TWI769385B - Method and system for screening potential purchasers of financial products - Google Patents

Abstract

A method for screening potential purchasers of financial products, implemented by a computer device, the computer device storing a plurality of pieces of credit ability data related to a plurality of customers, and a plurality of pieces of financial data related to the customers in a previous period Total historical asset information for conditions, as well as multiple adverse conditions. The method includes the following steps: the computer device determines whether the customer is a high spending power customer according to the credit ability data and the total historical asset data corresponding to the customer; the computer device determines whether the customer is a high spending power customer according to the total historical asset data corresponding to the high spending power customer and the Unfavorable conditions, determine whether a customer with high spending power is a customer to be recommended; the computer device uses a customer classification model to classify the customer to be recommended as a recommended customer category or a non-recommended customer category according to the total historical asset data of the recommended customer.

Description

Method and system for screening potential purchasers of financial products

The present invention relates to a customer analysis system, in particular to a customer screening system related to the financial field.

At present, there are many financial products issued by the banking or insurance industry in the financial field. Therefore, more and more relevant R&D personnel use various AI artificial intelligence technologies to analyze and filter the collected customer data, so as to obtain whether the customer has sufficient The results of various information such as spending power and the possibility of purchasing financial products, so as to market various financial products to appropriate customers based on the analysis results.

However, in practice, the results obtained after simply analyzing customer data with AI artificial intelligence, even if various existing AI artificial intelligence technologies are used, the accuracy rate still cannot achieve the expected results, and the accuracy rate is not good. The results of the analysis often result in the banking industry or the insurance industry being unable to find suitable customers, so that the promotion of the issued financial products is difficult and ineffective.

Therefore, how to propose a method and system that can match AI artificial intelligence and make the obtained analysis results more accurate is the primary issue that this creation intends to solve.

Therefore, the purpose of the present invention is to provide a method for screening potential purchasers of financial products that pre-processes the input data and uses AI artificial intelligence to obtain more accurate analysis results.

Therefore, the screening method for potential purchasers of financial products of the present invention is implemented by a computer device, the computer device stores a plurality of pieces of credit ability data related to the credit of a plurality of customers, and a plurality of pieces of credit ability data related to the customers in a Aggregate historical asset information on the financial position of a prior period defined from a previous point in time to a current point in time, and a number of adverse conditions related to various industry and customer economic conditions, including a step (A), a step ( B), a step (C).

In step (A), for each customer, determine whether the customer is a high spending power customer according to the credit capability data and the total historical asset data corresponding to the customer through the computer device.

Step (B) is, for each high spending power customer, by the computer device, according to the total historical asset data and the unfavorable conditions corresponding to the high spending power customer, determine whether the high spending power customer is a desire Recommend clients.

Step (C) is, for each customer to be recommended, using the computer device to use a customer classification for classifying the customer as a recommended customer or a non-recommended customer according to the total historical asset data corresponding to the customer to be recommended The model classifies the customer to be recommended as one of a recommended customer category and a non-recommended customer category.

Another object of the present invention is to provide a potential buyer group screening system for financial products that pre-processes the input data and uses AI artificial intelligence to obtain more accurate analysis results.

Therefore, the system for screening potential purchasers of financial products of the present invention includes a storage module and a processing module electrically connected to the storage module.

The storage module stores a plurality of pieces of credit capability data related to the credit of a plurality of customers, a plurality of pieces of total history related to the financial conditions of the customers from a previous point in time to a previous period defined by a current point in time Asset information, as well as a number of adverse conditions related to various industries and customer economic conditions.

Wherein, for each customer, the processing module determines whether the customer is a high spending power customer according to the credit ability data and the total historical asset data corresponding to the customer, and for each high spending power customer, the processing module The group determines whether the high spending power customer is a customer to be recommended according to the total historical asset data corresponding to the high spending power customer and the unfavorable conditions. For each customer to be recommended, the processing module determines whether the customer is to be recommended The corresponding total historical asset data, using a customer classification model for classifying customers as recommended customers or non-recommended customers, classify the customer to be recommended as one of a recommended customer category and a non-recommended customer category .

The effect of the present invention is: before using the customer classification model (AI artificial intelligence) for classification, the computer device performs a data preprocessing that can match the customer classification model, that is, first, according to the customer Corresponding to the credit ability data and the total historical asset data, perform screening for customers with high spending power, and then, according to the total historical asset data and the disadvantageous conditions corresponding to the high spending power customers, screen the customers to be recommended. After the data pre-processing is completed, the total historical asset data corresponding to the customer to be recommended is processed through the customer classification model (AI artificial intelligence), so as to achieve the goal of directly using the customer classification model (AI artificial intelligence) compared to the traditional method. Perform classification to more accurately classify the results of recommended/not recommended customer categories, so as to promote financial products more effectively.

Before the present invention is described in detail, it should be noted that in the following description, similar elements are designated by the same reference numerals.

Referring to FIG. 1 , an embodiment of the system for screening potential purchasers of financial products of the present invention includes a computer device 1 . The computer device 1 includes a storage module 11 , a display module 12 , and a processing module 13 electrically connected to the storage module 11 and the display module 12 .

The storage module 11 stores a plurality of pieces of credit ability data related to the credit of a plurality of customers, and a plurality of pieces of aggregate data about the financial status of the customers from a previous time point to a previous period defined by a current time point. Historical asset information, a number of adverse conditions related to various industries and customer economics, and a customer classification model for classifying customers as recommended or non-recommended. Among them, each customer belongs to one of an enterprise household and a personal household.

The total historical asset data of each customer belonging to the enterprise account includes multiple gross profit margins for the customer at different time points in the previous period, multiple gross profit margins for the customer at different time points in the previous period operating income, multiple collection days corresponding to the customer at multiple different time points in the previous period, multiple business recession ratios corresponding to the customer’s industry at multiple different time points in the previous period, Multiple revenue-to-profit ratios for the customer at multiple different time points in the previous period, multiple revenue information for the customer at multiple different time points in the previous period, Multiple pre-tax profit and loss corresponding to multiple different time points in the period, multiple collection days corresponding to the customer at multiple different time points in the previous period, multiple different time points for the customer in the previous period Corresponding multiple industry prosperity indicators (indicating whether the current time point is recession, progress or flat), and multiple cash flow ratios corresponding to the customer at multiple different time points in the previous period.

The total historical asset data of each customer belonging to the individual account includes the total amount of movable properties of the customer at different time points in the previous period, the total amount of movable assets corresponding to the customer at different time points in the previous period. The total amount of real estate, the personal income of the customer at different time points in the previous period, the high-risk financial products purchased by the customer at different time points in the previous period, and all the financial products purchased The ratio of high-risk investments for the product, and the client's total capital at various points in the previous period, and the client's low-risk investments at various points in the previous period information, and information on multiple high-risk investments by the client at various points in the prior period. Wherein, the low-risk investment information includes multiple savings insurance investment information of the client at multiple different time points in the previous period, and multiple fund investments corresponding to the client at multiple different time points in the previous period Information, multiple stock investment information corresponding to the client at multiple different time points in the previous period; and such high-risk investment information includes multiple target investment information corresponding to the client at multiple different time points in the previous period. Redemption Forward Contract Investment Information (TRF, Target Redemption Forward).

The unfavorable conditions include a plurality of unfavorable conditions for the business account corresponding to the business account and respectively related to a predetermined collection date, a predetermined recession ratio, a predetermined revenue-to-profit ratio, and a plurality of Disadvantages of the individual account corresponding to the individual account and respectively related to a preset high-risk investment ratio and a preset capital ratio.

In particular, in this embodiment, the credit capability data is the corporate credit risk index TCRI level, or is credit rating element data (for example: 3F5C elements, 3F is economic elements, management elements, financial elements; 5C elements are moral character, repayment ability, capital strength, guarantees and business environment conditions), but not limited thereto.

In particular, in this embodiment, the customer classification model utilizes a plurality of business customer training data groups belonging to a plurality of customers of the business customer and their corresponding recommendation results, as well as a plurality of customer data groups belonging to the individual customer. Trained from individual household training data sets and their corresponding recommendation results. Wherein, the enterprise training data set corresponding to each enterprise includes multiple training revenue information corresponding to multiple different time points in the previous period, and multiple training data corresponding to multiple different time points in the previous period. Pre-tax profit and loss, multiple training collection days corresponding to multiple different time points in the previous period, multiple training industry prosperity indicators corresponding to multiple different time points in the previous period, multiple different training periods corresponding to the previous period The multiple training cash flow ratios corresponding to the time point; and the individual account training data group corresponding to each individual household includes the multiple training savings insurance investment information corresponding to multiple different time points in the previous period, the Multiple training fund investment information corresponding to multiple different time points, multiple training stock investment information corresponding to multiple different time points in the previous period, and multiple training sessions corresponding to multiple different time points in the previous period Target redeemable forward contract investment information. In this embodiment, the customer classification model is a supervised model. Wherein, the supervised model is a type of neural network model, a logistic regression model (Logistic Regression), a decision tree model (Decision tree), a support vector machine (SVM, Support Vector Machines) or a radial basis function kernel (Radial basis function), but not limited to this.

In this embodiment, the implementation form of the computer device 1 is, for example, a personal computer, a server or a cloud host, but not limited thereto.

Referring to FIGS. 2 to 4 , the storage module 11 and the display module 12 of the computer device 1 will be described below by performing a method for screening potential purchasers of financial products by the system for screening potential purchasers of financial products of the present invention. As well as the operation details of each element of the processing module 13 , the method for screening potential buyers of financial products includes a step 51 , a step 52 , and a step 53 .

In step 51, for each customer, the processing module 13 determines whether the customer is a high spending power customer according to the credit capability data and the total historical asset data corresponding to the customer; when the processing module 13 determines When it is determined that the customer is the high spending power customer, the process goes to step 52 ; when the processing module 13 determines that the customer is not the high spending power customer, the method for screening potential purchasers of the financial product ends.

As shown in FIG. 3 , step 51 further includes a sub-step 511 and a sub-step 512 .

In sub-step 511 , for each customer, the processing module 13 obtains, according to the total historical asset data corresponding to the customer, a data indicating the financial increase or decrease of the customer in a first time interval in the previous period. Financial Earnings Information. Wherein, the first time interval is a time interval of the past one year, but is not limited thereto.

As shown in FIG. 4 , the sub-step 511 further includes a sub-step 511A, a sub-step 511B, a sub-step 511C, and a sub-step 511D.

In sub-step 511A, for each customer, when the customer belongs to the enterprise, the processing module 13 obtains a gross profit ratio and an operating income ratio according to the total historical asset data corresponding to the customer. The interest rate ratio is the average of all gross profit margins of the customer in the first time interval in the previous period divided by the average of all gross profit margins of all enterprises in the industry corresponding to the customer except the customer in the first time interval , the operating income ratio is the operating income corresponding to the customer at the last time point in the first time interval in the previous period divided by the operating income corresponding to the first time point in the first time interval . In particular, the processing module 13 first obtains from the storage module 11 all gross profit margins of all companies in the industry corresponding to the customer other than the customer, and then calculates the total profit margin of all companies in the industry corresponding to the customer other than the customer. The average of all gross profit margins for that first time period.

In sub-step 511B, for each customer, when the customer belongs to the enterprise, the processing module 13 uses the gross profit ratio and the operating income ratio as the financial income information.

In sub-step 511C, for each customer, when the customer belongs to the individual account, the processing module 13 obtains an available movable property amount and a personal income ratio according to the total historical asset data corresponding to the customer, the available movable property The amount is the average of all movable properties of the client in the first time interval in the preceding period, and the personal income ratio is the last time point in the first time interval in the preceding period for the client. The corresponding personal income is divided by the personal income corresponding to the first time point in the first time interval. In other embodiments, the available chattel amount can be converted into a corresponding grade or score first, and then the subsequent process steps are executed.

In sub-step 511D, for each customer, when the customer belongs to the individual account, the processing module 13 uses the available movable property amount and the personal income ratio as the financial income information.

In sub-step 512, for each customer, the processing module 13 determines whether the customer is the high spending power customer according to the credit capability data and the financial income information corresponding to the customer. When the processing module 13 determines that the customer is the high spending power customer, the process goes to step 52; when the processing module 13 determines that the customer is not the high spending power customer, the potential purchaser of the financial product ends Group screening method.

It is worth noting that, in the sub-step 512 of the present embodiment, for each customer, when the customer belongs to the enterprise, the processing module 13 determines the credit capability data, the gross profit ratio and The operating income ratio determines whether the customer belonging to the business household is the high spending power customer. For example, when the processing module 13 determines that the scores or grades indicated by the customer credit ability data belonging to the enterprise, the gross profit ratio and the operating income ratio all exceed their corresponding preset thresholds, then it belongs to the enterprise. The customer of the enterprise is the high spending power customer.

It is worth noting that, in the sub-step 512 of this embodiment, for each customer, when the customer belongs to the individual account, the processing module 13 determines the credit ability data corresponding to the customer, the available chattel amount and The personal income ratio determines whether the customer belonging to the individual household is the high spending power customer. For example, when the processing module 13 determines that the score or grade indicated by the customer credit ability data belonging to the business household, the amount of movable property available and the personal income ratio all exceed their respective corresponding preset thresholds, it belongs to the enterprise. The customers of individual households are the customers with high spending power.

In step 52, for each high spending power customer, the processing module 13 determines whether the high spending power customer is a customer to be recommended according to the total historical asset data and the disadvantageous conditions corresponding to the high spending power customer . When the processing module 13 determines that the customer with high spending power is the customer to be recommended, the process proceeds to step 53; when the processing module 13 determines that the customer with high spending power is not the customer to be recommended, the financial product ends screening method for potential buyers.

It is worth noting that, in step 52 of the present embodiment, for each high-spending customer, when the high-spending customer belongs to the enterprise, the processing module 13 determines the value in the previous period by determining the Whether any one of all collection days, all recession ratios, and all revenue-to-profit ratios in a second time interval meets one of the unfavorable conditions of the corporate account, so as to determine the high spending power customer Whether it is the recommended customer. Wherein, when the processing module 13 determines that one of the unfavorable conditions of the business owner is met, the customer with high spending power is not the customer to be recommended. The second time interval is a time interval of nearly one year, but is not limited thereto. In other embodiments, the processing module 13 can also determine whether all the collection days, all the recession ratios, and all the revenue and profit ratios in the second time interval meet the unfavorable conditions of the enterprise. any two of them to determine whether the high spending power customer is the customer to be recommended.

It is worth noting that, in step 52 of the present embodiment, for each high-spending customer, when the high-spending customer belongs to the individual household, the processing module 13 determines one of the previous periods for a third time The average of the total amount of funds in the interval is divided by the average of the total amount of funds of the high spending power customer in a fourth time interval in the previous period to obtain an funds ratio. Next, for each high-spending customer, when the high-spending customer belongs to the individual household, the processing module 13 determines the ratio of all high-risk investments in a fifth time interval in the previous period and the Whether any one of the capital ratios meets one of the unfavorable conditions of the individual account is used to determine whether the high spending power customer is the customer to be recommended. Wherein, when the processing module 13 determines that one of the unfavorable conditions of the individual account is met, the customer with high spending power is not the customer to be recommended. The third time interval is the time interval of the past one year, the fourth time interval is the time interval of the past five years, and the fifth time interval is the time interval of the past one year, but not limited to this . In other embodiments, the processing module 13 can also determine whether all the high-risk investment ratios and the capital ratio in the fifth time interval meet any one of the unfavorable conditions of the individual account. Whether customers with high spending power are recommended customers.

In step 53, for each customer to be recommended, the processing module 13 utilizes the customer classification model for classifying the customer as a recommended customer or a non-recommended customer according to the total historical asset data corresponding to the customer to be recommended, The customer to be recommended is classified into one of a recommended customer category and a non-recommended customer category and displayed on the display module 12 .

It is worth noting that, in step 53 of this embodiment, for each customer to be recommended, when the customer to be recommended belongs to the enterprise, the processing module 13 is based on the corresponding revenue information of the customer to be recommended. , the pre-tax profit and loss, the collection date, the industry prosperity indicators, and the cash flow ratio, using the customer classification model, classify the customer to be recommended as the recommended customer category or the non-recommended customer category and displayed on the display module 12 .

It is worth noting that, in step 53 of this embodiment, for each customer to be recommended, when the customer to be recommended belongs to the individual account, the processing module 13 is based on the insurance storage corresponding to the customer to be recommended. Investment information, such fund investment information, such stock investment information, and such target redeemable forward contract investment information, using the customer classification model, classify the customer to be recommended as the recommended customer category or the non-recommended customer category The customer category is displayed on the display module 12 .

To sum up, in the system for screening potential purchasers of financial products of the present invention, the processing module 13 performs two-stage data pre-processing, and the customers belonging to the enterprise account or the individual account are sorted according to the corresponding total Historical asset data and these unfavorable conditions, the high spending power customer was screened out in the first stage, and the customer to be recommended was screened out in the second stage, after two stages of data preprocessing, matched with the customer classification model ( AI artificial intelligence) processes the total historical asset data corresponding to the customers to be recommended. Compared with the traditional classification only with various trained supervised models (AI artificial intelligence), it can more accurately classify these customers. It is classified into recommended/not recommended customer categories to effectively avoid bad debts caused by recommending financial products to wrong customers, and to classify customers at any time in response to changes in the customer's industrial environment and revenue status, so as to obtain more accurate classification results , you can more effectively market financial products to the right customers based on the classification results. Therefore, the object of the present invention can surely be achieved.

However, the above are only examples of the present invention, and should not limit the scope of implementation of the present invention. Any simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the patent specification are still included in the scope of the present invention. within the scope of the invention patent.

1: Computer device 11: Storage Module 12: Display module 13: Processing modules 51~53: Steps 511~512: Substeps 511A~511D: Substeps

Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, wherein: FIG. 1 is a block diagram illustrating an embodiment of the screening system for potential purchasers of financial products according to the present invention; FIG. 2 is a flow chart illustrating a method for screening potential purchasers of financial products implemented in this embodiment; Fig. 3 is a flow chart illustrating the detailed process of how the screening method for potential purchasers of the financial product determines whether a customer is a high spending power customer; and FIG. 4 is a flow chart illustrating a detailed process of how the method for screening potential purchasers of financial products obtains financial return information.

51~53: Steps

Claims (12)

A method for screening potential purchasers of financial products, implemented by a computer device, the computer device stores a plurality of pieces of credit ability data related to the credit of a plurality of customers, and a plurality of pieces of credit ability data related to the customers at a previous point in time. Aggregate historical asset information on the financial condition of a prior period defined by a current point in time, and a number of adverse conditions related to various industries and economic conditions of customers, each customer belonging to one of a business household and a personal household , the total historical asset data of each customer belonging to the enterprise account includes multiple gross profit margins corresponding to the customer at different time points in the previous period, Multiple operating incomes, multiple collection days corresponding to the customer at these different time points in the previous period, and multiple recession ratios corresponding to the customer’s property at these different time points in the previous period , and the multiple revenue-to-profit ratios of the customer at the different time points in the previous period, the unfavorable conditions include multiple corresponding to the business account, and are respectively associated with a preset collection date, A predetermined recession ratio, and a predetermined revenue-to-profit ratio related to the unfavorable conditions of business customers, the screening method of potential purchasers of the financial product includes the following steps: (A) for each customer, through the computer The device determines whether the customer is a high spending power customer according to the credit ability data and the total historical asset data corresponding to the customer, wherein step (A) includes the following step (A-1) for each customer, borrowing Obtain, by the computer device, according to the total historical asset data corresponding to the customer, a data indicating the financial increase or decrease of the customer in a first time interval in the previous period Financial income information, wherein, step (A-1) includes the following steps, (A-1-1) For each customer, when the customer belongs to the enterprise, through the computer device, according to the customer corresponding to the Total historical asset information, to obtain a gross profit ratio and an operating income ratio, the gross profit ratio is the average of all gross profit margins of the customer in the first time interval in the previous period divided by the industry corresponding to the customer The average of all gross profit margins of all enterprises other than the customer in the first time interval, and the operating income ratio is the operating income of the customer at the last time point in the first time interval in the previous period divided by Take the operating income corresponding to the first time point in the first time interval, and (A-1-2) use the computer device to use the gross profit ratio and the operating income ratio as the financial income information, and (A-2) For each customer, use the computer device to determine whether the customer is the high spending power customer according to the credit capability data and the financial income information corresponding to the customer, wherein, for each customer, When the customer belongs to the enterprise, use the computer device to determine whether the customer belonging to the enterprise is the high spending power according to the credit capability data, the gross profit ratio and the operating income ratio corresponding to the customer customer; (B) for each high-spending power customer, determine whether the high-spending power customer is a customer to be recommended by the computer device, according to the total historical asset data and the disadvantageous conditions corresponding to the high-spending power customer customers, of which, For each high-spending customer, when the high-spending customer belongs to the business account, the computer device determines all the collection days, all the recession rates in a second time interval in the previous period by determining and whether any one of all revenue-to-profit ratios satisfies one of the unfavorable conditions of the business owners to determine whether the high-spending customer is the customer to be referred; and (C) for each customer to be referred , by using the computer device, according to the total historical asset data corresponding to the customer to be recommended, and using a customer classification model for classifying the customer as a recommended customer or a non-recommended customer, the customer to be recommended is classified as a recommended customer One of a customer category and a non-recommended customer category. According to the method for screening potential purchasers of financial products as described in claim 1, each customer belongs to one of a corporate account and an individual account, and the total historical asset information of each customer belonging to the individual account includes the Multiple sums of movable property corresponding to multiple different time points in the previous period, and multiple personal income corresponding to multiple different time points of the customer in the previous period, wherein step (A-1) includes the following steps: ( A-1-1) For each customer, when the customer belongs to the individual account, the computer device obtains an available movable property amount and a personal income ratio according to the total historical asset data corresponding to the customer, and the available movable property amount is is the average of all movable properties of the client in the first time interval in the previous period, and the personal income ratio is the last time point in the first time interval in the previous period corresponding to the client Individual income divided by the individual corresponding to the first time point in the first time interval income; (A-1-2) by the computer device, use the available chattel amount and the personal income ratio as the financial income information; and in step (A-2), for each customer, when the customer belongs to In the case of the individual account, the computer device is used to determine whether the customer belonging to the individual account is the high spending power customer according to the credit capability data, the available movable property amount and the personal income ratio corresponding to the customer. According to the method for screening potential purchasers of financial products as described in claim 1, each customer belongs to one of a corporate account and an individual account, and the total historical asset information of each customer belonging to the individual account includes the The ratio of multiple high-risk investments of the high-risk financial products purchased to all the financial products purchased at multiple different time points in the previous period, and the multiple assets corresponding to the customer at multiple different time points in the previous period The total amount, the disadvantageous conditions include a plurality of disadvantageous conditions for the individual account corresponding to the individual account and respectively related to a preset high-risk investment ratio and a preset capital ratio, wherein the step (B) includes the following steps: ( B-1) For each high-spending customer, when the high-spending customer belongs to the individual account, the computer device divides the average of the total amount of funds in a third time interval in the previous period by the high-spending (B-2) For each high spending power customer, when the high spending power customer belongs to the individual account , the computer device is determined by determining that during the previous period Whether any one of all the high-risk investment ratios and the capital ratio in a fifth time interval meets one of the unfavorable conditions of the individual account, so as to determine whether the high-spending power customer is the customer to be recommended. According to the method for screening potential purchasers of financial products as described in claim 1, each customer belongs to one of a corporate account and a personal account, and the total historical asset data of each customer belonging to the corporate account includes the Multiple pieces of revenue information corresponding to multiple different time points in the previous period, and multiple pieces of pre-tax profit and loss corresponding to the customer at multiple different time points in the previous period, wherein, in step (C), for each 1. A customer to be recommended, when the customer to be recommended belongs to the enterprise, the computer device uses the customer classification model to classify the customer to be recommended according to the corresponding revenue information and the pre-tax profit and loss of the customer to be recommended. The class is the recommended customer category or the non-recommended customer category. According to the method for screening potential purchasers of financial products as described in claim 1, each customer belongs to one of a corporate account and an individual account, and the total historical asset information of each customer belonging to the individual account includes the Multiple low-risk investment information corresponding to multiple different time points in the previous period, and multiple high-risk investment information corresponding to the client at multiple different time points in the previous period, wherein, in step (C), For each client to be recommended, when the client to be recommended belongs to the individual account, the computer device uses the client classification model to classify the low-risk investment information and the high-risk investment information corresponding to the client to be recommended. The customer to be referred is classified into the category of the recommended customer or the Customer categories are not recommended. According to the method for screening potential purchasers of financial products according to claim 5, the low-risk investment information includes multiple savings insurance investment information corresponding to the client at multiple different time points in the previous period, and the client’s investment information in the Multiple fund investment information corresponding to multiple different time points in the previous period, such high-risk investment information includes the client’s multiple target redeemable forward contract investment information corresponding to multiple different time points in the previous period , wherein, in step (C), for each client to be recommended, when the client to be recommended belongs to the individual account, the computer device is based on the information of the insurance investment, the investment of funds corresponding to the client to be recommended Information and such target redeemable forward contract investment information, using the customer classification model, to classify the customer to be referred as the recommended customer category or the non-recommended customer category. A system for screening potential purchasers of financial products, comprising: a storage module, storing a plurality of pieces of credit ability data related to the credit of a plurality of customers, and a plurality of pieces of credit ability data related to the customers from a previous time point to a current time Aggregate historical asset information on the financial condition of a prior period defined by the point, and a number of adverse conditions related to the economic conditions of various industries and customers, where each customer belongs to one of a business household and a personal household, each The total historical asset information of a customer belonging to the enterprise account includes multiple gross profit margins for the customer at different time points in the previous period, and multiple data for the customer at different time points in the previous period. Operating income, multiple collection days for the customer at those different points in time in the prior period, multiple recession rates for the property for the customer at those different points in the prior period, and the customer in the The multiple revenue-to-profit ratios corresponding to the different time points in the previous period, the unfavorable conditions include multiple corresponding to the enterprise account, and are respectively related to a preset collection date and a preset recession ratio , and a business owner disadvantage related to a preset revenue and profit ratio; and a processing module, which is electrically connected to the storage module; wherein, for each customer, the processing module is based on the credit corresponding to the customer. Ability data and the total historical asset data to determine whether the customer is a high spending power customer, for each high spending power customer, the processing module is based on the total historical asset data corresponding to the high spending power customer and the disadvantages condition, to determine whether the high spending power customer is a customer to be recommended. For each customer to be recommended, the processing module uses a method for classifying the customer as a recommended customer according to the total historical asset data corresponding to the customer to be recommended. or a customer classification model for non-recommended customers, classifying the customer to be recommended as one of a recommended customer category and a non-recommended customer category, wherein, for each customer, the processing module Total historical asset data, obtain a financial income information indicating the financial increase or decrease of the customer in a first time interval in the previous period, for each customer, the processing module is based on the credit capability data corresponding to the customer and the financial income information to determine whether the customer is the high spending power customer, wherein, for each customer, when the customer belongs to the enterprise, the processing module obtains the total historical asset data corresponding to the customer A gross profit ratio and an operating income ratio, the gross profit ratio being the average of all gross profit margins of the customer in the first time interval in the preceding period divided by all businesses in the industry corresponding to the customer other than the customer within the first time interval The average of all gross profit margins, the operating income ratio is the operating income for the customer at the last time point in the first time interval in the previous period divided by the first time point in the first time interval The corresponding operating income, the processing module uses the gross profit ratio and the operating income ratio as the financial income information. For each customer, when the customer belongs to the enterprise, the processing module The credit capability data, the gross profit ratio and the operating income ratio determine whether the customer belonging to the enterprise is the high spending power customer, wherein, for each high spending power customer, when the high spending power customer belongs to the enterprise account, the processing module determines whether any of all collection days, all recession ratios, and all revenue-to-profit ratios within a second time interval in the previous period meet these One of the unfavorable conditions of the business owner to determine whether the customer with high spending power is the customer to be recommended. The system for screening potential purchasers of financial products according to claim 7, wherein each customer belongs to one of a corporate account and an individual account, and the total historical asset data of each customer belonging to the individual account includes the customer Multiple totals of movable property corresponding to multiple different time points in the previous period, and multiple personal income corresponding to multiple different time points of the customer in the previous period, for each customer, when the customer belongs to the individual When the account is opened, the processing module obtains an available chattel amount and a personal income ratio according to the total historical asset data corresponding to the client, and the available chattel amount is the amount of the client in the first time interval in the previous period. The average of all movable assets, the personal income ratio is the personal income of the client at the last time point in the first time interval in the preceding period divided by the Using the personal income corresponding to the first time point in the first time interval, the processing module uses the available movable property amount and the personal income ratio as the financial income information. For each customer, when the customer belongs to the individual When the account is established, the processing module determines whether the client belonging to the individual account is the high spending power client according to the credit capability data corresponding to the client, the amount of movable property available and the personal income ratio. The system for screening potential purchasers of financial products according to claim 7, wherein each customer belongs to one of a corporate account and an individual account, and the total historical asset data of each customer belonging to the individual account includes the customer The multiple high-risk investment ratios of the high-risk financial products purchased to all the financial products purchased at various time points in the previous period, and the corresponding multiple high-risk investment ratios of the customer at various time points in the previous period The total amount of capital, these adverse conditions include a plurality of individual account disadvantages corresponding to the individual account, and are respectively related to a preset high-risk investment ratio and a preset capital ratio. For each customer with high spending power, when When the high spending power customer belongs to the individual account, the processing module divides the average of the total amount of funds in a third time interval in the previous period by the high spending power customer in a fourth time in the previous period The average of the total capital in the interval to obtain an capital ratio, for each high-spending customer, when the high-spending customer belongs to the individual account, the processing module is determined by determining one of the first in the previous period. Whether any one of the high-risk investment ratio and the capital ratio in the five time intervals meets one of the unfavorable conditions of the individual account, so as to determine whether the customer with high spending power is the customer to be recommended. The screening system for potential purchasers of financial products according to claim 7, wherein Among them, each customer belongs to one of a corporate account and a personal account, and the total historical asset data of each customer belonging to the corporate account includes the customer's multiple revenue at different points in the previous period. information, and multiple pre-tax profits and losses corresponding to the client at multiple different time points in the previous period. For each client to be recommended, when the client to be recommended belongs to the enterprise, the processing module is based on the client to be recommended. The customer's corresponding revenue information and the pre-tax profit and loss, using the customer classification model, classify the customer to be recommended as the recommended customer category or the non-recommended customer category. The system for screening potential purchasers of financial products according to claim 7, wherein each customer belongs to one of a corporate account and an individual account, and the total historical asset data of each customer belonging to the individual account includes the customer Information on multiple low-risk investments corresponding to multiple different time points in the previous period, and multiple high-risk investment information corresponding to the client at multiple different time points in the previous period, for each client to be recommended, When the customer to be recommended belongs to the individual account, the processing module uses the customer classification model to classify the customer to be recommended according to the low-risk investment information and the high-risk investment information corresponding to the customer to be recommended For the recommended customer category or the non-recommended customer category. The system for screening potential purchasers of financial products according to claim 11, wherein the low-risk investment information includes multiple savings insurance investment information corresponding to the customer at multiple different time points in the previous period, and the Information on multiple fund investments of the client at different time points in the previous period The high-risk investment information includes multiple target redeemable forward contracts corresponding to the client at multiple different time points in the previous period investment capital For each client to be recommended, when the client to be recommended belongs to the individual account, the processing module is based on the insurance investment information, the fund investment information and the target redemption information corresponding to the client to be recommended. Return the forward contract investment information, and use the customer classification model to classify the customer to be recommended as the recommended customer category or the non-recommended customer category.

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