the intelligent agent analysis engine, and performing panoramic profile construction and causal risk reasoning on the standardized due diligence report through a large language model to obtain an intelligent decision analysis report includes: The standardized due diligence report is analyzed using an intelligent agent analysis engine to perform industry position analysis, upstream and downstream dependence analysis, and fund collection characteristic analysis, generating panoramic customer profile data. The causal risk reasoning module performs chain reasoning on the risk points in the standardized due diligence report, establishes a logical chain between transaction phenomena, related party relationships and potential risk conclusions, and obtains risk causal inference data. By combining quantitative models and qualitative analysis, credit strategy decision-making calculations are performed on the panoramic customer profile data to generate credit limit suggestions and pricing suggestions. Based on the data mining of fund flow, supply chain finance demand and wealth management demand are analyzed to generate cross-marketing recommendation data. By integrating the risk causal inference data, the credit limit and pricing recommendations, and the cross-marketing recommendation data, an intelligent decision analysis report is obtained.

7. The intelligent analysis and risk identification method for bank transaction data according to claim 1, characterized in that, The step of performing natural language interactive interpretation processing based on the intelligent decision analysis report to obtain the flow risk interpretation result includes: Receive natural language query commands from users for the intelligent decision analysis report; The intelligent agent is invoked to trace back the reasoning process of the causal risk reasoning processing, and natural language explanation data for suspicious transactions is generated. Based on the natural language interpretation data and the natural language query instructions, a multi-round dialogue interaction process is performed to dynamically adjust the analysis script and obtain the interpretation result of the transaction risk.

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