Financial service risk early warning system based on big data
By building a big data financial service risk early warning system, the problem of difficulty in unifying the standards of multi-source heterogeneous data has been solved, stable early warning and handling in high-concurrency scenarios have been achieved, the false alarm rate and maintenance costs have been reduced, and inheritable strategies have been accumulated.
Patent Information
- Application Number
- CN202511398632.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-28
- Publication Date
- 2026-01-06
AI Technical Summary
Existing financial risk early warning systems struggle to unify the interpretation of heterogeneous data from multiple sources in scenarios with large-scale users and high-concurrency transactions. This results in poor early warning stability, a lack of cross-account and cross-device linkage and anomaly identification capabilities, a high false alarm rate, increased handling costs, and a lack of endogenous linkage and strategy accumulation.
Construct a big data-based financial service risk early warning system, including technical solutions: an element fusion module, a risk control assessment module, and an early warning audit module. This system achieves unified standards, quality verification, and element fusion of multi-source data, constructs account relationship diagrams and transaction timeline profiles, performs dual sample mining and causal disturbance detection, generates tiered early warnings, and links and handles and audits accordingly.
It improves the ability to identify anomalies in the linkage between cross-account transaction relationship graphs and time-series profiles, reduces false alarm rates, suppresses boundary jitter, achieves stable output of early warning and handling, and accumulates inheritable strategy fragments to support review and compliance checks, thereby reducing maintenance costs.
Smart Images
Figure CN121280147A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of financial technology, specifically to a financial service risk early warning system based on big data. Background Technology
[0002] Risk warning in financial services relies on continuous monitoring and comprehensive judgment of transactions, accounts, credit lines, channel devices, and external credit information. Traditional solutions mainly rely on static rules and a few scoring models, depending on threshold triggers, and have limited ability to identify anomalies across institutions, channels, and time periods. In scenarios with large-scale users and high-concurrency transactions, heterogeneous data sources, fluctuating quality, and timeliness requirements coexist, necessitating both rapid response and ensuring explainability and auditability.
[0003] Existing financial risk early warning systems suffer from multiple sources and heterogeneity, making it difficult to unify standards. Delays and omissions lead to feature drift, severely impacting the stability of early warnings. They rely heavily on static thresholds and single-point scoring, failing to identify chain-like fund transfers, cross-account collaborations, and cross-device replays. The lack of tiered safeguards and hysteresis mechanisms results in high boundary fluctuations and false alarm rates, increasing handling costs. Furthermore, there is a lack of intrinsic linkage between early warning and handling, and between auditing and tracing, making it difficult to distill experience into reusable strategy fragments. Therefore, designing a big data-based financial service risk early warning system is essential. Summary of the Invention
[0004] The purpose of this invention is to provide a financial service risk early warning system based on big data to solve the problems mentioned in the background art.
[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a big data-based financial service risk early warning system, comprising an element fusion module, a risk control assessment module, and an early warning audit module. The element fusion module is used to unify the scope, verify the quality, and fuse elements of multi-source financial data to form computable data assets. The risk control assessment module is used to construct account relationship diagrams and transaction time-series profiles, perform dual sample mining and causal disturbance detection, and output multi-dimensional risk scores and explanatory elements. The early warning audit module is used to generate tiered early warnings based on layered guardrails and hysteresis strategies, coordinate handling, and complete audit traceability and strategy backfeeding.
[0006] According to the above technical solution, the element fusion module includes an access adaptation unit, a quality verification unit, an element alignment unit, and a drift monitoring unit. The access adaptation unit is connected to the transaction system, account system, device fingerprint platform, merchant system, and external credit interface via a data bus, and is responsible for multi-source data access, field mapping, and incremental extraction. The quality verification unit is connected to the access adaptation unit via a signal and is responsible for integrity verification, timestamp consistency verification, outlier identification and missing value completion, and outputs quality labels. The element alignment unit is connected to the quality verification unit via a signal and is responsible for uniformly encoding and time-aligning accounts, devices, merchants, channels, geographical locations, and account binding relationships to form a traceable element view. The drift monitoring unit is connected to the element alignment unit via a signal and is responsible for monitoring feature distribution and input data caliber drift, and outputting drift alarms and rollback strategies.
[0007] The risk control assessment module includes a relationship graph construction unit, a time-series profiling unit, a dual sample mining unit, a causal disturbance detection unit, and a multi-dimensional assessment unit. The relationship graph construction unit is connected to the element alignment unit via a signal to generate a transaction relationship graph across accounts, devices, and merchants, and outputs structural indicators of nodes and edges and community division results. The time-series profiling unit is connected to the element alignment unit via a signal to establish time-series profiles of accounts, devices, and merchants, and outputs baselines and fluctuation boundaries for amount, frequency, cycle, channel, and regional migration. The dual sample mining unit is connected to the relationship graph construction unit and the time-series profiling unit via a signal to construct pairs of different behaviors in similar profile sets, forming dual difference features to improve separability. The causal disturbance detection unit is connected to the time-series profiling unit via a signal to identify the sequential relationship between abnormal triggers and outcome variables, and produces interpretable disturbance evidence elements. The multi-dimensional assessment unit is connected to the dual sample mining unit and the causal disturbance detection unit via a signal to fuse structural indicators, dual differences, and disturbance evidence to generate a comprehensive risk score and credibility, and outputs score decomposition to support interpretation.
[0008] The early warning audit module includes a hierarchical guardrail unit, a handling orchestration unit, an audit recording unit, and a strategy reinjection unit. The hierarchical guardrail unit is connected to the multi-dimensional evaluation unit via a signal, sets early warning thresholds, recovery thresholds, and hysteresis windows, and outputs hierarchical early warning and recovery strategies to suppress boundary jitter. The handling orchestration unit is connected to the payment gateway, credit engine, and customer service system via a control system, mapping hierarchical early warnings into executable action sequences of credit limit reduction, delay, review, and freeze, and collecting execution receipts. The audit recording unit is connected to the hierarchical guardrail unit and the handling orchestration unit via a signal, recording early warning evidence, handling trajectory, manual review conclusions, and customer communication minutes to form an auditable evidence chain. The strategy reinjection unit is connected to the audit recording unit via a signal, extracting effective rules and model fragments and reinjecting them into the element and evaluation layers, thus accumulating them into inheritable strategy assets.
[0009] Based on the above technical solution, the system operates as follows:
[0010] S1. Complete multi-source access, quality verification, element alignment and drift monitoring to form computable data assets and quality labels;
[0011] S2. Construct a transaction relationship diagram and time series profile, and output structural indicators, baselines and volatility boundaries;
[0012] S3. Perform dual sample mining and causal perturbation detection to generate a comprehensive risk score and credibility and produce explanatory elements;
[0013] S4. Generate graded early warning and recovery strategies based on layered guardrails and hysteresis windows, and arrange the sequence of handling actions;
[0014] S5. Implement the action, record the chain of evidence and review the conclusions, reinject effective strategy fragments into the elements and evaluation layers, and update the model and guardrail parameters.
[0015] According to the above technical solution, S1 specifically refers to:
[0016] S1-1. Complete field mapping and incremental extraction for transaction, account, device, merchant and external credit data, perform integrity, uniqueness and timestamp consistency checks, identify outliers and sudden increases and decreases, and generate record-level quality labels and missing source markers;
[0017] S1-2. Unify the encoding and time alignment of accounts, devices, merchants, channels, and geographic elements to form an element view; monitor feature distribution and image stability within a sliding time window, output drift alarms and rollback suggestions to ensure that subsequent features and models work under consistent standards, and make fine-grained threshold adjustments for different image clusters according to drift intensity using the incremental formula ΔA=μMi / M0(1-k%), where μ is the conversion coefficient for converting drift intensity into guardrail increment, Mi is the drift intensity measure of the i-th image cluster in the current time window, M0 is the corresponding baseline intensity, and k% is the noise reduction ratio based on quality label and delay label to obtain the adaptive threshold Ai=A0+μMi / M0(1-k%), where A0 is the global guardrail baseline threshold.
[0018] According to the above technical solution, S2 specifically refers to:
[0019] S2-1. Generate a transaction relationship graph on data with unified standards. The nodes are accounts and devices, and the edges are funds and login interactions. Calculate connectivity, betweenness, in-out degree, ternary closure and community division to form cross-account and cross-device linkage features.
[0020] S2-2. Establish time-series profiles for accounts, devices, and merchants, providing baselines and fluctuation boundaries for amounts, frequencies, cycles, channels, and geographical migrations, and marking differentiated patterns before and after holidays and during nighttime hours to provide context for subsequent anomaly identification and interpretation.
[0021] According to the above technical solution, S3 specifically refers to:
[0022] S3-1. Construct pairs of dual samples with similar subjects and different behaviors within the set of similar profiles, and extract key difference dimensions, such as sudden cross-regional fund transfers, unconventional equipment reuse and short-cycle cyclical transactions under the same profile, to form dual difference features to enhance separability.
[0023] S3-2. Detect the sequential relationship between abnormal triggers and outcome variables on the behavioral timeline, identify intervention signs and outcome responses, and generate interpretable perturbation evidence; integrate structural indicators, dual differences, and perturbation evidence into a comprehensive risk score and credibility, and output score decomposition to support the threshold setting of graded guardrails.
[0024] According to the above technical solution, S4 specifically refers to:
[0025] S4-1. Based on comprehensive risk scores and credibility, set multi-level early warning thresholds and recovery thresholds, set hysteresis windows to avoid frequent triggering at the boundary, give the handling priority and recovery conditions for each level, map the graded early warnings into action sequences of reduction, delay, manual review and freezing, issue according to channel capabilities and collect execution receipts; record handling delays, customer interactions and feedback status during the execution process to ensure that the handling is replayable and auditable;
[0026] S4-2. Monitor the real-time handling intensity corresponding to the graded early warning. The real-time handling intensity refers to the combined intensity of reduction, delay and freezing executed on the current channel and account set. It is required that it is not higher than the theoretical handling upper limit Pi of the same level strategy. If the real-time handling intensity increases and causes a reverse feedback jump, it is manifested as transaction return, surge in customer complaints and increase in false alarms. Record the actual handling upper limit P0a at this moment. Compare the difference between the theoretical handling upper limit and the actual handling upper limit with the difference of the previous time window: when (Pi-P0a) is larger than (P(i-1)-P0b), it is determined that the trend of the difference change is in an expanding state. In order to suppress overshoot and boundary oscillation, the handling upper limit is self-calibrated. The calibrated handling upper limit PE is calculated as Pi-γ(Pi-P0a) / (P(i-1)-P0b), where γ is the handling calibration coefficient used to control the calibration amplitude. When (Pi-P0a) is not large compared with (P(i-1)-P0b), it is determined that the trend of the difference change is in a converging state, and PE=Pi is maintained.
[0027] According to the above technical solution, S5 specifically refers to:
[0028] S5-1. Automatically handle graded early warnings and trigger manual review, record evidence, dialogue minutes and final conclusions to form an evidence chain with time sequence and subject association;
[0029] S5-2. Write the evidence chain and handling results into the audit database, extract effective rules and model fragments and feed them back to the elements and evaluation layers, and update the layered guardrail parameters and hysteresis windows in sync, so that the system can continuously improve on new samples and maintain consistency.
[0030] Compared with the prior art, the beneficial effects achieved by the present invention are: the present invention constructs a unified data governance and element alignment system, forms a cross-account transaction relationship diagram and time series profile, introduces dual sample mining, causal disturbance detection and layered guardrail strategy, generates hierarchical early warning and linkage handling and audit traceability;
[0031] Cross-account relationship graphs and time-series profiles enhance the linkage and anomaly identification, resulting in higher recall and lower false alarms for chain transfers and collaborative arbitrage. Layered guardrails and hysteresis windows suppress boundary jitter, maintaining stable early warning output in high-concurrency scenarios. Integrated handling and auditing accumulate inheritable strategy fragments, supporting retrospective review and compliance checks, and reducing maintenance costs. Attached Figure Description
[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0033] Figure 1 This is a schematic diagram of the overall modular structure of the present invention. Detailed Implementation
[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0035] Please see Figure 1 The present invention provides a technical solution: a financial service risk early warning system based on big data, including an element fusion module, a risk control assessment module, and an early warning audit module. The element fusion module is used to unify the scope, verify the quality, and fuse elements of multi-source financial data to form computable data assets. The risk control assessment module is used to construct an account relationship diagram and a transaction time sequence profile, perform dual sample mining and causal disturbance detection, and output multi-dimensional risk scores and explanatory elements. The early warning audit module is used to generate graded early warnings based on layered guardrails and hysteresis strategies, link up and handle, and complete audit traces and strategy backfeeding.
[0036] The element fusion module includes an access adaptation unit, a quality verification unit, an element alignment unit, and a drift monitoring unit. The access adaptation unit is connected to the transaction system, account system, device fingerprint platform, merchant system, and external credit interface via a data bus. It is responsible for multi-source data access, field mapping, and incremental extraction. The quality verification unit is connected to the access adaptation unit via a signal. It is responsible for integrity verification, timestamp consistency verification, outlier identification, and missing value completion, and outputs quality labels. The element alignment unit is connected to the quality verification unit via a signal. It is responsible for uniformly encoding and aligning the binding relationships of accounts, devices, merchants, channels, geographical locations, and accounts to form a traceable element view. The drift monitoring unit is connected to the element alignment unit via a signal. It is responsible for monitoring feature distribution and input data caliber drift, and outputting drift alarms and rollback strategies.
[0037] The risk control assessment module includes a relationship graph construction unit, a time-series profiling unit, a dual sample mining unit, a causal disturbance detection unit, and a multi-dimensional assessment unit. The relationship graph construction unit is connected to the element alignment unit via a signal to generate a transaction relationship graph across accounts, devices, and merchants, and outputs structural indicators of nodes and edges and community division results. The time-series profiling unit is connected to the element alignment unit via a signal to establish time-series profiles of accounts, devices, and merchants, and outputs baselines and fluctuation boundaries for amount, frequency, cycle, channel, and geographic migration. The dual sample mining unit is connected to the relationship graph construction unit and the time-series profiling unit via a signal to construct pairs of different behaviors in similar profile sets, forming dual difference features to improve separability. The causal disturbance detection unit is connected to the time-series profiling unit via a signal to identify the sequential relationship between abnormal triggers and outcome variables, and produces interpretable disturbance evidence elements. The multi-dimensional assessment unit is connected to the dual sample mining unit and the causal disturbance detection unit via a signal to integrate structural indicators, dual differences, and disturbance evidence to generate a comprehensive risk score and credibility, and outputs score decomposition to support interpretation.
[0038] The early warning audit module includes a hierarchical guardrail unit, a handling orchestration unit, an audit recording unit, and a strategy reinjection unit. The hierarchical guardrail unit is connected to the multi-dimensional evaluation unit via signals to set early warning thresholds, recovery thresholds, and hysteresis windows, and outputs hierarchical early warning and recovery strategies to suppress boundary jitter. The handling orchestration unit is connected to the payment gateway, credit engine, and customer service system via control to map hierarchical early warnings into executable action sequences of credit reduction, delay, review, and freezing, and collects execution receipts. The audit recording unit is connected to the hierarchical guardrail unit and the handling orchestration unit via signals to record early warning evidence, handling trajectory, manual review conclusions, and customer communication minutes to form an auditable evidence chain. The strategy reinjection unit is connected to the audit recording unit via signals to extract effective rules and model fragments and reinject them into the element and evaluation layers, accumulating them into inheritable strategy assets.
[0039] The system operates as follows:
[0040] S1. Complete multi-source access, quality verification, element alignment and drift monitoring to form computable data assets and quality labels;
[0041] S2. Construct a transaction relationship diagram and time series profile, and output structural indicators, baselines and volatility boundaries;
[0042] S3. Perform dual sample mining and causal perturbation detection to generate a comprehensive risk score and credibility and produce explanatory elements;
[0043] S4. Generate graded early warning and recovery strategies based on layered guardrails and hysteresis windows, and arrange the sequence of handling actions;
[0044] S5. Implement the action, record the chain of evidence and review the conclusions, reinject effective strategy fragments into the elements and evaluation layers, and update the model and guardrail parameters.
[0045] S1 specifically refers to:
[0046] S1-1. Complete field mapping and incremental extraction for transaction, account, device, merchant and external credit data, perform integrity, uniqueness and timestamp consistency checks, identify outliers and sudden increases and decreases, and generate record-level quality labels and missing source markers;
[0047] S1-2. Unify the coding and time alignment of accounts, devices, merchants, channels and geographic elements to form an element view; monitor feature distribution and image stability within a sliding time window, output drift alarms and rollback suggestions to ensure that subsequent features and models work under consistent standards, and make fine-grained threshold adjustments for different image clusters according to drift intensity. The incremental formula ΔA=μMi / M0(1-k%) is used, where μ is the conversion coefficient for converting drift intensity into guardrail increment, Mi is the drift intensity measure of the i-th image cluster in the current time window, M0 is the corresponding baseline intensity, and k% is the noise reduction ratio based on quality label and delay label to obtain the adaptive threshold Ai=A0+μMi / M0(1-k%), where A0 is the global guardrail baseline threshold.
[0048] S2 specifically refers to:
[0049] S2-1. Generate a transaction relationship graph on data with unified standards. The nodes are accounts and devices, and the edges are funds and login interactions. Calculate connectivity, betweenness, in-out degree, ternary closure and community division to form cross-account and cross-device linkage features.
[0050] S2-2. Establish time-series profiles for accounts, devices, and merchants, providing baselines and fluctuation boundaries for amount, frequency, cycle, channel, and geographic migration, and marking differentiated patterns before and after holidays and during nighttime hours to provide context for subsequent anomaly identification and interpretation.
[0051] S3 specifically refers to:
[0052] S3-1. Construct pairs of dual samples with similar subjects and different behaviors within the set of similar profiles, and extract key difference dimensions, such as sudden cross-regional fund transfers, unconventional equipment reuse and short-cycle cyclical transactions under the same profile, to form dual difference features to enhance separability.
[0053] S3-2. Detect the sequential relationship between abnormal triggers and outcome variables on the behavioral timeline, identify intervention signs and outcome responses, and generate interpretable perturbation evidence; integrate structural indicators, dual differences, and perturbation evidence into a comprehensive risk score and credibility, and output score decomposition to support the threshold setting of graded guardrails.
[0054] S4 specifically refers to:
[0055] S4-1. Based on comprehensive risk scores and credibility, set multi-level early warning thresholds and recovery thresholds, set hysteresis windows to avoid frequent triggering at the boundary, give the handling priority and recovery conditions for each level, map the graded early warnings into action sequences of reduction, delay, manual review and freezing, issue according to channel capabilities and collect execution receipts; record handling delays, customer interactions and feedback status during the execution process to ensure that the handling is replayable and auditable;
[0056] S4-2. Monitor the real-time handling intensity corresponding to the graded early warning. The real-time handling intensity refers to the combined intensity of reduction, delay and freezing executed on the current channel and account set. It is required that it is not higher than the theoretical handling upper limit Pi of the same level strategy. If the real-time handling intensity increases and causes a reverse feedback jump, it is manifested as transaction return, surge in customer complaints and increase in false alarms. Record the actual handling upper limit P0a at this moment. Compare the difference between the theoretical handling upper limit and the actual handling upper limit with the difference of the previous time window: when (Pi-P0a) is larger than (P(i-1)-P0b), it is determined that the trend of the difference change is in an expanding state. In order to suppress overshoot and boundary oscillation, the handling upper limit is self-calibrated. The calibrated handling upper limit PE is calculated as Pi-γ(Pi-P0a) / (P(i-1)-P0b), where γ is the handling calibration coefficient used to control the calibration amplitude. When (Pi-P0a) is not large compared with (P(i-1)-P0b), it is determined that the trend of the difference change is in a converging state, and PE=Pi is maintained.
[0057] S5 specifically refers to:
[0058] S5-1. Automatically handle graded early warnings and trigger manual review, record evidence, dialogue minutes and final conclusions to form an evidence chain with time sequence and subject association;
[0059] S5-2. Write the evidence chain and handling results into the audit database, extract effective rules and model fragments and feed them back to the elements and evaluation layers, and update the layered guardrail parameters and hysteresis windows in sync, so that the system can continuously improve on new samples and maintain consistency.
[0060] This invention constructs a unified data governance and element alignment system, forms a cross-account transaction relationship diagram and time-series profile, introduces dual sample mining, causal disturbance detection and hierarchical guardrail strategy, generates hierarchical early warning and linkage handling and audit traceability;
[0061] Cross-account relationship graphs and time-series profiles enhance the linkage and anomaly identification, resulting in higher recall and lower false alarms for chain transfers and collaborative arbitrage. Layered guardrails and hysteresis windows suppress boundary jitter, maintaining stable early warning output in high-concurrency scenarios. Integrated handling and auditing accumulate inheritable strategy fragments, supporting retrospective review and compliance checks, and reducing maintenance costs.
[0062] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0063] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A big data-based financial service risk early warning system, characterized in that: The system comprises an element fusion module, a risk control evaluation module, and a pre-warning audit module, the element fusion module is used for caliber unification, quality check and element fusion of multi-source financial data to form computable data assets, the risk control evaluation module is used for constructing an account relationship graph and a transaction time sequence portrait, conducting dual sample mining and causal disturbance detection, and outputting multi-dimensional risk scores and explanation elements, and the pre-warning audit module is used for generating hierarchical pre-warning based on hierarchical guardrails and hysteresis strategies, linking treatment, and completing audit traces and strategy backfilling. 2.The big data-based financial service risk early warning system according to claim 1, characterized in that: The element fusion module comprises an access adaptation unit, a quality check unit, an element alignment unit, and a drift monitoring unit, the access adaptation unit is connected to a transaction system, an account system, a device fingerprint platform, a merchant system, and an external credit investigation interface through a data bus, is responsible for multi-source data access, field mapping, and incremental extraction, the quality check unit is connected to the access adaptation unit through a signal, is responsible for integrity check, timestamp consistency verification, abnormal value identification, and missing value filling, and outputs quality labels, the element alignment unit is connected to the quality check unit through a signal, is responsible for unified coding and time alignment of account, device, merchant, channel, geographic location, and account binding relationships, forms a traceable element view, and the drift monitoring unit is connected to the element alignment unit through a signal, is responsible for monitoring feature distribution and input data caliber drift, and outputs drift alarm and rollback strategies; The risk control evaluation module comprises a relationship graph construction unit, a time sequence portrait unit, a dual sample mining unit, a causal disturbance detection unit, and a multi-dimensional evaluation unit, the relationship graph construction unit is connected to the element alignment unit through a signal, generates a transaction relationship graph across accounts, devices, and merchants, outputs structure indicators and community division results of nodes and edges, the time sequence portrait unit is connected to the element alignment unit through a signal, establishes a time sequence portrait of accounts, devices, and merchants, and outputs baseline and fluctuation boundaries of amount, frequency, period, channel, and regional migration, the dual sample mining unit is connected to the relationship graph construction unit and the time sequence portrait unit through a signal, constructs difference behavior pairs in a similar portrait set to form dual difference features to improve separability, the causal disturbance detection unit is connected to the time sequence portrait unit through a signal, identifies the sequence of abnormal triggers and result variables, and outputs explainable disturbance evidence elements, and the multi-dimensional evaluation unit is connected to the dual sample mining unit and the causal disturbance detection unit through a signal, fuses structure indicators, dual differences, and disturbance evidence to generate comprehensive risk scores and credibility, and outputs score decomposition to support explanation. The pre-warning audit module comprises a hierarchical guardrail unit, a treatment arrangement unit, an audit trace unit and a strategy backfill unit, the hierarchical guardrail unit is connected to the multi-dimensional evaluation unit, the pre-warning threshold, the recovery threshold and the hysteresis window are set, the hierarchical pre-warning and the recovery strategy are output, the boundary jitter is inhibited, the treatment arrangement unit is connected to the payment gateway, the credit engine and the customer service system through control, the hierarchical pre-warning is mapped into the executable action sequence of the reduction, the delay, the review and the freezing, and the execution receipt is recovered, the audit trace unit is connected to the hierarchical guardrail unit and the treatment arrangement unit through the signal, the pre-warning evidence, the treatment track, the artificial review conclusion and the customer communication minutes are recorded, the evidence chain that can be audited is formed, the strategy backfill unit is connected to the audit trace unit through the signal, the effective rules and model fragments are extracted and backfilled to the elements and the evaluation layer, and are deposited as the inheritable strategy assets. 3.The big data-based financial service risk early warning system according to claim 2, characterized in that: The operation method of the system is: S1, complete multi-source access, quality verification, element alignment and drift monitoring, form a computable data asset and quality label; S2, construct a transaction relationship graph and a time sequence portrait, output structure indicators, baselines and fluctuation boundaries; S3, conduct dual sample mining and causal disturbance detection, generate comprehensive risk scores and credibility and output explanation elements; S4, generate hierarchical pre-warning and recovery strategy according to hierarchical guardrail and hysteresis window, and arrange treatment action sequence; S5, execute treatment, record evidence chain and review conclusion, backfill effective strategy fragments to elements and evaluation layer, update model and guardrail parameters.
4. The big data-based financial service risk early warning system according to claim 3, characterized in that: The S1 is specifically: S1-1, field mapping and incremental extraction are performed on transactions, accounts, devices, merchants and external credit investigation data, integrity, uniqueness and timestamp consistency verification is performed, abnormal values and sudden increases and decreases are identified, and record-level quality labels and missing source markers are generated; S1-2, unified coding and time alignment are performed on account, device, merchant, channel and geographic elements, forming an element view; monitor feature distribution and portrait stability in a sliding time window, output drift alarm and rollback suggestion, ensure that subsequent features and models work under consistent specifications, make fine-grained threshold adjustments for different portrait clusters according to drift intensity, use the incremental formula ΔA=μMi / M0(1-k%), where μ is the conversion coefficient for converting drift intensity to guardrail increment, Mi is the drift intensity measure of the i-th portrait cluster in the current time window, M0 is the corresponding baseline intensity, and k% is the noise reduction proportion based on quality label and delay label, to obtain the adaptive threshold Ai of the portrait cluster Ai=A0+μMi / M0(1-k%), where A0 is the global guardrail baseline threshold.
5. The big data-based financial service risk early warning system according to claim 4, characterized in that: The S2 is specifically: S2-1, generate a transaction relationship graph on unified specification data, nodes are accounts and devices, edges are fund and login interactions, calculate connectivity, intermediate, in-out degree, three-closure and community division, form cross-account and cross-device linkage features; S2-2, establish time sequence portraits for accounts, devices and merchants, give baselines and fluctuation boundaries of amount, frequency, period, channel and geographical migration, mark the difference between pre-holiday and post-holiday and night period, provide context for subsequent anomaly identification and explanation.
6. The big data-based financial service risk early warning system according to claim 5, characterized in that: The S3 is specifically: S3-1, constructing a pair of samples of similar image sets, extracting key difference dimensions, such as sudden cross-regional fund jumping, unconventional equipment reuse and short-cycle transaction under the same image, forming a pair of difference features to enhance the separability; S3-2, detecting the sequence of abnormal trigger and result variable on the behavior timeline, identifying intervention signs and result response, and outputting interpretable disturbance evidence; fuse the structure index, the pair of difference and the disturbance evidence into the comprehensive risk score and the credibility, and output the score decomposition to support the threshold setting of the hierarchical guardrail.
7. The big data-based financial service risk early warning system according to claim 6, characterized in that: The S4 is specifically: S4-1, setting multi-level early warning thresholds and recovery thresholds according to the comprehensive risk score and the credibility, setting a hysteresis window to avoid frequent triggering at the boundary, giving the disposal priority and recovery condition of each level, mapping the hierarchical early warning into the action sequence of reduction, delay, manual review and freezing, issuing and recovering the execution feedback according to the channel capacity; record the disposal delay, customer interaction and feedback state during the execution process to ensure that the disposal is replayable and auditable; S4-2, monitoring the real-time disposal intensity corresponding to the hierarchical early warning, the real-time disposal intensity refers to the intensity of reduction, delay and freezing executed on the current channel and account set, which should not be higher than the theoretical disposal upper limit Pi of the same level strategy, if the real-time disposal intensity increases and causes reverse feedback jump, which is manifested as transaction backflow, increasing complaints and false positive rising, record the actual disposal upper limit P0a at this moment, compare the difference between the theoretical disposal upper limit and the actual disposal upper limit with the difference of the last time window: when (Pi-P0a) is larger than (P(i-1)-P0b), it is determined that the difference value change trend is in the expansion state, in order to suppress overshoot and boundary oscillation, the disposal upper limit is self-calibrated, the calibrated disposal upper limit PE=Pi-γ(Pi-P0a) / (P(i-1)-P0b), wherein γ is a disposal calibration coefficient for controlling the calibration amplitude; when (Pi-P0a) is not larger than (P(i-1)-P0b), it is determined that the difference value change trend is in the convergence state, and the PE=Pi is maintained. 8.The big data-based financial service risk early warning system according to claim 7, characterized in that: The S5 is specifically: S5-1, automatically disposing the hierarchical early warning and triggering manual review, recording evidence, dialogue summary and final conclusion to form an evidence chain with time sequence and subject association; S5-2, write the evidence chain and disposal result into the audit library, extract effective rules and model fragments to backfill into the elements and evaluation layer, update the hierarchical guardrail parameters and hysteresis window synchronously, so that the system continuously improves on new samples and maintains consistent caliber.