A financial risk intelligent identification and early warning method based on multi-source heterogeneous data fusion

Abstract

The application discloses a financial risk intelligent identification and early warning method based on multi-source heterogeneous data fusion, and sequentially executes seven procedures of multi-source heterogeneous data unified access, global data standardization and federated learning safety fusion, four-layer dimension multi-level feature extraction, CNN-LSTM attention mixed multi-label risk model training, millisecond-level real-time risk reasoning, dynamic self-adaptive threshold grading early warning and risk control closed loop iteration optimization. The application realizes cross-institution data "available but invisible" by relying on federated learning, builds a four-dimensional fusion feature set of customers-transaction-correlation-external, synchronously identifies multiple financial risks by using a hybrid time convolution attention model, and forms a complete risk control closed loop by matching a dynamic threshold grading disposal mechanism.

Description

Technical Field

[0001] This invention relates to the fields of financial risk control big data, cross-domain data fusion, and artificial intelligence deep learning technology, and in particular to a method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion. Background Technology

[0002] Existing financial institutions' internal business systems are siloed, with credit, deposits, payments, wealth management, and credit reporting residing in separate databases. Data storage formats, field definitions, and statistical granularity are completely inconsistent. External data from industry and commerce, judiciary, public opinion, and blacklists are held by third-party institutions, and due to data security regulations, direct exchange of raw data is impossible, creating typical data silos. Furthermore, data sources fall into three heterogeneous formats: structured transaction tables, semi-structured XML / JSON messages, and unstructured public opinion text and customer service recordings transcribed into text. Current technologies can only process single data types individually, lacking a unified, standardized, and privacy-secure cross-source fusion mechanism. The industry practice of purchasing third-party data and aggregating it locally carries risks of customer privacy leaks and data compliance penalties, failing to achieve cross-institutional collaborative modeling where "data is usable but not visible."

[0003] Traditional rule engines only configure explicit risk assessment rules, such as intuitive features like large single transactions, frequent short-term transfers, and historical overdue records. They cannot uncover risks associated with related groups formed by customer equity, guarantees, and fund transactions; at the same time, they ignore external derivative risks such as negative public opinion and corporate legal defaults. Conventional machine learning models only extract features at a single level and do not build a four-dimensional integrated feature set encompassing customers, transactions, relationships, and external factors. This results in extremely low ability to identify hidden and transmissible financial risks, with a consistently low underreporting rate of over 35% for group fraud and covert money laundering activities.

[0004] Traditional risk control models employ shallow models such as single fully connected neural networks, logistic regression, and random forests, lacking the ability to mine time-series data and failing to capture long-term patterns in customer transaction behavior. Some existing technologies use LSTM time-series models or CNN convolutional models in isolation, failing to simultaneously extract local mutation features and long-term time-series behavioral features. Furthermore, they lack attention mechanisms to automatically weight high-risk features, while low-weight, ineffective features interfere with model judgment, resulting in low overall accuracy. Additionally, most existing models are single-label binary classification models, only able to determine "normal / risk" in a single inference, unable to simultaneously differentiate between specific risk types such as fraud, money laundering, overdue payments, and cash-out schemes, leading to a lack of targeted risk management.

[0005] Existing risk control and early warning systems rely on manually set fixed risk thresholds, which are not dynamically adjusted based on macroeconomic cycles, customer segmentation and qualifications, or periodic risk fluctuations. During economic downturns, the overall probability of customer default increases, and fixed thresholds may miss a large number of medium-risk customers. When the trading behavior of high-quality customers fluctuates, fixed thresholds are prone to generating a large number of invalid false alarms, increasing the cost of manual review. Furthermore, existing models are mostly T+1 offline batch detection systems, which can only identify risks after the transaction is completed, failing to achieve millisecond-level real-time interception when a transaction occurs. This lack of proactive early warning means that financial losses cannot be avoided in advance.

[0006] Financial fraud methods are constantly evolving and changing. Fraud gangs continuously change their transaction patterns to circumvent risk control rules, and the generalization ability of statically trained models declines over time. Current technologies rely solely on periodic manual re-collection of samples to train the model, lacking automated result feedback, automatic sample labeling, and a lightweight monthly iteration closed-loop process. This results in long model update cycles and an inability to adapt to new risk attack methods.

[0007] In summary, existing financial risk control technologies have significant shortcomings in five key areas: data fusion security, risk identification dimensions, model algorithm architecture, real-time early warning strategies, and model iteration mechanisms. These shortcomings make it impossible to meet current regulatory requirements and the real-time risk control needs of financial institutions across all scenarios. There is an urgent need for a complete technical solution that enables secure fusion of multi-source heterogeneous data, multi-dimensional in-depth identification, dynamic hierarchical real-time early warning, and automated closed-loop iteration. Summary of the Invention

[0008] To address the aforementioned shortcomings of existing technologies, the present invention solves the following technical problems: It addresses the technical challenges of securely integrating heterogeneous data from different institutions and types, and breaking down data silos. It enables unified and standardized processing of structured, semi-structured, and unstructured data, and achieves cross-domain feature fusion without transmitting the original data, thus meeting data privacy compliance requirements. To address the technical problem of limited risk identification dimensions and inability to uncover hidden risks, a comprehensive four-layer feature system is constructed, encompassing customer static data, transaction timeline, entity relationships, and external public opinion, covering both explicit and implicit transmission-related financial risks. To address the technical challenges of a single model's inability to simultaneously extract local transaction features and temporal behavioral features, and the inability to simultaneously distinguish and identify multiple types of risks, a CNN-LSTM multi-head attention hybrid deep network was constructed to achieve simultaneous multi-label classification and output of detailed risk types. To address the technical issues of false alarms and missed alarms with fixed early warning thresholds, lagging offline detection and early warning, and insufficient real-time response speed, an adaptive dynamic threshold calculation model is constructed, which, combined with an inference acceleration engine, enables millisecond-level real-time transaction interception. To address the technical challenges of the lack of an automated iteration mechanism in models and the rapid decline in their generalization ability in the face of new fraud methods, a fully automated closed-loop optimization process of "identification-early warning-handling-sample feedback-model iteration" is established to continuously improve the accuracy of risk identification.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion, comprising the following steps: Step S1, Multi-source heterogeneous data access: Connect the financial institution's internal business data sources and external public data sources in parallel through a standardized data access gateway; the internal business data sources include the core transaction database, customer profile database, and credit database, while the external public data sources include the public opinion text database, business information database, judicial litigation database, and industry blacklist database; the gateway synchronously collects three types of raw data: structured transaction logs, semi-structured business messages, and unstructured public opinion texts; Step S2, Data Standardization and Federated Integration: The raw data obtained in Step S1 is subjected to unified processing including denoising, deduplication, missing value imputation, format conversion, and semantic alignment to construct a globally unified financial data dictionary; each local node of the data source independently completes feature pre-extraction and only uploads model gradient parameters to the federated collaboration center; each node completes cross-source feature alignment based on the shared gradient to generate a unified risk dataset across the entire domain. Step S3, Multi-level Feature Extraction: Extract customer static features, transaction time series features, entity association features, and external public opinion features from the unified risk dataset, and concatenate them to form a high-dimensional fusion feature vector; Step S4: Deep learning risk model training: Build a CNN-LSTM hybrid neural network and embed a multi-head attention layer. Configure multi-label classification branches in the model output layer. Complete supervised training using a labeled risk sample library. Perform hyperparameter tuning through five-fold cross-validation to solidify the inference model. Step S5, Real-time Risk Identification and Reasoning: Input the real-time business data stream into the solidified reasoning model and output the risk category code, risk level value, and confidence decimal. Step S6, Dynamic Threshold Classification Early Warning: Retrieve market cycle parameters, customer segmentation tags, and historical risk fluctuation curves to calculate adaptive risk thresholds in real time; classify four levels of handling instructions based on risk level values ​​and push the handling instructions to the front-end risk control terminal; Step S7, Closed-loop Iterative Optimization: Record the results of each business warning and handling, return the handling labels to the labeled risk sample library, and re-execute step S4 on a monthly cycle to complete the model iteration update.

[0010] Furthermore, in step S2, the Federal Collaboration Center only receives gradient parameters and feature mapping matrices uploaded by each local node, and does not store any node's original business data. The entire cross-agency data interaction is encrypted using a homomorphic encryption algorithm.

[0011] Furthermore, in step S3, the static characteristics of the customer include the customer's age, occupation type, asset and liability values, and historical performance frequency; the transaction time sequence characteristics include the transaction amount range, transaction frequency, transaction time period, cross-regional transaction markers, and counterparty device fingerprints; the entity association characteristics include equity association links, guarantee relationships, and frequency of fund transfers; and the external public opinion characteristics include the frequency of negative keywords, the number of judicial dishonesty records, and the number of business registration changes.

[0012] Furthermore, in step S4, the CNN layer uses a 3×1 one-dimensional convolutional kernel to extract local features of a single transaction, the LSTM layer is set with 128-dimensional hidden units to capture 72-hour time-series behavioral sequences, and the multi-head attention layer is set with 4 groups of attention heads to assign weighted coefficients to high-risk features.

[0013] Furthermore, in step S4, the multi-label classification branch independently outputs independent codes corresponding to five types of risks: fraud, overdue payment, money laundering, default, and cash-out. Each type of risk is matched with a risk level value in the range of 0 to 100 and a confidence value in the range of 0 to 1.

[0014] Furthermore, in step S5, the TensorRT inference acceleration engine is deployed, ensuring that the latency for complete inference of a single business data item does not exceed 200ms and supporting real-time detection of 10,000 concurrent transactions per second.

[0015] Furthermore, the adaptive risk threshold calculation formula in step S6 is T=α T0+β ΔM+γ ΔR; where T0 is the base fixed threshold, ΔM is the market volatility correction coefficient for the current month, ΔR is the customer's historical risk correction coefficient, and α, β, and γ are preset weighting coefficients.

[0016] Furthermore, the specific rules for classifying the four-level handling instructions in step S6 are as follows: if the risk level is <60 points, a low-risk release instruction is issued; if the risk level is 60 points ≤ risk level <80 points, a medium-risk manual review instruction is issued; if the risk level is 80 points ≤ risk level <90 points, a high-risk transaction interception instruction is issued; and if the risk level is ≥90 points, an extremely high-risk account freeze and regulatory reporting instruction is issued.

[0017] Furthermore, in step S7, the returned samples are labeled to distinguish between positive and negative samples, and the newly added risk samples are divided into the validation set and the training set in a 3:7 ratio. During iterative training, the weights of the CNN's underlying convolutional layers are frozen, and only the parameters of the LSTM and attention layers are updated.

[0018] Furthermore, this method is adapted to four types of financial business scenarios: bank retail credit risk control, third-party payment anti-fraud, securities account abnormal transaction monitoring, and insurance underwriting credit screening. In different scenarios, only the feature extraction dimension weight in step S3 and the threshold weight coefficient in step S6 are replaced.

[0019] The beneficial effects of this invention are: 1. This invention achieves unified and standardized fusion of structured, semi-structured, and unstructured heterogeneous data across the entire domain through a federated learning hierarchical architecture. The original data remains within the local node data center throughout the entire process, eliminating the need to transmit customer information to third parties or across institutions, fully complying with financial data privacy regulations. It breaks down the traditional data silo barriers, integrating four-dimensional risk information including customer, transaction, association, and external public opinion, improving the completeness of customer risk information by over 45%. It also addresses the technical shortcomings of existing technologies, such as inconsistent heterogeneous data formats and the inability to perform cross-domain collaborative modeling, eliminating the need for centralized storage of all original data and reducing server storage costs and data leakage risks.

[0020] 2. This invention constructs a four-dimensional, multi-layered fusion feature set, combined with a CNN-LSTM multi-head attention hybrid deep network, to simultaneously capture four types of risk signals: local transaction mutations, long-term temporal behavior, transmission by related groups, and external public opinion. Multi-label branches can simultaneously identify five sub-categories of financial risks: fraud, money laundering, overdue payments, defaults, and cash-out. Verified in a bank retail lending implementation, the risk identification accuracy has increased from 65% with traditional rule engines to 92%, with a 37% decrease in false negative rate and a 42% decrease in false positive rate. It possesses precise capture capabilities for hidden risks that traditional technologies cannot identify, such as concealed group-related fraud and long-term, gradual money laundering, improving risk identification coverage by 60% compared to existing solutions. 3. This invention relies on the TensorRT inference acceleration engine to achieve millisecond-level real-time risk detection, with a single transaction inference latency of ≤200ms. It supports simultaneous detection of tens of thousands of concurrent transactions and can complete risk assessment simultaneously when a transaction is submitted or a loan application is initiated, achieving pre-emptive interception rather than post-event tracing. The dynamic adaptive threshold is adjusted in real time according to the market and customer qualifications, reducing invalid manual review work orders caused by fixed thresholds by more than 50%, and significantly reducing the workload of manual risk control. The four-level graded handling instructions realize differentiated automated management and control, with low-risk transactions being fully automatically released and high-risk transactions being automatically intercepted and frozen. The efficiency of early warning is 50% higher than that of traditional T+1 offline risk control, significantly reducing bad debt losses for financial institutions. 4. This invention features a fully automated closed-loop iterative process, with lightweight monthly model updates. Freezing the underlying convolutional weights significantly shortens training time, eliminating the need for manual risk sample processing. A single technical solution can seamlessly adapt to four major financial scenarios: bank lending, payment anti-fraud, securities account monitoring, and insurance underwriting. Deployment can be completed quickly by simply adjusting feature weights and threshold coefficients, demonstrating strong versatility. The core federated fusion algorithm and hybrid attention deep learning model possess independent technical barriers, with no comparable open-source solutions that can directly replace them. After large-scale deployment, they can sustainably reduce the risk control manpower and system construction costs for financial institutions, making them highly valuable for commercial promotion. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the overall process of the financial risk intelligent identification and early warning method of the present invention.

[0023] Figure 2 This is a schematic diagram of the multi-source heterogeneous data fusion and federated learning architecture of the present invention.

[0024] Figure 3 This is a schematic diagram of the CNN-LSTM hybrid risk identification model structure of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] This invention provides two typical financial scenario implementation examples: Implementation Example 1, a bank retail credit risk control scenario, and Implementation Example 2, a third-party payment transaction anti-fraud scenario. Detailed parameters and comparative experimental data are provided for reference. Figures 1-3 Those skilled in the art can fully reproduce the technical solution of this invention without creative effort based on this embodiment.

[0027] Example 1 In the context of retail lending risk control in banks, this embodiment applies to the entire process of risk control for personal consumer loans and business loans in joint-stock commercial banks. It covers the entire business chain from loan application, monitoring of fund usage during the loan period, to post-loan overdue early warning, and fully executes steps S1 to S7 of claim 1. The detailed implementation parameters for each step are as follows: Step S1: Implementation and Configuration of Multi-Source Heterogeneous Data Access Internal data sources: core bank credit system, customer information management system, and People's Bank of China credit reporting system; external data sources: enterprise business registration information database, national court judicial litigation database, social media public opinion capture database, and financial industry blacklist database.

[0028] Collect heterogeneous data types: structured (customer ID card, income statement, credit report, historical loan record table), semi-structured (credit approval interface JSON message, system operation log), and unstructured (customer social media sentiment text, customer service complaint recording transcript).

[0029] The access gateway is configured with a Kafka cluster message queue, with a single node handling 5,000 loan application data streams per second, caching raw data in real time, with no data loss or delay.

[0030] Step S2: Data Standardization and Federated Integration Implementation Configuration Standardized process parameters: missing numerical fields are filled with the average of the same customer over the past 6 months; text entities are disambiguated using named entity recognition algorithms for business registration and personal names; timestamps are uniformly YYYY-MM-DD HH:MM:SS; amounts are uniformly converted to yuan; and character encoding is uniformly UTF-8.

[0031] Federated learning architecture deployment: The bank's local data center is the primary local node, and the industrial and commercial, judicial, and public opinion third-party institutions are each independent secondary nodes; each node completes standardized pre-extraction locally and only uploads gradient parameters to the federated collaboration center. The parameter transmission uses the Paillier homomorphic encryption algorithm for encryption; the collaboration center generates a global feature alignment matrix and distributes it to each node, which is then integrated to generate a unified credit risk dataset. Throughout the process, the original credit information and asset data of customers do not flow out of the bank's local data center, meeting the regulatory requirements for localized data.

[0032] Step S3: Multi-level four-dimensional feature extraction implementation configuration The feature channels are extracted in specific dimensions, resulting in a total of 216 dimensions for the fused feature vector: Customer static characteristics channel (62 dimensions): age, occupation category, monthly income range, property ownership mark, total debt, number of loans in the past 3 years, number of historical overdue payments, number of hard credit inquiries, etc. Transaction time series feature channel (74 dimensions): Time series features such as account transfer amount, transfer frequency, proportion of nighttime transactions, cross-regional transfer markers, large integer transfer markers, and repayment cycle fluctuations in the past 72 hours; Entity-related characteristic channels (48 dimensions): number of joint guarantors, corporate legal entity related levels, number of accounts with frequent fund transactions, and marking of untrustworthy related accounts; External public opinion characteristics (32 dimensions): frequency of negative public opinion keywords, number of judicial enforcement records, number of equity changes, and number of administrative penalty records.

[0033] All features are normalized and scaled to the 0-1 range using Min-Max, and then horizontally concatenated to form a 216-dimensional input vector.

[0034] Step S4: Implementation and Configuration of CNN-LSTM Multi-Head Attention Model Training Network fixed parameters: CNN layer convolution kernel 3×1, 16-channel convolution; LSTM hidden unit 128-dimensional; multi-head attention 4 attention heads; output layer 5 independent classification branches, corresponding to five types of credit risks: overdue, fraud, cash-out, default, and money laundering.

[0035] Sample library configuration: Collect 1.2 million credit samples from banks over the past 3 years, including 600,000 normal samples and 120,000 samples from each of the five risk categories, with a balanced sample ratio; Training hyperparameters: Adam optimizer learning rate 0.001, dropout coefficient 0.2, 80 iteration rounds, and five-fold cross-validation to select the optimal parameters; After training, export the TensorRT quantization inference model and store it on the credit risk control inference server.

[0036] Step S5: Real-time risk identification and reasoning implementation configuration Inference is triggered in real time when a loan application is submitted. The inference server takes 160ms to complete the inference for a single application, which is less than the 200ms limit. The server supports concurrent detection of 8,000 loan applications per second. The model outputs three types of results: risk code (02 overdue / 01 fraud / 04 cash-out, etc.), risk score of 0-100, and confidence level of 0-1, which are pushed to the early warning scheduling module simultaneously.

[0037] Step S6: Configure and implement dynamic threshold-based early warning system. Threshold calculation formula weight coefficients: α=0.7, β=0.15, γ=0.15; basic fixed threshold T0=60; ΔM is the monthly macro credit delinquency rate correction coefficient, which is automatically adjusted upward by 0-15 points during economic downturns; ΔR is the customer credit stratification correction coefficient, which lowers the threshold by 10 points for customers with good credit and raises the threshold by 10 points for customers with poor credit.

[0038] The Level 4 Disposal Rules have been implemented. Risk score < 60: Low risk, the system automatically approves and disburses loans without human intervention; 60 ≤ score < 80: Medium risk, loan application suspended, assigned risk control specialist to manually review credit history and public opinion information; Score 80 ≤ Score < 90: High risk, loan application rejected outright, customer marked for close monitoring; A score of 90 or higher indicates extremely high risk. The application will be rejected, and the client's information will be reported to the local financial regulatory bureau and added to the internal blacklist.

[0039] Step S7: Closed-loop iterative optimization implementation configuration After the credit specialist completes the manual review, the "genuine overdue / fraud / normal false alarm" labels are automatically fed back into the sample database; lightweight iterative training is started on the 15th of each month, freezing the weights of the CNN convolutional layers and only updating the parameters of the LSTM and attention layers. Each iteration of training takes 4 hours, and the online inference model is automatically replaced after training is completed.

[0040] Comparison of implementation results: Quantitative comparison of traditional rule-based risk control versus the proposed solution: Risk identification accuracy: 65% for traditional methods, 92% for this invention, an improvement of 27 percentage points; Underreporting rate: 36.2% for traditional methods, 9.1% for this invention, a decrease of 27.1 percentage points; False alarms requiring manual review: decreased by 51.3% per month; Single transaction detection latency: Traditional offline T+1 detection, this invention provides 160ms real-time detection; Credit default rate: Six months after the implementation of this program, the retail credit default rate decreased by 41.7%.

[0041] Example 2 Anti-fraud scenarios for third-party payment platform transactions This embodiment is applied to online transaction risk control of internet third-party payment platforms, covering all transaction scenarios such as e-commerce payment, transfer, recharge, and withdrawal. It fully executes all steps of claim 1, with the following differences in parameters compared to embodiment 1: Step S1: Add new data sources: device fingerprint database, IP address geolocation database, merchant business information database; add semi-structured device logs and payment messages; add unstructured merchant complaint texts.

[0042] Step S3 Feature Extraction Differentiation Adjustment: Increase the weight of features such as device replacement frequency, cross-regional IP, multi-merchant transactions in a short period of time, and large integer recharge in transaction time series features; focus on mining shared devices and shared IP related accounts of gangs in association features; add the frequency of keywords for merchant complaints of fraud in external public opinion features.

[0043] Step S6 Threshold weight coefficient adjustment: α=0.6, β=0.2, γ=0.2; base threshold T0=55, lower the base threshold for small-amount high-frequency transactions, and raise the correction coefficient for large-amount transfers.

[0044] Differentiated rules for tiered handling: High-risk transactions are intercepted in real time and trigger SMS facial verification; extremely high-risk transactions are directly frozen in payment accounts and reported to the anti-money laundering monitoring center simultaneously.

[0045] Traditional logistic regression risk control vs. the solution of this invention: Success rate of intercepting fraudulent, money laundering, and cash-out transactions: Traditional method 58%, this invention 89.4%; Compensation for financial losses: The platform's average monthly compensation has decreased by 48.6%; Real-time transaction detection latency: 132ms, supporting 10,000 concurrent payments per second; Early warning for new types of gang fraud: 7-15 days earlier than traditional methods.

[0046] The technical solution of this invention can be directly ported to scenarios such as securities account anomaly monitoring and insurance underwriting credit screening. Scenario switching only requires adjusting the feature dimension weights in step S3 and the dynamic threshold coefficients α / β / γ in step S6. The underlying federated fusion, CNN-LSTM attention model, and closed-loop iterative architecture do not need to be modified, demonstrating strong universality and adaptability. There are no special hardware requirements for deployment; ordinary x86 distributed server clusters can support it, eliminating the need for high-end dedicated AI computing chips. The engineering implementation cost is low, and it can be deployed on a large scale by small and medium-sized financial institutions.

[0047] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion, characterized in that, It includes the following steps: Step S1, Multi-source heterogeneous data access: Connect the financial institution's internal business data sources and external public data sources in parallel through a standardized data access gateway; the internal business data sources include the core transaction database, customer profile database, and credit database, while the external public data sources include the public opinion text database, business information database, judicial litigation database, and industry blacklist database; the gateway synchronously collects three types of raw data: structured transaction logs, semi-structured business messages, and unstructured public opinion texts; Step S2, Data Standardization and Federated Integration: The raw data obtained in Step S1 is subjected to unified processing including denoising, deduplication, missing value imputation, format conversion, and semantic alignment to construct a globally unified financial data dictionary; each local node of the data source independently completes feature pre-extraction and only uploads model gradient parameters to the federated collaboration center; each node completes cross-source feature alignment based on the shared gradient to generate a unified risk dataset across the entire domain. Step S3, Multi-level Feature Extraction: Extract customer static features, transaction time series features, entity association features, and external public opinion features from the unified risk dataset, and concatenate them to form a high-dimensional fusion feature vector; Step S4: Deep learning risk model training: Build a CNN-LSTM hybrid neural network and embed a multi-head attention layer. Configure the model output layer with a multi-label classification branch. Supervised training was completed using a labeled risk sample library, and hyperparameter tuning was completed through five-fold cross-validation to solidify the inference model. Step S5, Real-time Risk Identification and Reasoning: Input the real-time business data stream into the solidified reasoning model and output the risk category code, risk level value, and confidence decimal. Step S6, Dynamic Threshold Classification Early Warning: Retrieve market cycle parameters, customer segmentation tags, and historical risk fluctuation curves to calculate adaptive risk thresholds in real time; classify four levels of handling instructions based on risk level values ​​and push the handling instructions to the front-end risk control terminal; Step S7, Closed-loop Iterative Optimization: Record the results of each business warning and handling, return the handling labels to the labeled risk sample library, and re-execute step S4 on a monthly cycle to complete the model iteration update.

2. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, In step S2, the Federal Collaboration Center only receives gradient parameters and feature mapping matrices uploaded by each local node, and does not store any node's original business data. The entire cross-agency data interaction is encrypted using a homomorphic encryption algorithm.

3. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, In step S3, the static characteristics of the customer include the customer's age, occupation type, asset and liability values, and number of historical performance records; the transaction time sequence characteristics include the transaction amount range, transaction frequency, transaction time period, cross-regional transaction markers, and counterparty device fingerprints; the entity association characteristics include equity association links, guarantee relationships, and frequency of fund transfers; and the external public opinion characteristics include the frequency of negative keywords, the number of judicial dishonesty records, and the number of business registration changes.

4. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, In step S4, the CNN layer uses a 3×1 one-dimensional convolutional kernel to extract local features of a single transaction, the LSTM layer is set with 128-dimensional hidden units to capture 72-hour time-series behavioral sequences, and the multi-head attention layer is set with 4 groups of attention heads to assign weighted coefficients to high-risk features.

5. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, Step S4: The multi-label classification branch independently outputs independent codes for five types of risks: fraud, overdue payment, money laundering, default, and cash-out. Each type of risk is associated with a risk level value in the range of 0 to 100 and a confidence value in the range of 0 to 1.

6. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, Step S5 involves deploying the TensorRT inference acceleration engine during the inference phase. The latency for complete inference of a single business data item is no more than 200ms, and it supports real-time detection of 10,000 concurrent transactions per second.

7. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, Step S6: The adaptive risk threshold calculation formula is T=α T0+β ΔM+γ ΔR; where T0 is the base fixed threshold, ΔM is the market volatility correction coefficient for the current month, ΔR is the customer's historical risk correction coefficient, and α, β, and γ are preset weighting coefficients.

8. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, The specific rules for classifying the four levels of handling instructions in step S6 are as follows: If the risk level is <60, a low-risk release instruction is issued; if the risk level is 60 ≤ risk level <80, a medium-risk manual review instruction is issued; if the risk level is 80 ≤ risk level <90, a high-risk transaction interception instruction is issued; if the risk level is ≥90, an extremely high-risk account freeze and regulatory reporting instruction is issued.

9. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, Step S7: The re-entry samples are labeled to distinguish between positive and negative samples. New risk samples are divided into validation and training sets in a 3:7 ratio. During iterative training, the weights of the CNN's underlying convolutional layers are frozen, and only the parameters of the LSTM and attention layers are updated.

10. The method for intelligent identification and early warning of financial risks based on multi-source heterogeneous data fusion according to claim 1, characterized in that, This method is applicable to four types of financial business scenarios: bank retail credit risk control, third-party payment anti-fraud, securities account abnormal transaction monitoring, and insurance underwriting credit screening. In different scenarios, only the feature extraction dimension weight in step S3 and the threshold weight coefficient in step S6 are replaced.

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