Data-driven financial institution decision analysis method and related apparatus

Abstract

The embodiment of the application relates to the field of data processing, and provides a financial institution decision analysis method based on data driving and related devices, which comprises the following steps: obtaining endogenous business data set and exogenous correlation data set of a financial market; performing business expansion analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining business expansion analysis data set; performing risk management analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining risk management analysis data set; performing resource allocation analysis processing according to the endogenous business data set and the exogenous correlation data set of the financial market, obtaining resource allocation analysis data set; performing conflict checking processing according to the business expansion analysis data set, the risk management analysis data set and the resource allocation analysis data set, and performing decision suggestion quantization output, obtaining a financial institution decision analysis result, and more accurate financial institution decision analysis can be realized.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a data-driven decision analysis method and related apparatus for financial institutions. Background Technology

[0002] With the accelerated globalization and digitalization of financial markets, financial institutions face increasingly fierce market competition, and customer needs are becoming more diversified and personalized. At the same time, external uncertainties such as macroeconomic fluctuations, industry policy adjustments, and cross-market risk transmission have increased significantly. Against this backdrop, the precision of business expansion, the foresight of risk management, and the efficiency of resource allocation have become key supports for the core competitiveness of financial institutions. Decision analysis, as a crucial link connecting data and business actions, directly impacts the market adaptability and sustainable development capabilities of financial institutions. Currently, financial institutions generally rely on simple internal and external data for decision analysis, failing to achieve deep integration and value mining of internal and external data. This results in fragmented decision-making basis, delayed risk response, and misallocation of resources, making it difficult to formulate accurate and implementable decision solutions. Summary of the Invention

[0003] This application provides a data-driven decision analysis method and related apparatus for financial institutions, which can obtain more accurate decision analysis results for financial institutions and facilitate more accurate decision analysis for financial institutions.

[0004] The first aspect of this application provides a data-driven decision analysis method for financial institutions, which includes: Acquire endogenous business datasets from financial institutions and exogenous correlation datasets from financial markets; Business expansion analysis dataset is obtained by performing business expansion analysis processing based on the endogenous business dataset of financial institutions and the exogenous correlation dataset of financial markets. Risk management analysis datasets are obtained by performing risk management analysis on endogenous business datasets of financial institutions and exogenous correlation datasets of financial markets. Resource allocation analysis dataset is obtained by performing resource allocation analysis on the endogenous business dataset of financial institutions and the exogenous correlation dataset of financial markets. Conflict verification is performed on the business development analysis dataset, risk management analysis dataset, and resource allocation analysis dataset to obtain the financial institution verification analysis dataset. Based on the financial institution verification and analysis dataset, decision recommendations are quantitatively output to obtain the financial institution decision analysis results.

[0005] A second aspect of this application provides a data-driven decision analysis device for financial institutions, comprising: The acquisition unit is used to acquire endogenous business datasets of financial institutions and exogenous correlation datasets of financial markets. The first processing unit is used to perform business expansion analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset. The second processing unit is used to perform risk management analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a risk management analysis dataset. The third processing unit is used to perform resource allocation analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a resource allocation analysis dataset. The fourth processing unit is used to perform conflict verification processing based on the business expansion analysis dataset, the risk management analysis dataset, and the resource allocation analysis dataset to obtain the financial institution verification analysis dataset. The fifth processing unit is used to quantify and output decision recommendations based on the financial institution's verification and analysis dataset, thereby obtaining the financial institution's decision analysis results.

[0006] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0007] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0008] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0009] Implementing the embodiments of this application has the following beneficial effects: By acquiring internal business datasets from financial institutions and external correlation datasets from financial markets, business expansion analysis can be performed to obtain a business expansion analysis dataset. Risk management analysis can also be performed to obtain a risk management analysis dataset. Furthermore, resource allocation analysis can be performed to obtain a resource allocation analysis dataset. Conflict verification can then be performed on these datasets to obtain a financial institution verification analysis dataset. Finally, decision recommendations can be quantitatively output based on this dataset, resulting in more accurate financial institution decision analysis results, thus facilitating more precise financial institution decision analysis. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This application provides a schematic diagram of the structure of a data-driven decision analysis method for financial institutions. Figure 2 This application provides a flowchart illustrating a data-driven decision analysis method for financial institutions. Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 4 This application provides a schematic diagram of the structure of a data-driven decision analysis device for financial institutions. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] To better understand the data-driven decision analysis method for financial institutions provided in this application, a brief introduction to existing financial institution decision analysis technologies is given below. Existing decision analysis generally suffers from insufficient data integration. Endogenous business data mining remains superficial, and exogenous market data is used in a formalistic manner, failing to achieve deep correlation and resulting in fragmented decision-making basis. Risk assessment is centered on static data, lacking dynamic risk signal capture and multi-type risk linkage analysis, and mitigation measures lack personalized adaptation. Resource allocation relies heavily on experience, failing to accurately link with customer value and business potential, and failing to dynamically adapt to market changes, resulting in resource misallocation and waste. Decision output is mainly qualitative, lacking quantitative implementation basis, and lacks conflict verification mechanisms for the three types of decisions, leading to a disconnect between decision-making and execution. These problems render existing decision analysis inaccurate and unable to adapt to complex market environments and diversified business needs.

[0014] To address the aforementioned issues, this application provides a data-driven decision analysis method for financial institutions. This method can construct a decision analysis system that deeply integrates internal and external data, coordinates static and dynamic risk assessment, accurately adapts resource allocation, and quantifies and implements decision outputs. This enhances the scientific nature and operability of decision-making and can provide strong support for the business development of financial institutions.

[0015] Please see Figure 1 , Figure 1 A schematic diagram of a data-driven decision analysis system for financial institutions is shown. For example... Figure 1As shown, a data-driven decision analysis system for financial institutions can include a data acquisition module, a business development analysis module, a risk management analysis module, a resource allocation analysis module, a conflict verification module, and a decision recommendation quantitative output module. The system comprises several modules: a data acquisition module for collecting internal business data from financial institutions and externally related data from the financial market; a business expansion analysis module for extracting customer value characteristics from internal and external data, constructing a mapping relationship between market conditions and product demand, calculating a comprehensive business expansion score and prioritizing data, and outputting a business expansion analysis dataset; a risk management analysis module for extracting static risk characteristics and dynamic risk signals, quantifying multiple types of dynamic risks, adjusting dynamic risk levels and matching risk mitigation measures, and generating a risk management analysis dataset; a resource allocation analysis module for analyzing the current status and output benefits of resource input, combining external environmental adaptability and compliance constraints, formulating and correcting resource allocation plans, and generating a resource allocation analysis dataset; a conflict verification module for introducing dynamic weights of strategic objectives, verifying conflict points among the three analysis results (business expansion, risk management, and resource allocation), resolving contradictions through adjustment methods, and outputting a verification analysis dataset; and a decision recommendation quantitative output module for transforming the verification analysis dataset into hierarchical quantitative decision results, providing corresponding decision content to management, business personnel, and risk control personnel to support precise execution.

[0016] Please see Figure 2 , Figure 2 This application provides a flowchart illustrating a data-driven decision analysis method for financial institutions. For example... Figure 2 As shown, the data-driven decision analysis method for financial institutions includes: S10: Obtain the endogenous business dataset of financial institutions and the exogenous correlation dataset of financial markets.

[0017] The internal business data of financial institutions may include one or more internal business data sets of financial institutions. These internal business data can refer to data generated within the financial institution that is directly related to its own business. Internal business data of financial institutions may include, but is not limited to, basic customer information, account transaction data, credit business data, product holding data, service interaction data, special resource management data, and risk mitigation data, etc., and this application does not impose any restrictions on this.

[0018] The financial market exogenous correlation dataset may include one or more financial market exogenous correlation data, which can refer to various types of data generated outside of financial institutions and related to financial business. Financial market exogenous correlation data may include, but is not limited to, cross-market financial transaction data, market trend forecast data, regional economic data, industry development data, peer competition data, third-party credit data, macroeconomic data, and regulatory policy data, etc., and this application does not impose any restrictions on this.

[0019] Specifically, the dataset can be obtained from the internal databases of financial institutions, including but not limited to customer account opening information, transaction records for the past 12 months, loan repayment records, and information on purchased funds and insurance products. This data can then be preprocessed and its business analysis features extracted and standardized to obtain the aforementioned internal business dataset of financial institutions. Alternatively, it can be obtained through compliant data interfaces, including but not limited to real-time stock index fluctuation data, 90-day bond market trend forecasts, year-on-year GDP growth rate of a province, interbank branch distribution data, and the latest regulatory policy documents from the central bank. This data can then be preprocessed and its financial decision-making features extracted and standardized to obtain the aforementioned exogenous correlation dataset of the financial market. This application does not impose any restrictions on this.

[0020] It should be noted that, if the collaboration involves financial institutions, the aforementioned internal business data set of these financial institutions may also include all data sets directly related to their own business generated during the operation of their own platforms, such as, but not limited to, basic information of the collaborating financial institutions, service cooperation data, payment settlement data, service feedback data, supply chain collaboration data, and marketing activity cooperation data; the externally related data set of the financial market may also include various data sets generated outside the platform that are related to fintech services, such as, but not limited to, financial industry policy data, supply chain finance development data, digital marketing industry trend data, rights and interests service ecosystem data, information technology innovation industry adaptation data, and industry competition data, etc. This application does not impose any restrictions on this.

[0021] S20: Perform business expansion analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset.

[0022] The business development analysis dataset may include one or more business development analysis data points, which can refer to structured data obtained through business development analysis processing. This business development analysis data may include, but is not limited to, customer comprehensive value scores, customer-product fit, regional-industry potential index, comprehensive business development score, business development priority level, recommended product combinations, etc., and this application does not limit this.

[0023] Specifically, customer value characteristics can be screened from the internal business data of financial institutions, and a comprehensive customer value score can be dynamically weighted according to the business type of the financial institution to classify customer value levels. Furthermore, a mapping relationship between market trends and financial product demand can be established based on market data from exogenous related data, and customer demand preferences can be analyzed by combining customer value levels with product holding and service interaction data from the internal business of financial institutions. Additionally, regional GDP growth rate and industry growth rate can be extracted from exogenous data of the financial market to calculate the saturation of competition within the industry and the regional-industry potential index. Combined with customer demand intensity scores and product demand popularity scores, a comprehensive business expansion score can be calculated using a multi-dimensional weighted formula, and expansion priorities can be determined by ranking the scores.

[0024] For example, a bank recommends equity funds to its high-value customer A, taking into account the rising stock market. At the same time, considering the high growth potential of the region where customer A lives, it can classify customer A as an S-level (which can be the highest priority in business expansion) expansion target. This application does not impose any restrictions on this.

[0025] S30: Perform risk management analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a risk management analysis dataset.

[0026] The risk management analysis dataset may include one or more risk management analysis data, which can refer to structured data obtained through risk management analysis processing. Optionally, the risk management analysis data may include, but is not limited to, static risk characteristic labels, dynamic risk signals, risk trend prediction results (i.e., the dynamic risk trend prediction dataset to be discussed later), multi-type risk quantitative indicators, dynamic risk levels, risk mitigation measures, and comprehensive risk ratings (i.e., the comprehensive risk rating to be discussed later), etc., and this application does not impose any restrictions on this.

[0027] Specifically, static risk characteristics can be extracted and basic risk levels assessed based on credit ledgers and financial data from the internal business data of financial institutions, and third-party credit reports and industry benchmark data from the exogenous related data of financial markets. Dynamic risk signals can be captured by further combining recent transaction anomalies and fund flow data from the internal business of financial institutions with macroeconomic fluctuations and industry sentiment data from the exogenous related data of financial markets. Based on these dynamic risk signals and static risk levels, risk trends can be analyzed, and indicators such as liquidity risk, market risk, and operational risk can be quantitatively calculated to further adjust and obtain the dynamic risk level. Furthermore, the transmission relationship between different types of risks can be verified, and risk mitigation measures can be matched to assess the comprehensive risk rating, thus obtaining the aforementioned risk management analysis dataset.

[0028] For example, if a corporate client B has a static risk level of medium risk, but dynamic risk analysis reveals abnormal trading behavior such as a recent continuous outflow of large sums of money, and the prosperity of its industry has declined for two consecutive statistical periods, coupled with external risk signals of tightening industry policies, then its risk level can be upgraded to high risk through dynamic risk level adjustment. Optionally, by further combining data such as the client B's debt-to-asset ratio and collateral value, a risk mitigation measure of "adding 20% ​​full collateral" can be matched to form and obtain a risk management analysis dataset for client B. It is understood that this risk management analysis dataset may include the client B's static risk characteristic tags, dynamic risk signal list, risk trend prediction results, adjusted dynamic risk level and corresponding risk mitigation measures, comprehensive risk rating, etc., and this application does not impose any restrictions on this.

[0029] S40: Perform resource allocation analysis processing based on the endogenous business dataset of the financial institution and the exogenous correlation dataset of the financial market to obtain the resource allocation analysis dataset.

[0030] The resource allocation analysis dataset may include one or more resource allocation analysis data, which may refer to structured data obtained through resource allocation analysis processing. Optionally, the resource allocation analysis data may include, but is not limited to, characteristics of current resource input status, resource output benefit indicators, external environment adaptability scores, resource allocation constraints, optimized resource allocation ratios, implementation priorities, etc., and this application does not impose any restrictions on this.

[0031] Specifically, data such as fund allocation records and staffing ledgers can be extracted from the specialized resource management data of financial institutions' internal business to form a resource input status characteristic dataset. Furthermore, efficiency indicators such as return on investment and human resource input-output ratio can be calculated by combining the business revenue and profit data of the financial institutions' internal business. External environment adaptability analysis can also be conducted based on exogenous data related to the financial market, including industry return rates, policy support areas, and peer competition data. Under constraints such as total funding limits, staffing caps, and regulatory compliance requirements, an initial resource allocation plan can be generated with the goal of maximizing comprehensive benefits. The initial resource allocation plan can be dynamically adjusted by referring to the current characteristics of resource input to ensure its feasibility, thus obtaining the aforementioned resource allocation analysis dataset. For example, a financial institution can increase the proportion of funds allocated to the policy-supported area of ​​inclusive finance, taking into account its high return on investment and low peer competition saturation; this application does not impose any restrictions on this.

[0032] S50: Perform conflict verification processing based on the business development analysis dataset, the risk management analysis dataset, and the resource allocation analysis dataset to obtain the financial institution verification analysis dataset.

[0033] Conflict verification processing can be understood as a process of collaboratively verifying the results of three types of analyses: business expansion, risk management, and resource allocation. This involves identifying and reconciling contradictory information to ensure that decision-making recommendations balance growth, risk, and efficiency. The financial institution verification analysis dataset can include verification analysis data from one or more financial institutions. This data refers to the unified data formed after conflict verification processing. Essentially, this financial institution verification analysis data can be seen as eliminating conflicts among the three types of analytical results and retaining valid data that aligns with the financial institution's strategic objectives.

[0034] It should be noted that conflict verification and handling can achieve conflict reconciliation through conflict identification rules and dynamic weight guidance, ensuring that the final decision analysis results not only meet the core needs of financial institutions but also balance business growth, risk control, and resource efficiency. Specifically, conflicts can first be identified through preset rules (including but not limited to indicator threshold conflict rules, target priority conflict rules, and constraint mutual exclusion rules). For example, by cross-comparing the core indicators of the three types of analysis results, when there are conflicting target directions or mutually exclusive constraints, it can be determined as a conflict, such as conflicts between a business expansion priority of A and a high comprehensive risk rating, high regional industry potential and a low resource allocation ratio, and risk mitigation needs and resource saturation. Furthermore, a dynamic weight system can be set based on the phased strategic goals of financial institutions, where the allocation of business growth weight, risk control weight, and resource efficiency weight can be flexibly adjusted according to strategic orientation. For example, when financial institutions focus on business expansion, such as during the new product promotion period or the expansion into new regions, they can set the weighting for business growth at 40%, risk control at 30%, and resource efficiency at 30% to prioritize the advancement of high-potential businesses. When financial institutions prioritize risk control, such as during periods of market volatility or rising non-performing loan ratios, they can increase the weighting for risk control to 50%, decrease the weighting for business growth to 30%, and adjust the weighting for resource efficiency to 20% to strictly prevent losses from high-risk businesses. When financial institutions pursue stable operations, such as during the normal development phase, they can set a balanced weighting of 35% for business growth, 35% for risk control, and 30% for resource efficiency. This application does not impose any restrictions on this.

[0035] Optionally, guided by weighting, the core conflict points of the three types of analysis results—business expansion, risk management, and resource allocation—can be comprehensively verified. Common conflict types and reconciliation methods are as follows: 1. Business and Risk Conflict: If a client C has a business expansion priority of A (i.e., a high priority) but a high overall risk rating, and the financial institution is in the business expansion phase, this can be reconciled by combining optimized risk mitigation measures with restrictions on the scale of business expansion. For example, client C can be required to provide an additional 20% in full collateral, the cooperation amount can be controlled within 30% of its net assets, and the risk monitoring cycle can be shortened, such as changing from monthly monitoring to weekly monitoring. Alternatively, if the financial institution is in the risk control phase, client C's business expansion priority can be downgraded to B, and business expansion can be promoted after its overall risk level drops to a low to medium level. This application does not impose restrictions on this.

[0036] 2. Business and Resource Conflicts: For example, if a region's industry potential index is as high as 92 points (i.e., high regional industry potential), but the initial resource allocation only accounts for 5% of the total budget, there is a conflict of insufficient resource supply. In this case, resource efficiency weights can be combined. If the expected return on investment in this region is 20% higher than the industry average, then 3%-5% of the funds can be transferred from low-potential regions to supplement this region. At the same time, idle manpower can be prioritized to form a special expansion team to ensure that resources are tilted towards high-output areas. If the total resource quota is limited and cannot be increased, a resource allocation plan can be formulated according to the phased investment model. For example, 3% of the budget can be invested in the first phase to start pilot expansion, and the remaining 2% can be added after positive returns are generated. This application does not restrict this.

[0037] 3. Risk and resource conflict: If a high-risk business requires additional guarantee as a risk mitigation measure, but the institution's current guarantee resources (such as the quota of cooperative guarantee institutions and its own risk control and review personnel) are already saturated, this can be reconciled through resource restructuring and risk diversion. For example, on the one hand, new cooperative guarantee institutions can be coordinated to supplement the guarantee quota; on the other hand, part of the risk of this business can be transferred through reinsurance. At the same time, the risk control and review process can be optimized (such as introducing AI automated review tools) to improve manpower efficiency, so as to ensure that the risk mitigation measures are implemented without consuming too many core resources. This application does not impose any restrictions on this.

[0038] Through the aforementioned targeted reconciliation measures, a conflict-free and implementable financial institution verification and analysis dataset can be ultimately formed and obtained. This dataset not only integrates the effective information from the three types of analysis, but also marks the basis for conflict reconciliation, weight allocation instructions, and subsequent adjustment trigger conditions, such as initiating resource reallocation when the business return rate in a certain region is lower than expected by 10%. This application does not impose any restrictions on this.

[0039] S60: Based on the financial institution verification and analysis dataset, quantitative output of decision recommendations is performed to obtain the financial institution decision analysis results.

[0040] The quantitative output of decision recommendations can be understood as transforming validated analytical data into specific, quantifiable, and actionable decision content, avoiding vague output processes. Specifically, core indicators in the financial institution's validated analytical dataset, such as customer comprehensive value scores, risk levels, and resource allocation ratios, can be further matched with pre-defined data-decision mapping rules to generate actionable content at the corresponding level. These pre-defined data-decision mapping rules can refer to the correspondence between analytical data indicators and decision content parameters established in advance based on the financial institution's business specifications, risk control standards, and resource management rules. Optionally, different levels of mapping rules can be assigned to different positions within the pre-defined data-decision mapping rules, such as mapping rules corresponding to strategic-level outputs, execution-level outputs, and risk control-level outputs; this application does not impose any restrictions on this. It is understood that these data-decision mapping rules should be consistent with the financial institution's strategic objectives, compliance requirements, and operational processes.

[0041] The decision analysis results of a financial institution can refer to the quantifiable decision outcomes, i.e., the final decision products output by this application. Optionally, the decision analysis results of the financial institution may include, but are not limited to, customer expansion priority lists, risk control plans, and resource allocation plans; this application does not impose any restrictions on these. The decision analysis results of the financial institution can be seen as directly supporting the financial institution's marketing decisions.

[0042] Specifically, the quantified output of decision recommendations can transform conflict-checked analytical data into concrete content that can be directly implemented by different positions, avoiding vague expressions, and also establishing a traceability link between decision results and analytical data. Specifically, the output can be divided into three levels to cover the needs of the entire process from management to execution: 1. Strategic-level Outputs (for Management): These outputs can present comprehensive decision-making solutions in the form of a visual dashboard. Specific examples include, but are not limited to, a global view of business expansion, such as the priority distribution of different regions / industries / customer groups, and expected new revenue / customer volume; core risk control indicators, such as a list of high-risk businesses, predicted non-performing loan ratios, and coverage of risk mitigation measures; and resource allocation optimization plans, such as the allocation ratio of funds / human resources / channel resources across business lines and expected resource returns. Optionally, this may include explanations of strategic weighting, such as the current weighting logic and its correspondence with phased goals; and stress test results for sensitive scenarios, such as contingency plans for adjustments when market interest rates rise by 50 basis points or the non-performing loan ratio increases by 1%, to help management grasp the overall direction and formulate strategic adjustment decisions.

[0043] 2. Execution-level Outputs (for Business Personnel): These can be presented as action lists tailored to individual staff members or regions. Each business objective's decision-making content may include, but is not limited to: target customer / region / industry name; business expansion priority level; recommended product combinations, such as specific product names and recommended market share percentages; maximum cooperation amount; marketing resource allocation, such as a dedicated marketing fund of 500,000 RMB, two dedicated account managers, etc.; risk mitigation requirements, such as additional guarantee ratios, deposit amounts, etc.; expansion and progress timelines, such as completing the initial contact within T+7 days, completing product signing within T+30 days, etc.; performance indicators, such as a conversion rate target of ≥30%, and first-year revenue contribution of ≥1 million RMB, etc. It is understood that business personnel can directly implement the above action list, and this application does not impose any restrictions on this.

[0044] 3. Risk Control-Level Output (for Risk Control Personnel): This can be presented in the form of risk control boundary standards and monitoring lists, including but not limited to risk level classification thresholds, such as specific indicator ranges corresponding to dynamic risk levels of high / medium / low; prohibitions on high-risk businesses, such as prohibiting cooperation with high-risk clients without collateral and prohibiting new credit lines to industries with a non-performing loan ratio exceeding 5%; risk monitoring indicators and frequencies, such as real-time alerts for daily outflows of large sums of money exceeding 5 million yuan and weekly monitoring of fund flows for high-risk clients; standards for implementing risk mitigation measures, such as asset type requirements for additional collateral, collateral evaluation processes and valuation discount rates; and risk handling processes, such as response time limits after an alert signal and tiered approval authority. This ensures that risk control personnel clearly understand the control boundaries and handle risks in a timely manner.

[0045] For example, the quantitative decision output for a client D could be as follows: Client Name: XX Technology Co., Ltd.; Business Expansion Priority: Level A (higher business expansion priority); Recommended Product Portfolio: Working capital loans (60%) and supply chain financial services (40%); Maximum Cooperation Amount: RMB 50 million; Marketing Resource Allocation: RMB 800,000 dedicated marketing fund, 3 dedicated service personnel; Risk Mitigation Requirements: Adding its office building as collateral (70% valuation discount); Expansion Timeline: Due diligence completed in T+5 days, approval completed in T+15 days, and cooperation agreement signed in T+25 days; Performance Indicators: First-year revenue contribution ≥ RMB 3 million, non-performing loan rate controlled at 0%; Risk Control Monitoring Requirements: Weekly monitoring of fund flows, monthly verification of operating status; Early Warning Threshold: Real-time alarm for daily fund outflow exceeding RMB 8 million, etc. This application does not impose restrictions on these aspects.

[0046] By integrating data from both internal financial institutions and external financial markets, and constructing a three-in-one analytical system encompassing business expansion, risk management, and resource allocation, this system enables precise marketing through customer value segmentation and market correlation matching. It also strengthens security through a combination of static and dynamic risk analysis, improves operational efficiency through benefit-oriented resource allocation, and eliminates decision-making contradictions through conflict verification. This results in quantifiable and actionable decision outcomes, effectively addressing issues such as data fragmentation, biased analysis, imbalance between risk and growth, and resource waste in traditional financial marketing decisions. It achieves a balance between accuracy, security, and efficiency in decision-making, providing comprehensive support for financial institutions to optimize marketing effectiveness, control operational risks, and improve resource utilization efficiency in complex market environments.

[0047] In this embodiment, by acquiring the internal business dataset of financial institutions and the exogenous correlation dataset of financial markets, business expansion analysis can be performed based on these datasets to obtain a business expansion analysis dataset. Risk management analysis can also be performed based on these datasets to obtain a risk management analysis dataset. Furthermore, resource allocation analysis can be performed based on these datasets to obtain a resource allocation analysis dataset. Conflict verification can then be performed on these datasets to obtain a financial institution verification analysis dataset. Finally, decision recommendations can be quantitatively output based on this dataset, resulting in more accurate financial institution decision analysis results, which is beneficial for achieving more accurate financial institution decision analysis.

[0048] In one possible implementation, when conducting business expansion analysis, customer value stratification analysis can be performed first to extract core customer value and stratify it from the financial institution's internal business data; a mapping relationship between market conditions and product demand can be constructed, customer demand preferences can be analyzed, and a bridge connecting market trends and customer needs can be built; business expansion priority analysis can be performed to integrate multi-dimensional information such as customer value, demand matching, and industry environment, thereby forming a business expansion analysis dataset. Specifically, a method for conducting business expansion analysis based on the financial institution's internal business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset may include: A1. Based on the financial institution's endogenous business dataset, customer value features are filtered to obtain a key customer value feature dataset. A2. Based on the business type of the financial institution, each key customer value feature in the customer value key feature dataset is quantitatively weighted (the weights are dynamically matched according to the business type of the financial institution) to obtain a business type weighted customer value dataset. A3. Calculate and process the overall customer value score based on the weighted customer value dataset of the business type to obtain a tiered customer value dataset. A4. Based on the subset of cross-market financial transaction data and the subset of market trend prediction data in the aforementioned financial market exogenous correlation dataset, establish a mapping relationship between market trends and financial product demand to obtain a mapping table between market trends and financial product demand. A5. Based on the customer value stratification dataset and the financial institution's endogenous business dataset, perform customer demand preference analysis and processing to obtain a customer demand matching dataset. A6. Based on the customer demand matching dataset, the market trend and financial product demand mapping table, the customer value stratification dataset, and the financial market exogenous correlation dataset, perform business expansion priority analysis to obtain a business expansion analysis dataset.

[0049] The customer value key feature dataset may include one or more customer value key feature data. These customer value key feature data may refer to the core features that can reflect the customer's contribution to the financial institution and cooperation potential, which are selected from the financial institution's internal business data, such as transaction frequency, scale of funds deposited, credit performance, and types of products purchased. This application does not impose any restrictions on this.

[0050] Specifically, raw fields (such as average daily deposits, transaction frequency, number of overdue payments, and number of years of cooperation) stored in the actual business system of financial institutions can be extracted from subsets of customer transaction data, credit performance data, and cooperation duration data in the institution's internal business dataset. Further correlation analysis (such as Pearson correlation coefficient) can then be used to calculate the correlation coefficient between each raw field and core quantitative indicators of customer value, retaining fields with an absolute correlation coefficient ≥ 0.3 (such as average daily deposits, transaction frequency, and number of overdue payments) to filter out key characteristics of customer value. These core quantitative indicators of customer value can be general core indicators used by financial institutions to assess customer value, and different institutions can flexibly choose according to their business type; for example, retail banks can use the customer's annual profit contribution indicator, corporate banks can use the customer's annual comprehensive return indicator, and insurance institutions can use the customer's annual premium contribution indicator.

[0051] Specifically, features strongly correlated with customer value can be selected from the financial institution's internal business data based on its business positioning. For example, for retail banks, features such as transaction frequency over the past 12 months, average daily deposit balance, loan repayment performance rate, and amount held in wealth management products can be selected; for corporate banks, features such as total annual transaction volume, credit line utilization rate, cooperation duration, and contribution to upstream and downstream industrial chains can be selected to integrate and form the aforementioned key customer value feature dataset. This application does not impose any restrictions on this.

[0052] The business type of a financial institution can refer to its core operating area. Optionally, common business types for financial institutions may include, but are not limited to, retail banking, corporate banking, investment banking, and wealth management. Alternatively, the business type can also refer to the business type of the financial institutions it serves, or its corresponding core demand scenarios, such as, but not limited to, bank supply chain finance services, consumer finance marketing and customer acquisition, securities firm intelligent decision-making, and insurance compliance and risk control. This application does not impose any restrictions in this regard.

[0053] Quantitative weighting can be understood as assigning corresponding weights to key customer value characteristics based on the strategic priorities of different business types. In other words, it's the process of strengthening the impact of core characteristics on customer value assessment through mathematical calculations. A business type-weighted customer value dataset can include one or more business type-weighted customer value data sets. This data refers to the data containing the correlation between feature values ​​and weights after assigning corresponding weights to each key customer value characteristic.

[0054] Specifically, weight allocation can be dynamically matched according to business type to ensure alignment with strategic objectives. For example, retail banks can focus on individual fund accumulation and transaction activity, allocating 35% weight to average daily deposit balance, 30% to transaction frequency, 20% to credit fulfillment rate, and 15% to product holding types. Corporate banks can focus on transaction volume and cooperation stability, allocating 40% weight to annual transaction volume, 25% to cooperation duration, 20% to credit line utilization rate, and 15% to contribution to the industrial chain. This associates each customer's characteristic value with its corresponding weight, forming and obtaining a weighted customer value dataset for the aforementioned business types.

[0055] It should be noted that, to ensure that key customer value features of different dimensions (such as average daily deposit balance in "yuan" and transaction frequency in "number of transactions") can be weighted for calculation, the original values ​​of each key customer value feature can be standardized. For example, the extreme value method can be used to map the original feature values ​​of key customer value features to a range of 0-100 points. The standardization formula can be as follows: Standardized feature value = (Original feature value - Minimum feature value) / (Maximum feature value - Minimum feature value) × 100, where the maximum and minimum feature values ​​can be taken from the historical statistical range of the feature in the financial institution's internal business dataset, such as a minimum of 0 yuan and a maximum of 1 million yuan for average daily deposit balance. It can be understood that the standardized feature values ​​can then be used for subsequent calculation of the overall customer value score.

[0056] A customer's overall value score can be a numerical value that quantifies the overall value of a customer, calculated using a weighted summation formula. Optionally, the customer's overall value score can range from 0 to 100, with higher scores indicating higher customer value and lower scores indicating lower customer value. The customer value stratification dataset can include one or more customer value stratification data points. This stratification data can refer to the data formed after classifying customers into tiers based on their overall value scores. Optionally, this customer value stratification data can include, but is not limited to, customer identifiers, overall value scores, and value tiers.

[0057] Optionally, a weighted summation formula can be used to calculate the customer's overall value score. See the formula below for details: CV=Σ(Fi×Wi), i=1,2,...,n; Where CV represents the overall customer value score; Σ represents the summation operation; Fi represents the value of the i-th key customer value feature (i.e., the standardized value of the i-th key customer value feature); Wi represents the feature weight corresponding to the i-th key customer value feature (i.e., the weight dynamically assigned to the i-th key customer value feature based on the financial institution's business type); i represents the index of the key customer value feature; and n represents the total number of key customer value features. It should be noted that the sum of the feature weights can be 1.

[0058] For example, a retail bank customer E has a daily average deposit balance characteristic score of 80 (after standardization) with a weight of 35%, a transaction frequency characteristic score of 90 with a weight of 30%, a credit fulfillment rate characteristic score of 100 with a weight of 20%, and a product holding type characteristic score of 70 with a weight of 15%. Therefore, customer E's overall customer value score = 80 × 35% + 90 × 30% + 100 × 20% + 70 × 15% = 84.5 points. At this point, value levels can be further categorized based on the overall customer value score. For example, a score of 90 or above can be classified as ultra-high-value customers, 80-89 as high-value customers, 60-79 as medium-value customers, and below 60 as basic-value customers, thus forming the aforementioned customer value stratification dataset.

[0059] The subset of cross-market financial transaction data may include one or more cross-market financial transaction data sets. This subset can be viewed as a subdivided subset of exogenous correlation datasets in the financial market, and may include financial market transaction data such as stock indices, bond yields, fund net asset values, foreign exchange rates, and commodity prices. Optionally, this cross-market financial transaction data may also include, but is not limited to, financial industry policy data, supply chain finance development data, digital marketing industry trend data, equity service ecosystem data, information technology innovation industry adaptation data, and industry competition data; this application does not impose any restrictions on this.

[0060] The market trend prediction data subset may include one or more market trend prediction data. This subset can be viewed as a subdivided subset of the exogenous correlation dataset of the financial market, and may contain predictive data on the trend of the financial market over a future period (such as 30 days or 90 days); or it may contain predictive data on the trend of various sub-sectors of fintech over a future period (such as 6 months or 12 months), such as the growth rate forecast of digital transformation of supply chain finance, the growth forecast of demand for AI marketing tools, and the forecast of the implementation pace of information technology innovation adaptation policies.

[0061] A market trend and financial product demand mapping table is a table used to record the correlation between different market trend types and corresponding high-demand financial products, as well as the popularity of product demand. Specifically, it can extract cross-market financial transaction data and market trend forecast data from exogenous correlation data in the financial market, and further analyze the correlation between these and the demand for financial products. For example, when the stock index shows a sustained upward trend, the demand for equity funds is high; when the interest rate trend is clear downward, the demand for fixed-income wealth management products is high; when foreign exchange rate volatility intensifies, the demand for foreign exchange wealth management and currency hedging products is high. By further mapping market trend types (such as rising stock markets, falling interest rates, and fluctuating exchange rates), corresponding high-demand financial products (such as equity funds, fixed-income wealth management, and foreign exchange hedging products), and product demand popularity scores (0-100 points), the above-mentioned market trend and financial product demand mapping table can be established.

[0062] The customer demand matching dataset can include one or more customer demand matching datasets. These datasets can refer to data containing information such as the type of product and the intensity of potential customer needs, which is generated after analyzing customer value hierarchy data and internal business data of financial institutions.

[0063] Specifically, customer demand preferences can be identified by combining customer value stratification datasets and product holding and service interaction data from financial institutions' internal business datasets, such as the types of products purchased, customer service consultation records, and online browsing history. For example, a high-value customer who already holds money market funds and short-term wealth management products and has repeatedly inquired about stock-related services can be identified as having a high potential demand for equity funds and stock account opening services. A medium-value customer who only holds fixed deposits and has no other service interaction records can be identified as having a moderate potential demand for stable wealth management products. This can create a customer demand matching dataset that includes customer identification, potential product type demand, and demand intensity. This application does not impose any restrictions on this.

[0064] Business expansion prioritization analysis can be understood as a process of integrating multi-dimensional information such as customer value, demand matching degree, and market environment to further determine the order of business expansion through quantitative scoring. The business expansion analysis dataset can include one or more business expansion analysis data points. This dataset can be viewed as a structured dataset containing core information such as customer expansion priority levels, recommended product combinations, and regional industry potential rankings.

[0065] Specifically, multi-dimensional quantitative scoring can be achieved by integrating subsets of regional economic and industry development data from customer demand matching datasets, market trend and financial product demand mapping tables, customer value stratification datasets, and financial market exogenous correlation datasets. For example, regional GDP growth rate and industry growth rate can be extracted to calculate a regional industry potential index. Combined with customer demand intensity score and product demand popularity score, a product suitability score can be calculated to further determine the comprehensive business expansion score. This allows for prioritization based on the comprehensive business expansion score in descending order, and also generates a regional industry potential ranking. Ultimately, this results in a business expansion analysis dataset containing information such as customer identifier, region / industry, comprehensive score, priority level, and recommended product combinations.

[0066] In this embodiment, a progressive process of customer value segmentation, market trend mapping, demand preference mining, and priority quantification achieves deep integration and efficient conversion of multi-dimensional data. On the one hand, customer value segmentation based on the financial institution's internal business data ensures that the expansion targets focus on high-value groups, avoiding resource waste. On the other hand, by constructing a mapping relationship between market conditions and product demand through exogenous correlation data from the financial market, product recommendations align with market trends, increasing conversion rates. Furthermore, multi-dimensional weighted scoring determines expansion priorities, taking into account customer value, product suitability, and regional industry potential, making business expansion decisions more accurate and forward-looking. The above process solves problems such as vague customer positioning, product recommendations being out of touch with the market, and lack of quantitative basis for priority division in traditional business expansion. It provides comprehensive support for financial institutions to accurately screen expansion targets, optimize product recommendation strategies, and rationally allocate marketing resources, effectively improving business expansion efficiency and returns.

[0067] In one possible implementation, when performing business expansion priority analysis, a multi-dimensional integrated priority analysis system can be formed based on a progressive process of external potential assessment, internal fit analysis, and comprehensive scoring and ranking. For example, external market opportunities can be explored using data on regional economy, industry development, and industry competition to calculate regional-industry potential; internal supply and demand matching can be focused on, combining customer demand intensity and product market popularity to analyze customer-product fit; and external potential, internal fit, and customer value can be integrated to quantify and rank the comprehensive score, thereby forming a business expansion analysis dataset. Specifically, a method for performing business expansion priority analysis based on the customer demand matching dataset, the market trend and financial product demand mapping table, the customer value stratification dataset, and the financial market exogenous correlation dataset to obtain a business expansion analysis dataset may include: B1. Extract the year-on-year GDP growth rate of the region based on the regional economic data subset in the exogenous correlation dataset of the financial market to obtain the GDP growth rate of the region. B2. Extract the year-on-year revenue growth rate of the respective industry from the industry development data subset in the aforementioned financial market exogenous correlation dataset to obtain the industry growth rate. B3. Calculate the industry competition saturation based on the industry development data subset in the aforementioned financial market exogenous correlation dataset; B4. Based on the GDP growth rate of the region, the growth rate of the industry, and the saturation of competition in the same industry, perform regional-industry potential analysis to obtain a regional-industry potential analysis dataset. B5. Calculate the customer demand intensity score based on the customer demand matching dataset to obtain the customer demand intensity score; B6. Calculate the product demand popularity score based on the market trend and financial product demand mapping table to obtain the product demand popularity score; B7. Based on the customer demand intensity score and the product demand popularity score, perform customer-product fit analysis to obtain a customer-product fit dataset. B8. Calculate the comprehensive business expansion score based on the customer value stratification dataset, the customer-product matching dataset, and the region-industry potential analysis dataset. B9. Based on the comprehensive business development score, prioritize the business development process to obtain the business development analysis dataset.

[0068] The regional economic data subset can be viewed as a subdivided subset of exogenous relational data that focuses on the operation of the regional economy. This regional economic data subset may include one or more regional economic data, which may include, but are not limited to, regional GDP, per capita disposable income, fixed asset investment, etc.

[0069] The year-on-year GDP growth rate of a region can refer to the GDP growth rate of the region where the customer or business is located during the target statistical period. It can be understood that the year-on-year GDP growth rate of that region can reflect the vitality of regional economic development.

[0070] Specifically, relevant data about the customer's or business's region can be filtered from the regional economic data subset. For example, the total GDP for two consecutive statistical periods (such as the current year and the previous year) can be extracted, and the GDP growth rate of the region can be calculated using the formula: Regional GDP growth rate = (Total GDP of the current year - Total GDP of the previous year) ÷ Total GDP of the previous year × 100%. For example, if the total GDP of the region where customer K is located is 1.26 trillion yuan this year and 1.2 trillion yuan last year, then the GDP growth rate of the region where customer K is located can be calculated as 5%.

[0071] Industry development data subsets can be viewed as a concentrated sub-set of exogenous correlation data in the financial market that focuses on the development status of specific industries. This subset may include data on one or more industries, including but not limited to total revenue, profit margins, market size, and policy support for each industry. The industry growth rate refers to the revenue growth rate of the industry to which a client or business belongs within a specific statistical period. Understandably, this industry growth rate reflects industry development trends and market demand potential.

[0072] Specifically, the total revenue of a customer's or business's industry for two consecutive statistical periods (e.g., this year and the previous year) can be extracted from the industry development data subset. The year-on-year revenue growth rate of the industry can then be calculated using the formula: Year-on-year revenue growth rate = (Total revenue this year - Total revenue last year) ÷ Total revenue last year × 100%. For example, if a customer L's new energy industry has a total revenue of 800 billion yuan this year and 689.7 billion yuan last year, then the year-on-year revenue growth rate of customer L's industry can be calculated as 16%.

[0073] The saturation of competition within the same industry can refer to the degree of matching between the service supply and market demand of financial institutions in the region or industry where the customer or business is located. It is understood that this saturation level reflects the intensity of market competition, and its value can range from 0 to 1. Optionally, the closer the saturation level is to 1, the more intense the competition; the closer the saturation level is to 0, the more moderate the competition. This application does not impose any restrictions on this.

[0074] Specifically, based on industry development data subsets such as competitor layout and market share, the industry competition saturation can be calculated using the formula: Industry Competition Saturation = Region - Effective Supply of Competitors in the Industry ÷ Region - Total Market Demand in the Industry. Here, effective supply can include quantitative indicators such as the number of competitors, service network coverage, and product types; total market demand can include quantitative indicators such as corporate financing demand and personal financial service demand within the region / industry. For example, if the effective supply of competitors in a certain region / industry is 75 and the total market demand is 100, then the industry competition saturation can be calculated as 0.75.

[0075] Regional-industry potential analysis can be understood as a process that comprehensively considers regional economic vitality, industry growth trends, and market competition intensity to quantitatively assess the overall business expansion value of a region or industry. The regional-industry potential analysis dataset can include one or more regional-industry potential analysis data points. This dataset can refer to structured data containing regional-industry potential assessment results. This data may include, but is not limited to, regional-industry names, regional-industry potential indices, regional-industry potential levels, and regional-industry potential driving factors. The regional-industry potential index can range from 0 to 100 points; a higher score indicates greater expansion potential, while a lower score indicates less expansion potential.

[0076] Specifically, a weighted summation method can be used to calculate the aforementioned regional-industry potential index. Optionally, the formula can be used: Regional-Industry Potential Index = Standardized value of regional GDP growth rate × 0.3 + Standardized value of industry growth rate × 0.3 + (1 - Industry competition saturation) × 100 × 0.4. It should be noted that the weights in the above formula can be set according to the degree of influence of each indicator on the regional-industry potential; this application does not impose any restrictions on this. Optionally, the above weights can be dynamically optimized according to the strategic orientation of financial institutions. For example, if an institution focuses on high-growth industries, the weight of the industry growth rate can be increased to 0.4, while both the regional GDP growth rate and industry competition saturation can be set to 0.3; this application does not impose any restrictions on this.

[0077] The standardization process can employ extreme value methods, such as converting GDP growth rate and industry growth rate into scores of 0-100. For example, a region-industry GDP growth rate of 5% can be standardized to 50 points using extreme value methods; an industry growth rate of 16% yields a standardized score of 80 points, and an industry competition saturation of 0.75. Therefore, the region-industry potential index can be calculated using the formula: 50 × 0.3 + 80 × 0.3 + (1 - 0.75) × 100 × 0.4 = 15 + 24 + 10 = 49 points. Furthermore, potential levels can be categorized based on scores (e.g., 80 points and above is high potential, 60-79 points is medium-high potential, 40-59 points is medium potential, and below 40 points is low potential), thus forming the aforementioned region-industry potential analysis dataset.

[0078] The customer demand matching dataset can include one or more customer demand matching data points. This data may contain information such as the type of product a customer might need, the clarity of that need, and historical interaction records. The customer demand intensity score quantifies the urgency of a customer's need for a target financial product or service, and its value can range from 0 to 100. Understandably, a higher customer demand intensity score indicates a stronger need, while a lower score indicates a weaker need.

[0079] Specifically, based on a customer demand matching dataset, scores can be calculated from dimensions such as demand clarity, demand frequency, and historical conversion intention, followed by weighted summation to obtain the aforementioned customer demand intensity score. Optionally, the customer demand matching dataset can be a dataset formed by integrating subsets of customer consultation interaction data, product browsing data, and historical transaction data from a financial institution's internal business dataset. Based on this customer demand matching dataset, scores can be calculated from different dimensions according to customer demand behavior weighted scoring rules (each dimension's scoring rules correspond to specific fields in the dataset). For example, for the demand clarity dimension, the scoring rules can be based on the "consultation content keywords" field in the customer consultation interaction data subset: clearly expressing a product purchase intention, such as consulting "how to buy equity funds," would receive a score of 30; consulting product information without mentioning purchase, such as consulting "how much do equity funds yield?" For the "What" category, a score of 20 points is awarded; for browsing without consultation, a score of 10 points is awarded. Regarding the frequency of requests, the scoring rules are based on the "Number of Consultations" field in the customer consultation interaction data subset: ≥3 consultations in the past 3 months, a score of 25 points; 1-2 consultations in the past 3 months, a score of 20 points; 0 consultations in the past 3 months, a score of 10 points. Regarding historical conversion intention, the scoring rules are based on the "Purchase Records of Similar Products" field in the historical transaction data subset: Purchase of similar products (e.g., wealth management products), a score of 20 points; purchase of other types of products, a score of 15 points; no purchase record, a score of 10 points.

[0080] For example, if customer M explicitly expresses an intention to purchase equity funds (e.g., setting the clarity of need to 30 points), has inquired about related products 3 times in the past 3 months (e.g., setting the frequency of need to 25 points), and has previously purchased similar low-risk financial products (e.g., setting the historical conversion intention to 20 points), and further calculates the customer need intensity score using a weighted ratio of 4:3:3, then customer M's customer need intensity score would be 30 × 0.4 + 25 × 0.3 + 20 × 0.3 = 12 + 7.5 + 6 = 25.5 points. Optionally, the weights in this formula can be adjusted according to actual needs, and this application does not impose any restrictions on this.

[0081] Product demand popularity score quantifies the strength of market demand for a target financial product under current market trends, with a value ranging from 0 to 100. In other words, a higher product demand popularity score indicates stronger market demand for the product, while a lower score indicates weaker market demand.

[0082] Specifically, the product demand heat score can be obtained by assigning a value to the driving strength of the product based on the market trend and financial product demand mapping table, combined with the current market trend. For example, if the stock market is currently rising continuously (market trend type), the demand heat benchmark score for equity funds in the market trend and financial product demand mapping table is 80 points. After adding a dynamic adjustment based on the recent 15% month-on-month increase in fund market trading activity, the product demand heat score can be further calculated as 80 × (1 + 15%) = 92 points.

[0083] Customer-product fit analysis can be understood as a process of assessing the degree of match between individual customer needs and product market popularity. The customer-product fit dataset may include one or more customer-product fit data points, which can refer to structured data containing the customer's and target product's fit assessment results. Specifically, this customer-product fit data may include, but is not limited to, customer identifiers, target product names, fit scores, and reasons for fit. The fit score can range from 0 to 100 points, and this application does not impose any limitations on this.

[0084] Specifically, the weighted summation method can be used to calculate the above-mentioned fit score. The formula is: Customer-Product Fit Score = Customer Demand Intensity Score × 0.5 + Product Demand Popularity Score × 0.5. For example, if customer N has a demand intensity score of 25.5 and a stock fund product demand popularity score of 100, then customer N's ​​customer-product fit score can be calculated as 25.5 × 0.5 + 100 × 0.5 = 12.75 + 50 = 62.75, thus further forming and obtaining the above-mentioned customer-product fit dataset.

[0085] The business expansion priority ranking process can be understood as using a descending score and threshold-based grading rule. First, businesses are ranked in descending order of their overall business expansion score. Then, different priority levels are assigned based on preset score thresholds. Specific grading rules could be: 80 points and above are classified as Level 1 Priority (Core Expansion Clients); 60-79 points as Level 2 Priority (Key Expansion Clients); 40-59 points as Level 3 Priority (Regular Expansion Clients); and below 40 points as Level 4 Priority (Clients to be Temporarily Deferred). These score thresholds can be adjusted by financial institutions according to their business strategies; this application does not impose any restrictions on this.

[0086] The business expansion analysis dataset may include one or more business expansion analysis data sets, which can refer to structured data containing core business expansion decision-making information. Optionally, this business expansion analysis data may include, but is not limited to, customer identifiers, region / industry, overall business expansion score, priority level, recommended product mix, and expansion strategy suggestions.

[0087] Specifically, business development scores can be sorted in descending order, such as into four priority levels: S-level (overall score ≥ 90), A-level (80-89), B-level (60-79), and C-level (< 60). For example, a customer Q with an overall business development score of 68.725 can be classified as a B-level priority customer. Further combining this with information such as their region / industry and suitable products, a business development analysis dataset can be generated, containing information such as: Customer ID: XXX, Region / Industry: XX Province / New Energy Industry, Overall Business Development Score: 68.73, Priority: B-level, Recommended Products: Equity Funds + New Energy Industry-Specific Investments, Development Strategy: Focus on Pushing Industry Policy Benefits + Product Return Cases, etc.

[0088] In this embodiment, the precise and scientific nature of expansion decisions is achieved through the multi-dimensional quantification of external market potential, internal customer needs, product market popularity, and long-term customer value. Regional-industry potential analysis based on exogenous data enables business expansion to accurately capture market opportunities and avoid overly competitive areas. Matching analysis between customer needs and product popularity ensures that recommended products align with individual customer needs and market trends. Comprehensive scoring and prioritization allow financial institutions to clearly identify high-value expansion targets and rationally allocate marketing resources. This entire process solves the problems of experience-based decision-making, fragmented resource allocation, and a disconnect between market and demand in traditional business expansion, providing comprehensive data support and decision-making basis for financial institutions to improve business expansion conversion rates, reduce marketing costs, and achieve sustainable growth.

[0089] In one possible implementation, risk management analysis can follow a progressive process: static risk assessment, dynamic risk capture, trend prediction, quantitative calculation, level adjustment, and linkage verification, forming a complete risk control closed loop. For example, a basic risk data system can be constructed by extracting static risk characteristics and dynamic risk signals; dynamic risk trends can be judged by combining static risk levels, and multiple types of dynamic risks can be quantified to provide a basis for risk level adjustment; dynamic risk level optimization and cross-risk linkage verification can be used to form a risk management analysis dataset containing risk ratings and mitigation measures. Specifically, a method for obtaining a risk management analysis dataset by performing risk management analysis based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset may include: C1. Static risk feature extraction processing is performed on the endogenous business dataset of the financial institution and the exogenous correlation dataset of the financial market to obtain a static risk feature dataset; C2. Based on the endogenous business dataset of the financial institution and the exogenous correlation dataset of the financial market, perform dynamic risk signal extraction processing to obtain a dynamic risk signal dataset; C3. Based on the dynamic risk signal dataset and the static risk level data subset in the static risk feature dataset, perform dynamic risk trend judgment to obtain a dynamic risk trend prediction dataset. C4. Perform multi-type dynamic risk quantification calculations based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a multi-type dynamic risk quantification dataset. C5. Perform dynamic risk level adjustment processing based on the multi-type dynamic risk quantification dataset and the dynamic risk trend prediction dataset to obtain the target dynamic risk level dataset. C6. Based on the static risk feature dataset, the target dynamic risk level dataset, the financial institution's endogenous business dataset, and the financial market's exogenous correlation dataset, perform cross-risk linkage verification processing to obtain a risk management analysis dataset.

[0090] Static risk feature extraction can be understood as the process of mining relatively stable and long-term risk-related features from internal and external data to form standardized risk identification dimensions. A static risk feature dataset may include one or more static risk feature data points. This static risk feature dataset can be viewed as a structured dataset containing core information about static risks. Optionally, the core content of the static risk feature data may include, but is not limited to, static risk level data and static risk feature labels; wherein, the static risk level data can be a basic risk level assessed based on historically stable features, and the static risk feature labels can be classification identifiers for risk types and risk sources.

[0091] Specifically, stable characteristics such as customer registered capital, debt-to-asset ratio, historical credit performance records, and compliance review pass rates can be extracted from the internal business data of financial institutions; and basic information such as corporate credit ratings, number of lawsuits, industry average non-performing loan ratios, and regulatory classification ratings can be extracted from exogenous related data of the financial market. Optionally, static risk feature labels can be obtained by establishing a mapping relationship between extracted features and label generation rules. This mapping relationship can be represented by a mapping relationship table (the mapping relationship table presets label rules corresponding to each extracted feature, and the rules can be adjusted according to the risk preferences of financial institutions). Optionally, this mapping relationship table between extracted features and label generation rules can be set based on empirical values ​​or historical data. For example, for corporate client E, characteristics such as no overdue repayment records in the past three years, a debt-to-asset ratio of 65%, and a third-party credit rating of AA can be extracted. Thus, corporate client E can be assessed as having a medium static risk level and can be labeled with static risk characteristic tags such as "good credit performance" and "high financial leverage". For individual client F, characteristics such as no legal litigation records, no overdue credit card repayments, and stable income can be extracted. Thus, individual client F can be assessed as having a low static risk level and can be labeled with static risk characteristic tags such as "good personal credit" and "stable repayment ability". These characteristics can be further integrated to form the above-mentioned static risk characteristic dataset.

[0092] Dynamic risk signal extraction and processing can be understood as the process of capturing real-time changes in internal and external data that may trigger risk fluctuations, in order to identify potential risk trigger points. A dynamic risk signal dataset can include one or more dynamic risk signal data points. This dataset can be viewed as a collection of data recording information related to dynamic risks, which may include, but is not limited to, risk signal type, trigger time, related business, and scope of impact. It should be noted that this dynamic risk signal can be considered as various dynamic events or indicator changes that may lead to an increase or decrease in risk level in the short term.

[0093] Specifically, dynamic information such as recent transaction anomalies, changes in fund flows, abnormal credit line usage, and repayment delays can be monitored from the internal business data of financial institutions; real-time dynamics such as industry policy adjustments, macroeconomic fluctuations, market interest rate changes, and negative public opinion exposure can be tracked from exogenous related data of the financial market. Optionally, dynamic risk signals can be extracted by establishing a mapping relationship between dynamic monitoring indicators and signal triggering rules. This mapping relationship can be represented by a mapping relationship table (the mapping relationship table presets the triggering rules corresponding to each dimension of dynamic monitoring indicators, and the rules can be adjusted according to the risk level of the financial institution). Optionally, this mapping relationship table between dynamic monitoring indicators and signal triggering rules can be set based on empirical values ​​or historical data. For example, if a corporate client G is found to have a large amount of funds flowing out continuously without reporting the purpose, has experienced two consecutive delayed repayments, or has its industry introduced production restriction policies, these information can all be classified as dynamic risk signals. Similarly, if an individual client H frequently applies for credit products from multiple financial institutions in a short period of time and has large, non-fixed transfers in and out of bank statements, these can also be classified as dynamic risk signals. By organizing these signals according to type (fund movement, policy impact, public opinion, etc.), the aforementioned dynamic risk signal dataset can be formed.

[0094] A subset of static risk level data may include one or more static risk level data sets. This subset can be a core subset of the static risk characteristic dataset, containing basic static risk level information for various businesses or customers, and can be considered a benchmark for trend judgment.

[0095] Dynamic risk trend assessment can be understood as the process of combining dynamic risk signals with static risk levels to analyze the direction of risk development, the speed of change, and the potential impact. A dynamic risk trend prediction dataset may include one or more dynamic risk trend prediction data points. This dataset can be viewed as a dataset containing the results of risk trend analysis. Optionally, the dynamic risk trend prediction dataset may include, but is not limited to, risk trend types such as rising, falling, or stable; as well as predictions of trend duration and impact assessments.

[0096] Specifically, a combination of signal quantification and correlation analysis is used for trend analysis, and the following preset judgment rules can be established (adjustable according to the risk appetite of financial institutions): For example, in the trend type judgment rules, if the static risk level is medium / high + dynamic risk signals ≥ 2 types (such as abnormal fund flows + negative policy impacts) and the signals have a causal relationship, then it can be judged as an upward trend; if the static risk level is low + dynamic risk signals ≤ 1 type and there is no correlation, then it can be judged as a stable trend; if the static risk level is medium / high + dynamic risk signals fade (such as debt repayment + clarification of public opinion), then it can be judged as a downward trend; The rules for predicting the duration of a signal's duration are as follows: If multiple signals are strongly correlated (e.g., negative policy impact → tight cash flow → delayed repayment), the duration can be predicted to be 3-6 months; if a single signal is uncorrelated, the duration can be predicted to be 1-2 months; if the signal fades and no new signals appear, the duration can be predicted to be ≤1 month. The rules for assessing the degree of impact are as follows: If a signal involves core indicators (funds, performance) and the scale of related business is large, the impact can be judged as high; if a signal involves non-core indicators and the scale of related business is small, the impact can be judged as low; and if it falls between the two, the impact can be judged as medium.

[0097] The dynamic risk trend can be judged by combining the strength, frequency, and correlation of dynamic risk signals with the static risk level as a benchmark. For example, if a client I has a medium static risk level and has recently experienced three abnormal fund movement signals and two negative industry policy signals, and there is a causal relationship between the signals (such as negative policies leading to tight cash flow), then the dynamic risk trend of client I can be judged to be rapidly rising, with a predicted duration of 3-6 months and a high degree of impact. If a client J has a low static risk level and has recently experienced only one small repayment delay signal, which has been promptly repaid, and there are no other related risk signals, then the dynamic risk trend of client J can be judged to be stable and a low degree of impact. These judgment results can be compiled to form the dynamic risk trend prediction dataset mentioned above.

[0098] Multi-type dynamic risks can refer to common dynamic risk categories in financial business, including but not limited to market risk, liquidity risk, credit risk, operational risk, and compliance risk. Among them, market risk can refer to the risk of loss due to fluctuations in financial market prices; liquidity risk can refer to the risk of difficulty in cash flow and inability to meet payment needs; credit risk can refer to the risk of loss due to counterparty default; operational risk can refer to the risk of loss due to internal process defects or human error; and compliance risk can refer to the risk of penalties due to violations of regulatory provisions.

[0099] Multi-type dynamic risk quantification can be understood as converting different types of dynamic risks into quantifiable numerical indicators through standardized formulas to achieve accurate risk measurement. A multi-type dynamic risk quantification dataset can include one or more multi-type dynamic risk quantification data points. This dataset can be viewed as a dataset containing various dynamic risk quantification indicators. It should be noted that each type of multi-type dynamic risk can correspond to a specific quantified value and evaluation standard.

[0100] Specifically, different quantitative models and calculation methods can be used for different types of dynamic risks. For example, when calculating market risk, the Value at Risk (VaR) model can be used to calculate the market risk value, with the formula: VaR = Portfolio Value × Market Volatility × Confidence Level Coefficient. The confidence level coefficient can be the quantile of the standard normal distribution at the corresponding confidence level; for example, 1.65 corresponds to a 95% confidence level, and 2.33 corresponds to a 99% confidence level. Considering the risk appetite of financial institutions, a 95% confidence level can be uniformly set. When calculating credit risk, indicators such as the probability of recent customer defaults and the loss rate can be used, and a credit risk pricing model can be used to calculate the expected loss rate. When calculating liquidity risk, data such as the gap between cash inflows and outflows and the liquidity of assets can be used to calculate the liquidity coverage ratio. For example, using the above methods, the market risk value of a certain business can be calculated as 500,000 yuan, the expected credit risk loss rate as 1.2%, and the liquidity coverage ratio as 120%. These quantitative results can then be organized according to risk type to form a multi-type dynamic risk quantitative dataset.

[0101] Dynamic risk level adjustment can be understood as the process of optimizing and adjusting dynamic risk levels based on the quantitative results of multiple types of dynamic risks and the prediction of dynamic risk trends. The target dynamic risk level dataset may include one or more target dynamic risk level data, which can refer to the final risk level data formed after adjustment. Optionally, the target dynamic risk level data may include, but is not limited to, information such as the target dynamic risk level of the business or customer, the basis for adjustment, and the adjustment range.

[0102] Specifically, dynamic risk level adjustment rules can be set to combine quantitative indicators (i.e., multi-type dynamic risk quantitative datasets) with trend judgment results (i.e., dynamic risk trend prediction datasets) to adjust the dynamic risk level upwards or downwards. Optionally, the dynamic risk level can usually be divided into five levels: low, low-medium, medium, medium-high, and high; this application does not impose any restrictions on this.

[0103] Cross-risk linkage verification processing can be understood as verifying the transmission relationship and mutual influence between different types of risks to ensure that risk assessment is comprehensive and without omissions, avoiding the one-sidedness of a single risk assessment. The risk management analysis dataset can include one or more risk management analysis data sets, which may contain, but are not limited to, information such as static risk characteristics, dynamic risk signals, risk trends, quantitative indicators, target dynamic risk levels, risk mitigation measures, and comprehensive risk ratings.

[0104] Risk mitigation measures can refer to countermeasures taken to reduce risk losses. A comprehensive risk rating can refer to the overall final risk assessment result obtained by combining the static risk level with the target dynamic risk level and cross-risk linkage verification results (which may include the transmission, superposition, or offsetting effects of different types of risks). This comprehensive risk rating can be the core basis for risk prevention and control decisions, and its rating can be divided into five levels: excellent, good, moderate, concerning, and high-risk. This application does not impose any restrictions on this.

[0105] Specifically, a weighted summation method can be used to calculate the comprehensive risk rating, with the following rules (total score 0-100 points, corresponding to five levels): Base score (40%), derived from the static risk level: low risk is considered 80 points, low-to-medium risk 70 points, medium risk 60 points, medium-to-high risk 50 points, and high risk 40 points; Dynamic adjustment score (30%), determined by the difference between the target dynamic risk level and the static risk level: an upward adjustment of one level is considered a deduction of 10 points, no change in level is considered no deduction, and a downward adjustment of one level is considered an addition of 10 points (e.g., ...). A static medium risk score of 60 points and a dynamic high risk score will result in a deduction of 10 points (3 points for a 30% weighting). The linkage verification score (30%) is adjusted based on cross-risk linkage results. If there is risk overlap (e.g., market risk → credit risk), 5-10 points will be deducted; if there is risk offsetting (e.g., low liquidity risk offsetting some credit risk), 3-8 points will be added; if there is no obvious correlation, no points will be deducted. For rating mapping, a total score ≥90 points is considered excellent, 80-89 points is good, 60-79 points is moderate, 40-59 points is watchful, and <40 points is high-risk.

[0106] Specifically, the relationships between various risks can be analyzed to verify whether there are risk superposition or offsetting effects. For example, an increase in market risk for a certain business may lead to an increase in its credit risk, and an increase in operational risk may lead to an increase in compliance risk. In this case, it is necessary to comprehensively assess the overall risk level after the superposition of various risks. If the liquidity risk of a certain business is low, it can offset some of the impact of credit risk to a certain extent. In this case, it is necessary to comprehensively consider various risks to adjust and obtain the final risk assessment result.

[0107] Furthermore, corresponding risk mitigation measures can be matched based on the target dynamic risk level and the results of cross-risk linkage analysis. For example, for high-risk businesses, measures such as additional guarantees, increasing the risk reserve provision ratio, and shortening the monitoring cycle can be matched; for medium-risk businesses, measures such as strengthening post-loan management and limiting business scale can be matched. In other words, a comprehensive risk rating can be given by combining static risk characteristics, dynamic risk performance, and the effectiveness of mitigation measures, thereby forming a risk management analysis dataset containing complete risk information.

[0108] In this embodiment, a dual risk assessment model combining static and dynamic approaches is employed to achieve comprehensive risk capture and precise measurement. On one hand, static risk feature extraction lays the foundation for risk assessment, ensuring the effective identification of long-term stable risks. On the other hand, dynamic risk signal capture and quantitative calculation enable real-time tracking of risk changes and timely detection of potential risks. Through risk trend prediction, level adjustment, and cross-risk linkage verification, the problems of lagging static assessment and isolated judgment of single risks in traditional risk management can be effectively solved, enabling full-cycle and multi-dimensional risk control. The resulting risk management analysis dataset can provide clear risk ratings and implementable mitigation measures, providing comprehensive support for financial institutions to formulate risk prevention and control strategies and optimize business decisions, effectively reducing risk losses and ensuring sound business operations.

[0109] In one possible implementation, during resource allocation analysis, a progressive process can be followed: current situation assessment, benefit evaluation, environmental adaptation, constraint definition, preliminary allocation, and dynamic correction. This process aims to construct a resource optimization system that integrates internal and external data and balances benefits and constraints. For example, by analyzing the current state of resource input and quantifying output benefits, the foundation for internal resource operation can be clarified; by analyzing the adaptability to the external environment and defining resource allocation boundaries, an internal and external constraint framework can be established; and by initial resource allocation and dynamic optimization, a scientifically sound resource allocation analysis dataset can be formed. Specifically, a method for obtaining a resource allocation analysis dataset by performing resource allocation analysis based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset may include: D1. Extract resource input features from the subset of special resource management data in the endogenous business dataset of the financial institution to obtain a resource input status feature dataset. D2. Based on the business revenue-related data subset and the special resource management data subset in the financial institution's endogenous business dataset, calculate the resource output efficiency to obtain a quantitative dataset of resource output efficiency. D3. Perform external environment adaptability analysis on the subset of external market environment adaptability data in the aforementioned financial market exogenous correlation dataset to obtain the external environment adaptability assessment dataset. D4. Based on the resource input status characteristic dataset, the resource-related data subset in the endogenous business dataset of financial institutions, and the regulatory compliance restriction data subset in the exogenous association dataset of financial markets, perform resource allocation boundary constraint determination processing to obtain the resource allocation constraint condition dataset. D5. Based on the resource allocation constraint dataset, and according to the resource output benefit quantification dataset and the external environment adaptability assessment dataset, perform preliminary resource allocation processing to obtain the initial resource allocation dataset. D6. Based on the resource input status feature dataset, perform dynamic correction processing on the initial resource configuration dataset to obtain the resource configuration analysis dataset.

[0110] The specific resource management data subset may include one or more specific resource management data sets. This specific resource management data subset can be viewed as a subdivided subset of the financial institution's internal business data that focuses on resource management, and may include, but is not limited to, information such as the amount, direction, cycle, and allocation recipients of various resources. This application does not impose any restrictions on this.

[0111] Resource input feature extraction can be understood as the process of mining the core attributes, distribution patterns, and structural characteristics of resource input from specific resource management data. A resource input status feature dataset can include one or more resource input status feature data points. These feature data points can refer to structured data recording relevant characteristics of resource input, and may include, but are not limited to, information such as resource type, input scale, allocation ratio, utilization efficiency, and historical input trajectory.

[0112] Specifically, a mapping relationship can be established between special resource management data and resource input characteristics to extract input characteristics related to resources such as funds, human resources, channels, and technology from the special resource management data subset. For example, the total annual investment in financial resources can be extracted as 500 million yuan, of which marketing funds account for 30%, credit funds account for 50%, and R&D funds account for 20%; the total staff of human resources can be extracted as 800 people, with business teams accounting for 60%, risk control teams accounting for 20%, and functional departments accounting for 20%; and the characteristics of channel resources such as 30 offline outlets, 2 online platforms, and 15 cooperative channels can be extracted. These characteristics can be integrated to form the above-mentioned resource input status characteristic dataset to clearly present the current resource allocation pattern.

[0113] The subset of revenue-related data can include one or more revenue-related data points. This revenue-related data can be viewed as a sub-set of financial institutions' internal business data that is directly related to revenue, and may include, but is not limited to, financial data such as revenue, profit, fee income, and interest income from various business lines. Resource output efficiency calculation can be understood as the process of quantifying the economic returns and business value generated by various resource inputs by linking resource inputs with business revenue data.

[0114] The resource output efficiency quantification dataset may include one or more resource output efficiency quantification data, which may refer to structured data containing core resource efficiency indicators. Optionally, core resource efficiency indicators may include, but are not limited to, return on investment, human resource input-output ratio, channel revenue contribution, and technology input conversion rate.

[0115] Specifically, input-output correlation analysis can be used to calculate the efficiency indicators of various resources. For example, regarding financial resources, the following formula can be used: Profit of a business line ÷ Capital investment of that business line × 100%, to calculate, for example, a 15% return on investment for credit business and an 8% return on investment for wealth management business; regarding human resources, the following formula can be used: Total revenue of a team ÷ Total human resource costs of that team × 100%, to calculate, for example, a 300% human resource input-output ratio for a business team and a 180% human resource input-output ratio for a risk control team; regarding channel resources, the following formula can be used: Transaction volume of online platform ÷ Investment in online channel construction × 100%, to calculate, for example, a 250% revenue contribution from online platform channels; and these quantitative results can be further organized to form the aforementioned resource output efficiency quantitative dataset.

[0116] The subset of external market environment adaptation data may include one or more sets of external market environment adaptation data. This subset can be viewed as a sub-set of exogenous correlation data in the financial market that is related to resource allocation adaptability. This subset may include, but is not limited to, information such as industry growth trends, policy-supported areas, market demand hotspots, and peer resource allocation benchmarks.

[0117] External environment adaptability analysis can be understood as the process of assessing the degree of alignment between the current resource allocation direction and the external market environment, industry trends, and policy guidance. The external environment adaptability assessment dataset may include one or more external environment adaptability assessment data sets, which can refer to structured data containing the adaptability analysis results. This external environment adaptability assessment data may include, but is not limited to, adaptability areas, adaptability scores, expected policy benefits, market opportunity size, and adaptability risk warnings; this application does not impose any limitations on this.

[0118] Specifically, adaptability analysis can be conducted by establishing a mapping relationship between external market environment data and adaptability assessment dimensions. This mapping relationship can be represented by a mapping relationship table (which pre-defines the correspondence between environmental dimensions such as policies, industries, and markets and adaptability scoring rules). Adaptability analysis can be performed based on subsets of external market environment data, such as policies, industries, and markets. For example, in the policy dimension, if inclusive finance and green finance are identified as key policy support areas, the adaptability score for a 20% allocation of current credit resources towards inclusive finance can be assessed as 85 points. In the industry dimension, if the growth rate of the new energy and technology industries reaches 18%, the adaptability score for a 15% allocation of current R&D resources towards the technology finance sector can be assessed as 75 points. In the market dimension, if the annual growth rate of personal wealth management demand is 12%, the adaptability score for a 25% allocation of current marketing resources towards wealth management can be assessed as 90 points. Further integrating this information can form the aforementioned external environment adaptability assessment dataset, clarifying external opportunities and adjustment directions for resource allocation.

[0119] Resource-related data subsets can be viewed as subdivided subsets of a financial institution's internal business data that are related to resource constraints. This subset may include one or more resource-related data points, which may include, but are not limited to, data on total funding limits, staffing quotas, channel development capacity, and technology research and development cycle limitations.

[0120] The regulatory compliance restriction data subset may include one or more regulatory compliance restriction data, which may refer to regulatory requirements related to resource allocation in the exogenous related data of the financial market, including but not limited to capital adequacy requirements, credit concentration limits, compliant fund usage standards, and anti-money laundering related resource allocation regulations.

[0121] The process of determining resource allocation boundary constraints can be understood as integrating the aforementioned internal and external data to clarify the upper and lower limits, prohibitive requirements, and other boundary conditions for resource allocation. The resource allocation constraint dataset can include one or more resource allocation constraint data points, which can refer to structured data containing resource allocation constraint information. This resource allocation constraint data can be divided into two categories: internal constraints and external constraints. Internal constraints can refer to the financial institution's internal resource carrying capacity limitations, while external constraints can refer to external requirements such as regulatory policies and compliance standards.

[0122] Specifically, internal constraints can be extracted from the resource-related data subset, such as a maximum total funding amount of 800 million yuan, a maximum staff of 1,000 people, and a maximum of 5 offline outlets to be built annually. External constraints can be extracted from the regulatory compliance restriction data subset, such as a capital adequacy ratio of no less than 10.5%, a single customer credit concentration of no more than 10% of net capital, and compliance training resource investment of no less than 5% of total marketing resources. These constraints can then be further categorized and organized according to funding, human resources, channels, and technology to form the aforementioned resource allocation constraint dataset, defining the compliance boundaries and feasibility scope of resource allocation.

[0123] Preliminary resource allocation processing can be understood as the process of formulating an initial resource allocation plan within a resource allocation constraint framework, taking into account resource output efficiency and adaptability to the external environment. The initial resource allocation dataset may include one or more initial resource allocation data sets, which can refer to structured data containing the preliminary resource allocation plan. This initial resource allocation data may include, but is not limited to, the allocation ratio, allocation amount, and allocation sequence of various resources across different business lines, regions, and customer groups.

[0124] Specifically, the goals can be to maximize efficiency, optimize adaptability, and avoid exceeding constraints. For example, multi-objective optimization algorithms can be used to formulate the initial resource allocation plan. Regarding financial resources, considering the rate of return and policy compatibility, 40% of the total 800 million yuan can be allocated to inclusive finance, 30% to wealth management, 20% to lending, and 10% to R&D, while ensuring that the concentration of credit to a single customer does not exceed the limit. In terms of human resources, 100 new positions can be added, with 60 allocated to the business team, 30 to the risk control team, and 10 to the compliance team, to align with market demands and compliance requirements. Regarding channel resources, 70% of the annual channel development funds can be invested in online platform optimization, and 30% in expanding offline branches, to match the high growth trend of online business. This will form and obtain the aforementioned initial resource allocation dataset.

[0125] Dynamic adjustment can be understood as the process of adjusting and optimizing the initial resource allocation plan based on the current characteristics of resource input, ensuring that the plan is adapted to the actual operation and is feasible. After dynamic adjustment, a resource allocation dataset can be generated. This resource allocation analysis dataset can include information such as the optimized resource allocation plan, the basis for the allocation, expected benefits, implementation steps, and adjustment trigger conditions.

[0126] Specifically, information such as historical investment efficiency, resource idleness, and business integration needs in the resource investment status characteristic dataset can be referenced to adjust the initial configuration plan. For example, if the initial plan is to invest 15% of funds in a new business line, but historical data shows that the resource idle rate for this type of new business has reached 20%, the proportion invested in the new business line can be reduced to 10%, and a 3-month observation period can be set. If the initial plan is to add 5 offline outlets, but the current average operating load of offline outlets is only 70%, the addition of 5 offline outlets can be adjusted to 2 offline outlets, and the existing outlet layout can be further optimized. Optionally, if a team's historical input-output ratio is consistently low, its manpower allocation ratio can be appropriately reduced, tilting towards high-efficiency teams, thereby forming and obtaining the above-mentioned resource allocation analysis dataset to ensure that the plan is both strategic and feasible.

[0127] In this embodiment, a three-dimensional analysis framework—comprising internal status analysis, external environment assessment, and constraint boundary determination—enables scientific and precise resource allocation. On one hand, input characteristic extraction and benefit calculation based on endogenous business data provide clear internal data support for resource allocation, avoiding blind investment. On the other hand, combining environmental adaptability analysis and compliance constraint definition with exogenous data ensures resource allocation aligns with industry trends and regulatory requirements, reducing operational risks. Through dual optimization of initial configuration and dynamic correction, the problems of prioritizing investment over efficiency, internal factors over external factors, and planning over implementation in traditional resource allocation can be effectively addressed. The resulting resource allocation analysis dataset can guide financial institutions in rationally allocating core resources such as funds, human resources, channels, and technology, maximizing resource utilization efficiency, optimizing business returns, and minimizing compliance risks, providing solid support for the institution's strategic implementation and sustainable development.

[0128] For examples consistent with the above embodiments, please refer to... Figure 3 , Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application, such as... Figure 3 As shown, it includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps. Acquire endogenous business datasets from financial institutions and exogenous correlation datasets from financial markets; Business expansion analysis dataset is obtained by performing business expansion analysis processing based on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Risk management analysis data is obtained by performing risk management analysis on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Resource allocation analysis data is obtained by performing resource allocation analysis on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Conflict verification processing is performed on the business development analysis dataset, the risk management analysis dataset, and the resource allocation analysis dataset to obtain the financial institution verification analysis dataset. Based on the financial institution verification and analysis dataset, decision recommendations are quantitatively output to obtain the financial institution decision analysis results.

[0129] For those consistent with the above, please refer to Figure 4 , Figure 4 This application provides a schematic diagram of the structure of a data-driven decision analysis device for financial institutions. For example... Figure 4 As shown, the device includes: Acquisition unit 101 is used to acquire the endogenous business dataset of financial institutions and the exogenous correlation dataset of financial markets; The first processing unit 102 is used to perform business expansion analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset. The second processing unit 103 is used to perform risk management analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a risk management analysis dataset. The third processing unit 104 is used to perform resource allocation analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a resource allocation analysis dataset. The fourth processing unit 105 is used to perform conflict verification processing based on the business expansion analysis dataset, the risk management analysis dataset, and the resource allocation analysis dataset to obtain the financial institution verification analysis dataset; The fifth processing unit 106 is used to quantify and output decision recommendations based on the financial institution verification and analysis dataset to obtain the financial institution decision analysis results.

[0130] In one possible implementation, the first processing unit 102 is configured to perform business expansion analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset, specifically for: Customer value feature datasets are obtained by filtering customer value features based on the financial institution’s endogenous business dataset. Based on the business type of the financial institution, each key customer value feature data in the customer value key feature dataset is quantitatively weighted to obtain a business type weighted customer value dataset. Based on the weighted customer value dataset of the business type, a comprehensive customer value score is calculated to obtain a tiered customer value dataset. Based on the subset of cross-market financial transaction data and the subset of market trend prediction data in the aforementioned financial market exogenous correlation dataset, a mapping relationship between market trends and financial product demand is established, resulting in a mapping table between market trends and financial product demand. Based on the customer value stratification dataset and the financial institution's endogenous business dataset, customer demand preference analysis is performed to obtain a customer demand matching dataset. Based on the customer demand matching dataset, the market trend and financial product demand mapping table, the customer value stratification dataset, and the financial market exogenous correlation dataset, business expansion priority analysis is performed to obtain a business expansion analysis dataset.

[0131] In one possible implementation, the second processing unit 103 is configured to perform risk management analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a risk management analysis dataset, specifically for: Static risk feature datasets are obtained by performing static risk feature extraction processing on the endogenous business datasets of the financial institutions and the exogenous correlation datasets of the financial markets. Dynamic risk signal datasets are obtained by performing dynamic risk signal extraction processing based on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Based on the dynamic risk signal dataset and the static risk level data subset in the static risk feature dataset, dynamic risk trend judgment is performed to obtain a dynamic risk trend prediction dataset. Based on the endogenous business dataset of the financial institution and the exogenous correlation dataset of the financial market, multi-type dynamic risk quantification calculations are performed to obtain a multi-type dynamic risk quantification dataset. Dynamic risk level adjustment processing is performed based on the multi-type dynamic risk quantification dataset and the dynamic risk trend prediction dataset to obtain the target dynamic risk level dataset. Based on the static risk feature dataset, the target dynamic risk level dataset, the financial institution's endogenous business dataset, and the financial market's exogenous correlation dataset, cross-risk linkage verification processing is performed to obtain a risk management analysis dataset.

[0132] In one possible implementation, the three processing units 104 are used to perform resource allocation analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a resource allocation analysis dataset, specifically for: Resource input features are extracted from a subset of specialized resource management data in the intrinsic business dataset of the financial institution to obtain a resource input status feature dataset. Based on the business revenue-related data subset and the special resource management data subset in the financial institution's endogenous business dataset, resource output efficiency is calculated to obtain a quantitative dataset of resource output efficiency. Based on the external market environment adaptation data subset in the aforementioned financial market exogenous correlation dataset, external environment adaptability analysis is performed to obtain an external environment adaptability assessment dataset. Based on the resource input status characteristic dataset, the resource-related data subset in the endogenous business dataset of financial institutions, and the regulatory compliance restriction data subset in the exogenous correlation dataset of financial markets, resource allocation boundary constraints are determined to obtain a resource allocation constraint condition dataset. Based on the resource allocation constraint dataset, preliminary resource allocation processing is performed according to the resource output benefit quantification dataset and the external environment adaptability assessment dataset to obtain the initial resource allocation dataset. The initial resource allocation dataset is dynamically corrected based on the resource input status feature dataset to obtain the resource allocation analysis dataset.

[0133] In one possible implementation, the first processing unit 102 is configured to perform business expansion priority analysis processing based on the customer demand matching dataset, the market trend and financial product demand mapping table, the customer value stratification dataset, and the financial market exogenous correlation dataset, to obtain a business expansion analysis dataset, specifically used for: Based on the regional economic data subset in the aforementioned financial market exogenous correlation dataset, the year-on-year GDP growth rate of the respective region is extracted to obtain the GDP growth rate of the respective region. Based on the industry development data subset in the aforementioned financial market exogenous correlation dataset, the year-on-year revenue growth rate of the respective industry is extracted to obtain the industry growth rate. The industry competition saturation is calculated based on the industry development data subset in the aforementioned financial market exogenous correlation dataset. Based on the GDP growth rate of the region, the growth rate of the industry, and the saturation of competition in the same industry, a regional-industry potential analysis dataset is obtained. The customer demand intensity score is calculated based on the customer demand matching dataset. The product demand popularity score is calculated based on the market trend and financial product demand mapping table. Based on the customer demand intensity score and the product demand popularity score, a customer-product fit analysis is performed to obtain a customer-product fit dataset. A comprehensive business expansion score is calculated based on the customer value stratification dataset, the customer-product matching dataset, and the region-industry potential analysis dataset. Based on the comprehensive business development score, the business development priorities are sorted to obtain the business development analysis dataset.

[0134] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the data-driven financial institution decision analysis methods described in the above method embodiments.

[0135] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the data-driven financial institution decision analysis methods described in the above method embodiments.

[0136] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data-driven decision analysis method for financial institutions, characterized in that, The data-driven decision analysis method for financial institutions includes: Acquire endogenous business datasets from financial institutions and exogenous correlation datasets from financial markets; Business expansion analysis dataset is obtained by performing business expansion analysis processing based on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Risk management analysis data is obtained by performing risk management analysis on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Resource allocation analysis data is obtained by performing resource allocation analysis on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Conflict verification processing is performed on the business development analysis dataset, the risk management analysis dataset, and the resource allocation analysis dataset to obtain the financial institution verification analysis dataset. Based on the financial institution verification and analysis dataset, decision recommendations are quantitatively output to obtain the financial institution decision analysis results.

2. The data-driven decision analysis method for financial institutions according to claim 1, characterized in that, The step of performing business expansion analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset includes: Customer value feature datasets are obtained by filtering customer value features based on the intrinsic business datasets of the financial institutions. Based on the business type of the financial institution, each key customer value feature data in the customer value key feature dataset is quantitatively weighted to obtain a business type weighted customer value dataset. Based on the weighted customer value dataset of the business type, a comprehensive customer value score is calculated to obtain a tiered customer value dataset. Based on the subset of cross-market financial transaction data and the subset of market trend prediction data in the aforementioned financial market exogenous correlation dataset, a mapping relationship between market trends and financial product demand is established, resulting in a mapping table between market trends and financial product demand. Based on the customer value stratification dataset and the financial institution's endogenous business dataset, customer demand preference analysis is performed to obtain a customer demand matching dataset. Based on the customer demand matching dataset, the market trend and financial product demand mapping table, the customer value stratification dataset, and the financial market exogenous correlation dataset, business expansion priority analysis is performed to obtain a business expansion analysis dataset.

3. The data-driven decision analysis method for financial institutions according to claim 2, characterized in that, The risk management analysis dataset is obtained by performing risk management analysis based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset, including: Static risk feature datasets are obtained by performing static risk feature extraction processing on the endogenous business datasets of the financial institutions and the exogenous correlation datasets of the financial markets. Dynamic risk signal datasets are obtained by performing dynamic risk signal extraction processing based on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Based on the dynamic risk signal dataset and the static risk level data subset in the static risk feature dataset, dynamic risk trend judgment is performed to obtain a dynamic risk trend prediction dataset. Based on the endogenous business dataset of the financial institution and the exogenous correlation dataset of the financial market, multi-type dynamic risk quantification calculations are performed to obtain a multi-type dynamic risk quantification dataset. Dynamic risk level adjustment processing is performed based on the multi-type dynamic risk quantification dataset and the dynamic risk trend prediction dataset to obtain the target dynamic risk level dataset. Based on the static risk feature dataset, the target dynamic risk level dataset, the financial institution's endogenous business dataset, and the financial market's exogenous correlation dataset, cross-risk linkage verification processing is performed to obtain a risk management analysis dataset.

4. The data-driven decision analysis method for financial institutions according to claim 3, characterized in that, The resource allocation analysis dataset is obtained by performing resource allocation analysis based on the endogenous business dataset of the financial institution and the exogenous correlation dataset of the financial market, including: Resource input features are extracted from a subset of specialized resource management data in the intrinsic business dataset of the financial institution to obtain a resource input status feature dataset. Based on the business revenue-related data subset and the special resource management data subset in the financial institution's endogenous business dataset, resource output efficiency is calculated to obtain a quantitative dataset of resource output efficiency. Based on the subset of external market environment adaptation data in the aforementioned financial market exogenous correlation dataset, external environment adaptability analysis is performed to obtain an external environment adaptability assessment dataset. Based on the resource input status characteristic dataset, the resource-related data subset in the endogenous business dataset of financial institutions, and the regulatory compliance restriction data subset in the exogenous correlation dataset of financial markets, resource allocation boundary constraints are determined to obtain a resource allocation constraint condition dataset. Based on the resource allocation constraint dataset, preliminary resource allocation processing is performed according to the resource output benefit quantification dataset and the external environment adaptability assessment dataset to obtain the initial resource allocation dataset. The initial resource allocation dataset is dynamically corrected based on the resource input status feature dataset to obtain the resource allocation analysis dataset.

5. The data-driven decision analysis method for financial institutions according to claim 2, characterized in that, The process of performing business expansion priority analysis based on the customer demand matching dataset, the market trend and financial product demand mapping table, the customer value stratification dataset, and the financial market exogenous correlation dataset yields a business expansion analysis dataset, including: Based on the regional economic data subset in the aforementioned financial market exogenous correlation dataset, the year-on-year GDP growth rate of the respective region is extracted to obtain the GDP growth rate of the respective region. Based on the industry development data subset in the aforementioned financial market exogenous correlation dataset, the year-on-year revenue growth rate of the respective industry is extracted to obtain the industry growth rate. The industry competition saturation is calculated based on the industry development data subset in the aforementioned financial market exogenous correlation dataset. Based on the GDP growth rate of the region, the growth rate of the industry, and the saturation of competition in the same industry, a regional-industry potential analysis dataset is obtained. The customer demand intensity score is calculated based on the customer demand matching dataset. The product demand popularity score is calculated based on the market trend and financial product demand mapping table. Based on the customer demand intensity score and the product demand popularity score, a customer-product fit analysis is performed to obtain a customer-product fit dataset. A comprehensive business expansion score is calculated based on the customer value stratification dataset, the customer-product matching dataset, and the region-industry potential analysis dataset. Based on the comprehensive business development score, the business development priorities are sorted to obtain the business development analysis dataset.

6. A data-driven decision analysis device for financial institutions, characterized in that, The device includes: The acquisition unit is used to acquire endogenous business datasets of financial institutions and exogenous correlation datasets of financial markets. The first processing unit is used to perform business expansion analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset. The second processing unit is used to perform risk management analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a risk management analysis dataset. The third processing unit is used to perform resource allocation analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a resource allocation analysis dataset. The fourth processing unit is used to perform conflict verification processing based on the business expansion analysis dataset, the risk management analysis dataset, and the resource allocation analysis dataset to obtain the financial institution verification analysis dataset. The fifth processing unit is used to quantify and output decision recommendations based on the financial institution's verification and analysis dataset, thereby obtaining the financial institution's decision analysis results.

7. The data-driven financial institution decision analysis device according to claim 6, characterized in that, The first processing unit is configured to perform business expansion analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a business expansion analysis dataset, specifically for: Customer value feature datasets are obtained by filtering customer value features based on the intrinsic business datasets of the financial institutions. Based on the business type of the financial institution, each key customer value feature data in the customer value key feature dataset is quantitatively weighted to obtain a business type weighted customer value dataset. Based on the weighted customer value dataset of the business type, a comprehensive customer value score is calculated to obtain a tiered customer value dataset. Based on the subset of cross-market financial transaction data and the subset of market trend prediction data in the aforementioned financial market exogenous correlation dataset, a mapping relationship between market trends and financial product demand is established, resulting in a mapping table between market trends and financial product demand. Based on the customer value stratification dataset and the financial institution's endogenous business dataset, customer demand preference analysis is performed to obtain a customer demand matching dataset. Based on the customer demand matching dataset, the market trend and financial product demand mapping table, the customer value stratification dataset, and the financial market exogenous correlation dataset, business expansion priority analysis is performed to obtain a business expansion analysis dataset.

8. The data-driven financial institution decision analysis device according to claim 7, characterized in that, The second processing unit is used to perform risk management analysis processing based on the financial institution's endogenous business dataset and the financial market's exogenous correlation dataset to obtain a risk management analysis dataset, specifically for: Static risk feature datasets are obtained by performing static risk feature extraction processing on the endogenous business datasets of the financial institutions and the exogenous correlation datasets of the financial markets. Dynamic risk signal datasets are obtained by performing dynamic risk signal extraction processing based on the endogenous business dataset of the financial institutions and the exogenous correlation dataset of the financial markets. Based on the dynamic risk signal dataset and the static risk level data subset in the static risk feature dataset, dynamic risk trend judgment is performed to obtain a dynamic risk trend prediction dataset. Based on the endogenous business dataset of the financial institution and the exogenous correlation dataset of the financial market, multi-type dynamic risk quantification calculations are performed to obtain a multi-type dynamic risk quantification dataset. Dynamic risk level adjustment processing is performed based on the multi-type dynamic risk quantification dataset and the dynamic risk trend prediction dataset to obtain the target dynamic risk level dataset. Based on the static risk feature dataset, the target dynamic risk level dataset, the financial institution's endogenous business dataset, and the financial market's exogenous correlation dataset, cross-risk linkage verification processing is performed to obtain a risk management analysis dataset.

9. A terminal, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the data-driven financial institution decision analysis method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the data-driven financial institution decision analysis method as described in any one of claims 1-5.

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