Financial product matching recommendation method, computer equipment, medium and program product

Abstract

The invention relates to the technical field of information processing, and discloses a financial product matching recommendation method, computer equipment, a medium and a program product. The method is applied to a financial product matching recommendation system, comprises a plurality of components connected in sequence, and comprises the following steps: connecting a data acquisition component with a supply and marketing system of an enterprise, and acquiring operation data of each time stage in the supply and marketing system through the data acquisition component; preprocessing the operation data of each time stage through a data processing component to obtain target data of each time stage; analyzing the target data of each time stage through a data analysis component to determine a financial demand view of the enterprise; matching according to the financial demand view of the enterprise and the on-sale financial product set through a product matching component based on a preset matching rule to determine a matching result of each time stage; and recommending the matching result of each time stage to an enterprise through an intelligent recommendation component. According to the invention, accurate recommendation can be carried out on enterprises.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a financial product matching recommendation method, computer equipment, medium and program product. Background Art

[0002] Financial product institutions, such as banks, traditionally operate in a model whereby enterprises proactively initiate funding demands, and banks verify the funding demands of enterprises through a series of methods. The financial products independently selected by enterprises may not necessarily meet their real funding demands, resulting in the funds of the financial products independently selected by enterprises not meeting their actual needs.

[0003] Therefore, there is an urgent need for a financial product matching recommendation method that can match financial products according to enterprise needs, improve matching accuracy, and make recommendations to enterprises. Summary of the invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problem in the related art that enterprises independently select financial products, resulting in a low match between financial products and the actual needs of enterprises.

[0005] In order to solve the above technical problems, the present invention provides a financial product matching recommendation method, which is applied to a financial product matching recommendation system. The financial product matching recommendation system includes a data acquisition component, a data processing component, a data analysis component, a product matching component and an intelligent recommendation component connected in sequence. The financial product matching recommendation method includes:

[0006] Connecting the data acquisition component to the supply and marketing system of the enterprise, and collecting the operating data of each time period in the supply and marketing system through the data acquisition component; the operating data includes: sales data, enterprise data, inventory data, order data and cost data;

[0007] The data processing component pre-processes the operating data of each time period to obtain the target data of each time period; the pre-processing includes: data extraction, data cleaning and data standardization;

[0008] The data analysis component is used to analyze the target data of each time period to determine the financial demand view of the enterprise; the financial demand view includes the purchase history, purchase preference, billing cycle, predicted capital demand and credit score of the enterprise at each time period;

[0009] The product matching component obtains a set of financial products on sale, and matches the set of financial products on sale with the financial demand view of the enterprise based on preset matching rules to determine matching results for each time period; the set of financial products on sale includes all financial products on sale by the financial product institution; the matching results for each time period include at least one financial product; and the preset matching rules include at least one matching rule;

[0010] Recommending matching results of various time periods to enterprises through the intelligent recommendation component;

[0011] Among them, when matching the enterprise's financial needs view and the set of financial products on sale based on preset matching rules to determine the matching results for each time stage, a first product set and a second product set are determined based on the set of financial products on sale and the purchase history in the enterprise's financial needs view, the first product set includes financial products with the same purchase history in the set of financial products on sale, and the second product set includes financial products in the set of financial products on sale other than the first product set; based on the preset matching rules, the first matching results for each time stage are determined based on the enterprise's financial needs view and the first product set, and the first matching results for each time stage are recommended to the enterprise through the intelligent recommendation component. When receiving the enterprise's indication information of refusing to purchase, the second matching results for each time stage are determined based on the preset matching rules based on the enterprise's financial needs view and the second product set, and the second matching results for each time stage are recommended to the enterprise through the intelligent recommendation component.

[0012] In an optional implementation, the data collection component is connected to the supply and marketing system of the enterprise, and the business data of each time period in the supply and marketing system is collected by the data collection component, including:

[0013] The data collection component is connected to the supply and marketing system of the enterprise according to a preset connection method. When the preset connection method is an interface method, the operating data of each time period in the supply and marketing system is collected through a data collection interface developed according to the database structure of the supply and marketing system.

[0014] In an optional implementation, when the preset matching rule includes multiple matching rules, the matching results of each time stage are determined by matching the enterprise's financial demand view and the set of financial products on sale based on the preset matching rule, including:

[0015] Based on each of the preset matching rules, matching is performed according to the financial demand view of the enterprise and the set of financial products on sale to determine the matching results of each time stage under each matching rule.

[0016] In an optional implementation, recommending the matching results of each time period to the enterprise through the intelligent recommendation component includes:

[0017] Based on a preset recommendation method, the matching results of each time period under each matching rule are automatically recommended to the enterprise through the intelligent recommendation component; the preset recommendation method includes at least one recommendation method.

[0018] In an optional implementation, when the financial product matching recommendation system includes a recommendation method setting interface, the method further includes:

[0019] The preset recommendation method is set in the recommendation method setting interface, and the priority corresponding to each recommendation method in the preset recommendation method is set.

[0020] In an optional implementation, when the financial product matching recommendation system includes a matching rule setting interface, the method further includes:

[0021] The preset matching rules are defined in the matching rule setting interface, and the priority corresponding to each matching rule in the preset matching rules is set.

[0022] In an optional implementation, when the financial product matching recommendation system includes a risk preference setting interface, the method further includes:

[0023] Set the enterprise's risk preference in the risk preference setting interface;

[0024] The intelligent recommendation component recommends the matching results of each time period to the enterprise, including:

[0025] Based on the risk preference of the enterprise, the matching results of each time stage are recommended to the enterprise through the intelligent recommendation component.

[0026] In a second aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the financial product matching recommendation method of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0027] In a third aspect, the present invention provides a computer-readable storage medium, a single computer-readable storage medium storing computer instructions, the computer instructions being used to enable a computer to execute the financial product matching recommendation method of the first aspect or any corresponding embodiment thereof.

[0028] In a fourth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the financial product matching recommendation method of the first aspect or any corresponding embodiment thereof.

[0029] The technical solution provided by the present invention has the following technical effects:

[0030] In the technical solution of the embodiment of the present invention, the data acquisition component is connected to the supply and marketing system of the enterprise: by directly connecting to the supply and marketing system of the enterprise, the business data of the enterprise, including sales, enterprise, inventory, order and cost data, can be obtained in real time and accurately, ensuring the reliability and timeliness of the data source. The data processing component preprocesses the business data: data preprocessing includes data extraction, cleaning and standardization, which can ensure the quality and consistency of the data and provide a reliable basis for subsequent data analysis. The data analysis component determines the financial demand view of the enterprise: by analyzing the target data of each time stage obtained after preprocessing, a detailed financial demand view can be constructed to help financial institutions better understand the needs of the enterprise. The product matching component obtains and matches the set of financial products on sale: by presetting the matching rules, at least one financial product can be recommended for each business stage based on the financial demand view and the set of financial products on sale of the enterprise, thereby improving the accuracy and applicability of the matching. The intelligent recommendation component recommends the matching results to the enterprise: it can provide personalized financial product recommendations based on the preferences and behaviors of the enterprise, thereby improving the satisfaction and acceptance of the enterprise. By analyzing the purchase history, the set of financial products on sale can be divided into financial products that are the same as the company's historical purchases (first product set) and different financial products (second product set), which can improve the pertinence of recommendations. Prioritize the recommendation of financial products that are the same as the company's historical purchases, which helps to build the company's trust and familiarity with the products and improve the willingness to buy. If the company rejects the recommendation of the first product set, a recommendation of the second product set can be provided, which increases the diversity and flexibility of the recommendation and reduces the risk of corporate loss. The technical solution of the embodiment of the present invention can improve the accuracy of matching: by analyzing the operating data of each time stage in the supply and marketing system, more accurate financial product recommendations can be provided. By considering the purchase history and preferences of the enterprise, personalized services can be provided. Through intelligent recommendations and flexible matching rules, the user experience and satisfaction of the enterprise can be improved. Automated and intelligent recommendation processes can reduce manual operations and improve operational efficiency. By considering the credit score and funding needs of the enterprise, the risk of financial institutions can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 is a flowchart of a financial product matching recommendation method according to an embodiment of the present invention;

[0033] Figure 2 is a structural diagram of a financial product matching recommendation system according to an embodiment of the present invention;

[0034] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] The embodiments of the present invention provide a financial product matching recommendation method, computer equipment, medium and program product to solve the traditional operation mode in the related technology: the enterprise actively initiates capital demand, and the financial products independently selected by the enterprise may not meet the actual capital demand of the enterprise, resulting in the funds of the financial products independently selected by the enterprise not meeting the actual needs of the enterprise, resulting in low matching accuracy.

[0037] An embodiment of the present invention provides an embodiment of a financial product matching recommendation method. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer device such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] Figure 1 It is a flowchart of the financial product matching recommendation method according to an embodiment of the present invention.

[0039] like Figure 1As shown, an embodiment of the present invention provides a financial product matching recommendation method, which is applied to a financial product matching recommendation system. The financial product matching recommendation system includes a data acquisition component, a data processing component, a data analysis component, a product matching component and an intelligent recommendation component connected in sequence. The financial product matching recommendation method includes:

[0040] S101: Connect the data collection component to the supply and marketing system of the enterprise, and collect the operating data of each time period in the supply and marketing system through the data collection component.

[0041] In this embodiment, the operating data includes but is not limited to: sales data, enterprise data, inventory data, order data and cost data. Sales data includes but is not limited to: total sales (accounts receivable): the sum of all sales in a period of time, the time of collection. Sales growth rate: the rate of change of sales data over time. Sales volume: the number of products or services sold. Sales channel: the channel where sales occur, such as online, offline, direct sales, distribution, etc. Product / service category sales: sales of different product or service categories. Sales forecast: sales forecast based on historical data and market trends. Enterprise data includes but is not limited to: basic information of the enterprise: including enterprise name, unified social credit code, etc. Enterprise purchase history: the record of financial products purchased by the enterprise in the past. Enterprise repayment record: the repayment record of financial products in the past. Enterprise feedback: complaints, suggestions and evaluations of the enterprise. Enterprise satisfaction: the satisfaction of the enterprise with financial products. Enterprise loyalty: the frequency of repeated purchases of financial products by the enterprise. Enterprise segmentation: divide the enterprise into different groups according to enterprise characteristics. Inventory data includes but is not limited to: inventory level: the current inventory quantity. Inventory turnover rate: the turnover speed of inventory. Inventory cost: the cost of holding inventory, including storage, insurance, loss, etc. Inventory replenishment: the time and frequency of inventory replenishment. Stockout and surplus: the situation of insufficient or surplus inventory. Inventory accuracy: the accuracy of inventory records. Inventory location: the location of inventory in the warehouse. Inventory safety level: the minimum inventory level set to prevent stockouts. Order data includes but is not limited to: Order quantity: the total number of orders received within a certain period of time. Order amount: the total amount of the order. Order processing time: the time from order receipt to completion processing. Order cancellation rate: the proportion of cancelled orders. Order completion rate: the proportion of successfully completed orders. Order delay rate: the proportion of delayed delivery of orders. Order source: the source channel of the order. Order regional distribution: the geographical distribution of the order. Order product mix: the combination of different products or services in the order. Cost data includes but is not limited to: Direct costs: costs directly related to the production of products or services, such as raw materials, direct labor, etc. Indirect costs: costs not directly related to the production of products or services, such as management expenses, sales and marketing expenses, etc. Fixed costs: costs that do not change with output, such as rent, depreciation, etc. Variable costs: Costs that change with output, such as raw material costs. Total costs (accounts payable): Total costs of running a business, payment schedule. Cost structure: Distribution of costs across different categories. Cost efficiency: Efficiency of cost control. Cost forecast: Cost forecast based on historical data and market trends.

[0042] In this embodiment, the data collection component can be used to collect the operating data of each time period in the supply and marketing system in real time. The data collection component can also be used to collect industry information outside the supply and marketing system, for example, industry reports: published by professional consulting companies, research institutions or industry associations, providing information such as industry trends, market size, and competition landscape. Government and public data: statistical data, policy documents, and industry specifications released by government agencies. Financial market data: data from stock exchanges and financial information service platforms, such as stock prices, trading volumes, and company financial reports.

[0043] The industry information of these external data can provide rich background knowledge and market insights for the financial product matching recommendation system, helping the system to more accurately analyze the financial needs of enterprises, thereby providing financial product recommendations that better meet the needs of enterprises. By integrating this information, the system can better understand the market environment, predict industry trends, and adjust the recommendation strategy accordingly to improve enterprise satisfaction and business efficiency.

[0044] In this embodiment, the various time stages of the order life cycle may include but are not limited to: Order creation stage: The enterprise initiates and can place an order through various channels such as online platforms, telephone, email, etc. The enterprise needs to provide key information such as product model, quantity, payment method, and delivery address when placing an order. Order processing stage: Once the order is created, it enters the order processing stage. In this stage, the information of the order is verified, reviewed, and inventory availability is checked. Order execution and production stage: After the order processing is completed, the order enters the execution and production stage. If the order involves physical products, this stage includes the manufacturing and production of the product. Ensure that the required raw materials and resources can be supplied in time to complete the order on time. Order distribution and delivery stage: After the production of the order is completed, the next step is the distribution and delivery stage of the order. This involves logistics, transportation, and delivery arrangements. After-sales service and support stage: After-sales service may include technical support, maintenance services, return and exchange policies, etc. Order settlement and analysis stage: The enterprise needs to verify whether the order is completed as agreed, and then process the settlement and invoice. At the same time, through the analysis of order data, companies can understand order completion status, buyer preferences and supply chain performance, thereby optimizing future operational strategies and decisions.

[0045] In this embodiment, S101 connects the data collection component to the supply and marketing system of the enterprise, and collects the business data of each time period in the supply and marketing system through the data collection component, specifically including:

[0046] The data collection component is connected to the supply and marketing system of the enterprise according to the preset connection method. When the preset connection method is the interface method, the operating data of each time period in the supply and marketing system is collected through the data collection interface developed according to the database structure of the supply and marketing system.

[0047] In this embodiment, the preset connection mode includes not only the above interface connection mode, but also the operation data of each time stage can be obtained by connecting to the supply and marketing system database. The operation data of each time stage can be stored in the database in the form of a table.

[0048] S102: Pre-processing the operating data of each time period through the data processing component to obtain the target data of each time period.

[0049] In this embodiment, the preprocessing method includes but is not limited to: data extraction, data cleaning and data standardization. Data extraction includes obtaining specific business data from business data of various time stages according to preset fields (such as field names), for example, the company name in the enterprise data, the total sales amount, collection time, order data in the sales data, and the total cost and payment time in the cost data. Data cleaning includes the process of identifying and correcting errors, missing values, duplicate records and outliers in the data. The purpose of data cleaning is to improve data quality and ensure the reliability of analysis results. It can include filling missing values, deleting duplicate records, correcting erroneous data and processing outliers. Data standardization includes the process of configuring the fields in the extracted data with the preset fields according to the preset field mapping rules. This includes determining which original data fields will be retained, converted or discarded, and how to map these fields to the new data structure. Field mapping helps to ensure the consistency and accuracy of data, and unify the format and units.

[0050] S103: Analyze the target data of each time period through the data analysis component to determine the financial demand view of the enterprise.

[0051] In this embodiment, the financial demand view includes, but is not limited to: the purchase history, purchase preferences, billing cycle, predicted capital demand and credit score of the enterprise at various time stages, and may also include industry development trends determined based on industry information. Specifically, the technical solution of determining the financial demand view of the enterprise by analyzing the target data at various time stages through the data analysis component can be realized by building an analysis model.

[0052] In this embodiment, as an example, the target data and industry information of each time period can be analyzed by the data analysis component to determine the financial demand view of the enterprise. The purchase history of the enterprise at each time period can be determined based on the enterprise data, the purchase preference can be determined based on the purchase history of the enterprise at each time period, the billing cycle can be determined based on the sales data and cost data, the fund forecast demand can be predicted based on the cost data and sales data, and the credit score can be determined based on the enterprise purchase history and the enterprise repayment record.

[0053] S104: Obtain the set of financial products on sale through the product matching component, and match the enterprise's financial demand view and the set of financial products on sale based on preset matching rules to determine the matching results for each time stage.

[0054] In this embodiment, the set of financial products on sale includes all financial products on sale by the financial product institution. The matching result of each time period includes at least one financial product. The preset matching rule includes at least one matching rule. The financial products may include deposit products, loan products and remittance products.

[0055] In this embodiment, when the preset matching rules include multiple matching rules, S104 matches the enterprise's financial demand view and the set of financial products on sale based on the preset matching rules to determine the matching results for each time period, specifically including: matching the enterprise's financial demand view and the set of financial products on sale based on each matching rule in the preset matching rules to determine the matching results for each time period under each matching rule.

[0056] As an example, one of the preset matching rules may be to match the billing cycle and predicted capital demand of the enterprise at each time stage with the set of financial products on sale to determine the matching results at each time stage. Another matching rule may be to preferentially match the billing cycle and predicted capital demand of the enterprise at each time stage with the set of financial products on sale to determine the initial matching results at each time stage. Then, the purchase history, purchase preference and credit score of the enterprise at each time stage are matched with the initial matching results at each time stage to determine the final matching results at each time stage. Another matching rule may be to preferentially match the purchase history of the enterprise at each time stage with the set of financial products on sale to determine the initial matching results at each time stage. Then, the purchase preference, billing cycle, predicted capital demand and credit score of the enterprise at each time stage are matched with the initial matching results at each time stage to determine the final matching results at each time stage. Specifically, different weights may be assigned to the data in the financial demand view (e.g., purchase history, purchase preference, billing cycle, predicted capital demand and credit score), and the matching degree is calculated according to the weight, and the first preset number of financial products with a matching degree greater than a preset value or a larger matching degree are used as matching results.

[0057] S105: Recommend matching results of each time period to the enterprise through the intelligent recommendation component.

[0058] In this embodiment, when matching the enterprise's financial demand view and the set of financial products on sale is performed based on the preset matching rules to determine the matching results of each time stage, as an example, S104 specifically includes: determining the first product set and the second product set according to the purchase history in the set of financial products on sale and the enterprise's financial demand view, the first product set includes financial products with the same purchase history in the set of financial products on sale, and the second product set includes financial products other than the first product set in the set of financial products on sale. Determine the first matching result of each time stage according to the enterprise's financial demand view and the first product set based on the preset matching rules. Based on the above example, the corresponding S105 specifically includes: recommending the first matching result of each time stage to the enterprise through the intelligent recommendation component, and when receiving the indication information that the enterprise refuses to purchase, the above S104 also includes: determining the second matching result of each time stage according to the enterprise's financial demand view and the second product set based on the preset matching rules. The corresponding S105 also includes: recommending the second matching result of each time stage to the enterprise through the intelligent recommendation component. In the above example, matching and recommending are performed first according to the first product set, and then matching and recommending are performed according to the second product set, which can improve the customer's purchase success rate while improving the matching and recommendation efficiency.

[0059] In this embodiment, the matching results of each time stage are recommended to the enterprise through the intelligent recommendation component in S105, specifically including: automatically recommending the matching results of each time stage under each matching rule to the enterprise through the intelligent recommendation component based on the preset recommendation method. The preset recommendation method includes at least one recommendation method. As an example, the preset recommendation method may include SMS recommendation, phone call recommendation, marketing personnel visit recommendation, etc. Setting different recommendation methods can improve the recommendation success rate.

[0060] In this embodiment, the financial product matching recommendation method can be constructed as a model, and the technical solution of the present invention can be implemented by means of a model. Specifically, a general supply and marketing model based on the industrial supply chain can be constructed based on the financial product matching recommendation method. The model can be automatically embedded in the supply and marketing system of the enterprise, that is, it can be automatically embedded in the industrial ecological chain of the partner enterprise. Through big data analysis, the hidden financial needs of upstream and downstream enterprises in the supply chain can be excavated for accurate matching and recommendation. The model is applied to the orders, accounts receivable and payable of the enterprise, and based on the historical operating data and industry dynamics analysis of the enterprise, it predicts the capital demand and capital risk of the enterprise in the future, and has high practical value.

[0061] In an optional implementation, when the financial product matching recommendation system includes a recommendation mode setting interface, the financial product matching recommendation method further includes:

[0062] In the recommendation method setting interface, set the preset recommendation method and set the priority of each recommendation method in the preset recommendation method. You can also set the recommendation frequency, and you can also divide the enterprise into different levels according to the preset range based on the amount of funds corresponding to the financial products in the enterprise's purchase history, so as to support differentiated recommendations for enterprise groups. As an example, the preset range includes 300,000-400,000, 200,000-300,000, 100,000-200,000, 50,000-100,000, 10,000-50,000, etc., and the corresponding enterprise level can be level one, level two, level three, level four, level five, etc. Specifically, you can set the priority of each recommendation method in the preset recommendation method according to the enterprise level. For a level one enterprise (300,000-400,000), when the preset recommendation method includes SMS recommendation, phone recommendation, and marketing personnel visit recommendation, you can set the priority from high to low as: marketing personnel visit recommendation, phone recommendation, SMS recommendation. For a level one enterprise (10,000-50,000), you can set the priority from high to low as: SMS recommendation, phone recommendation, and marketing personnel visit recommendation.

[0063] In an optional implementation, when the financial product matching recommendation system includes a matching rule setting interface, the financial product matching recommendation method further includes:

[0064] Define the preset matching rules in the matching rule setting interface, and set the priority of each matching rule in the preset matching rules. As an example, when the preset matching rules include three matching rules, set the priority order as matching rule 1 greater than matching rule 2 greater than matching rule 3, then matching is performed according to matching rule 1 first, and matching result 1 obtained by matching according to matching rule 1 is recommended to the enterprise first, then matching is performed according to matching rule 2, and matching result 2 obtained by matching according to matching rule 2 is recommended to the enterprise, and finally matching result 3 obtained by matching according to matching rule 3 is recommended to the enterprise.

[0065] In an optional implementation, when the financial product matching recommendation system includes a risk preference setting interface, the financial product matching recommendation method further includes:

[0066] Set the enterprise's risk preference in the risk preference setting interface.

[0067] The matching results of each time period are recommended to enterprises through the intelligent recommendation component, including:

[0068] Based on the risk preferences of enterprises, matching results for different time periods are recommended to enterprises through intelligent recommendation components.

[0069] In this embodiment, if the risk preference includes low risk, medium risk or high risk, the risk preference of the enterprise can be determined according to the purchase preference of the enterprise at each time stage. For example, if the financial products in the enterprise's purchase history are mostly low-risk products, the enterprise's purchase preference can be determined to be low-risk financial products, thereby determining that the enterprise's risk preference is low risk, and the low-risk financial products in the matching results of each time stage can be recommended to the enterprise through the intelligent recommendation component.

[0070] Based on the technical solution of the present invention, financial products can be recommended at the customer / enterprise level at the time stage required by the customer to meet the customer's capital needs. At the bank level, the customer's real capital intention can be understood to effectively protect the risk of loan issuance. Based on the technical solution of the present invention, the data collection component can be embedded in the supply and marketing system corresponding to the partner enterprise to collect the operating data of the partner enterprise.

[0071] The data processing component can extract and process the data after format conversion, which is preprocessing. The data analysis component can build an industry-based analysis model, conduct big data analysis on the business conditions of enterprises, and explore the real financial needs of customers. The product matching component can match the bank's deposit, loan and remittance products for customers based on their real financial needs and billing cycles. It supports matching rule settings, multiple result matching, and industry and customer risk preference settings. The intelligent promotion component supports automatic push of multiple marketing channels, channel priority settings, and customer group differentiation.

[0072] It should be noted that the contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

[0073] In this embodiment, a financial product matching recommendation system is also provided, and a single device is used to implement the above-mentioned embodiment and optional implementation methods, which have been described and will not be repeated. As used below, the term "component" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0074] Figure 2 It is a structural diagram of a financial product matching recommendation system according to an embodiment of the present invention.

[0075] The present invention provides a financial product matching recommendation system. Figure 2 As shown, the financial product matching recommendation system includes: a data collection component 11, a data processing component 12, a data analysis component 13, a product matching component 14 and an intelligent recommendation component 15 which are connected in sequence.

[0076] The data collection component 11 is used to connect with the supply and marketing system of the enterprise to collect the business data of each time period in the supply and marketing system. The business data includes: sales data, enterprise data, inventory data, order data and cost data.

[0077] The data processing component 12 is used to pre-process the business data of each time period to obtain the target data of each time period. The pre-processing includes: data extraction, data cleaning and data standardization.

[0078] The data analysis component 13 is used to analyze the target data at each time stage to determine the financial demand view of the enterprise. The financial demand view includes the purchase history, purchase preference, billing cycle, predicted capital demand and credit score of the enterprise at each time stage.

[0079] The product matching component 14 is used to obtain the set of financial products on sale, and to match the financial demand view of the enterprise with the set of financial products on sale based on the preset matching rules to determine the matching results of each time period. The set of financial products on sale includes all financial products on sale by the financial product institution. The matching results of each time period include at least one financial product. The preset matching rules include at least one matching rule.

[0080] The intelligent recommendation component 15 is used to recommend the matching results of each time period to the enterprise.

[0081] The product matching component 14 is specifically used to determine a first product set and a second product set according to the set of financial products on sale and the purchase history in the financial demand view of the enterprise, wherein the first product set includes financial products with the same purchase history in the set of financial products on sale, and the second product set includes financial products in the set of financial products on sale except the first product set. The first matching results of each time stage are determined according to the financial demand view of the enterprise and the first product set based on a preset matching rule.

[0082] The intelligent recommendation component 15 is specifically used to recommend the first matching results of each time stage to the enterprise through the intelligent recommendation component.

[0083] The product matching component 14 is further configured to determine second matching results for each time period based on the enterprise's financial demand view and the second product set based on preset matching rules when receiving an indication that the enterprise refuses to purchase.

[0084] The intelligent recommendation component 15 is also used to recommend the second matching results of each time stage to the enterprise through the intelligent recommendation component.

[0085] In an optional embodiment, the data collection component 11 is specifically used to connect the data collection component and the supply and marketing system of the enterprise according to a preset connection method. When the preset connection method is an interface method, the operating data of each time period in the supply and marketing system is collected through a data collection interface developed according to the database structure of the supply and marketing system.

[0086] In an optional embodiment, the product matching component 14 is specifically used to determine the matching results of each time period under each matching rule by matching the enterprise's financial demand view and the set of financial products on sale based on each matching rule in the preset matching rules when the preset matching rules include multiple matching rules.

[0087] In an optional implementation, the intelligent recommendation component 15 is specifically used to automatically recommend the matching results of each time period under each matching rule to the enterprise through the intelligent recommendation component based on a preset recommendation method. The preset recommendation method includes at least one recommendation method.

[0088] In an optional embodiment, when the financial product matching recommendation system includes a recommendation method setting interface, the financial product matching recommendation system also includes a recommendation method setting unit, which is used to set a preset recommendation method in the recommendation method setting interface and set the priority corresponding to each recommendation method in the preset recommendation method.

[0089] In an optional embodiment, when the financial product matching recommendation system includes a matching rule setting interface, the financial product matching recommendation system also includes a matching rule setting unit, which is used to define preset matching rules in the matching rule setting interface and set the priority corresponding to each matching rule in the preset matching rules.

[0090] In an optional implementation, when the financial product matching recommendation system includes a risk preference setting interface, the financial product matching recommendation system also includes a risk preference setting unit, and the risk preference setting unit is used to set the risk preference of the enterprise in the risk preference setting interface.

[0091] The matching results of each time period are recommended to enterprises through the intelligent recommendation component, including:

[0092] Based on the risk preferences of enterprises, matching results for different time periods are recommended to enterprises through intelligent recommendation components.

[0093] The further functional description of the above-mentioned components and units is the same as that of the above-mentioned corresponding embodiments and will not be repeated here.

[0094] The financial product matching recommendation system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0095] The embodiment of the present invention also provides a computer device having the above Figure 2 The financial product matching recommendation system shown.

[0096] See also Figure 3 , Figure 3 Schematic diagram of the hardware structure of the computer device according to the embodiment of the present invention. Figure 3 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In an optional embodiment, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor device). Figure 3 A processor 10 is taken as an example.

[0097] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0098] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0099] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating device, an application required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In an optional embodiment, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0100] The memory 20 may include a volatile memory, such as a random access memory. The memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive. The memory 20 may also include a combination of the above-mentioned types of memory.

[0101] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0102] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0103] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0104] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A financial product matching recommendation method, characterized in that: Applied to a financial product matching recommendation system, the financial product matching recommendation system includes a data acquisition component, a data processing component, a data analysis component, a product matching component and an intelligent recommendation component connected in sequence, and the financial product matching recommendation method includes: Connecting the data acquisition component to the supply and marketing system of the enterprise, and collecting the operating data of each time period in the supply and marketing system through the data acquisition component; the operating data includes: sales data, enterprise data, inventory data, order data and cost data; The data processing component pre-processes the operating data of each time period to obtain the target data of each time period; the pre-processing includes: data extraction, data cleaning and data standardization; The data analysis component is used to analyze the target data of each time period to determine the financial demand view of the enterprise; the financial demand view includes the purchase history, purchase preference, billing cycle, predicted capital demand and credit score of the enterprise at each time period; The product matching component obtains a set of financial products on sale, and matches the set of financial products on sale with the financial demand view of the enterprise based on preset matching rules to determine matching results for each time period; the set of financial products on sale includes all financial products on sale by the financial product institution; the matching results for each time period include at least one financial product; and the preset matching rules include at least one matching rule; Recommending matching results of various time periods to enterprises through the intelligent recommendation component; Among them, when matching the enterprise's financial needs view and the set of financial products on sale based on preset matching rules to determine the matching results for each time stage, a first product set and a second product set are determined based on the set of financial products on sale and the purchase history in the enterprise's financial needs view, the first product set includes financial products with the same purchase history in the set of financial products on sale, and the second product set includes financial products in the set of financial products on sale other than the first product set; based on the preset matching rules, the first matching results for each time stage are determined based on the enterprise's financial needs view and the first product set, and the first matching results for each time stage are recommended to the enterprise through the intelligent recommendation component. When receiving the enterprise's indication information of refusing to purchase, the second matching results for each time stage are determined based on the preset matching rules based on the enterprise's financial needs view and the second product set, and the second matching results for each time stage are recommended to the enterprise through the intelligent recommendation component.

2. The method according to claim 1, characterized in that The step of connecting the data collection component to the supply and marketing system of an enterprise and collecting the operating data of each time period in the supply and marketing system through the data collection component includes: The data collection component is connected to the supply and marketing system of the enterprise according to a preset connection method. When the preset connection method is an interface method, the operating data of each time period in the supply and marketing system is collected through a data collection interface developed according to the database structure of the supply and marketing system.

3. The method according to claim 1, characterized in that: When the preset matching rule includes multiple matching rules, the matching results of each time stage are determined by matching the enterprise's financial demand view and the set of financial products on sale based on the preset matching rule, including: Based on each of the preset matching rules, matching is performed according to the financial demand view of the enterprise and the set of financial products on sale to determine the matching results of each time stage under each matching rule.

4. The method according to claim 3, characterized in that The intelligent recommendation component recommends the matching results of each time period to the enterprise, including: Based on a preset recommendation method, the matching results of each time period under each matching rule are automatically recommended to the enterprise through the intelligent recommendation component; the preset recommendation method includes at least one recommendation method.

5. The method according to claim 4, characterized in that When the financial product matching recommendation system includes a recommendation method setting interface, the method further includes: The preset recommendation method is set in the recommendation method setting interface, and the priority corresponding to each recommendation method in the preset recommendation method is set.

6. The method according to claim 1, characterized in that When the financial product matching recommendation system includes a matching rule setting interface, the method further includes: The preset matching rules are defined in the matching rule setting interface, and the priority corresponding to each matching rule in the preset matching rules is set.

7. The method according to claim 1, characterized in that When the financial product matching recommendation system includes a risk preference setting interface, the method further includes: Set the enterprise's risk preference in the risk preference setting interface; The intelligent recommendation component recommends the matching results of each time period to the enterprise, including: Based on the risk preference of the enterprise, the matching results of each time stage are recommended to the enterprise through the intelligent recommendation component.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the financial product matching recommendation method according to any one of claims 1 to 7 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the financial product matching recommendation method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the financial product matching recommendation method according to any one of claims 1 to 7.

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