TW202025040A - Corporate customer smart target system and method - Google Patents

Abstract

This disclosure discloses a corporate customer intelligent exploration method, comprising: obtaining a customer data with a plurality of financial characteristics from a memory by a calculator, and generating an initial score according to the financial characteristics, a plurality of initial conditions and a plurality of initial weighing; generating a summary score according to the initial scores, a summary condition and a summary weighting by the calculator, and comparing the summary score with at least one rating standard for obtaining a rating result; and sending the summary score to the memory and sending the rating result to a display interface by the calculator for showing the rating result.

Description

Intelligent exploration system and method for enterprise financial customers

The present invention relates to a smart exploration system and method based on big data, in particular to a smart exploration system and method applied to financial customers.

In the era of big data, in order to efficiently develop potential customers, many companies use data analysis methods to initially find suitable customer lists, and classify them according to different analysis results to help companies provide services for customers of different natures .

However, in the financial industry, because information changes rapidly and the needs of enterprises can change at any time, the detailed processes of data analysis often need to be updated and maintained manually to reflect the needs of enterprises in a timely manner. However, artificially updated data analysis methods often require multiple trial calculations and corrections before they can be used. The update speed often cannot keep up with the changes in the financial industry’s environment in real time, and the time and cost it takes are for companies. It is also an additional burden.

Therefore, there is still a need for a smart exploration system and method for corporate finance customers to improve the above problems.

The present invention is to provide a smart prospecting system and method for enterprise financial customers, which can self-correct the detailed flow of the analysis method according to the difference between the analysis result and actual conditions, so as to be applied in the financial industry.

The present invention is related to a smart exploration method for corporate finance customers, which includes: obtaining a customer data containing multiple financial characteristics from a memory by an arithmetic unit, and using the arithmetic unit to obtain data of a customer based on the financial characteristics, a plurality of initial screening conditions, and Multiple initial weights to generate an initial score; the calculator generates a comprehensive score according to the initial score, a comprehensive screening condition, and a comprehensive weight, and compares the comprehensive score and at least one ranking standard to obtain a ranking Result; and using the arithmetic unit to send the comprehensive score to the memory and send the rating result to a display interface for the display interface to present the rating result; wherein each of the financial characteristics is associated with A plurality of the initial screening conditions, and the comprehensive screening condition is related to a plurality of the initial screening conditions, and the initial screening conditions related to the comprehensive screening condition are related to different financial feature.

A smart exploration system for enterprise financial customers, including: a memory for storing a customer data containing multiple financial characteristics, multiple initial screening conditions, multiple initial weights, a comprehensive screening condition, a comprehensive weight, at least A rating range, a comprehensive score, a reference score, and a preset adjustment range; an arithmetic unit is electrically connected to the memory to obtain the customer data, the initial screening conditions, the initial weights, and the comprehensive The screening conditions, the comprehensive weighted amount, the at least one rating range, the comprehensive score, the reference score, and the preset adjustment range are used to generate according to the financial characteristics, the initial screening conditions, and the initial weighted amounts An initial score, and generating the comprehensive score according to the initial score, the comprehensive screening condition, and the comprehensive weighting amount and sending it to the memory, and comparing the comprehensive score and at least one rating range to generate a ranking result; and The display interface is electrically connected to the arithmetic unit, and is used for receiving the rating result sent by the arithmetic unit and presenting the rating result.

The present invention is to provide a smart prospecting system and method for enterprise financial customers, which can analyze multiple potential customers according to different financial characteristics, compare part of the analysis results with actual data, and automatically modify the analysis method according to the comparison results for application in finance industry.

The above description of the disclosure and the following description of the implementation manners are used to demonstrate and explain the spirit and principle of the present invention, and to provide a further explanation of the patent application scope of the present invention.

The detailed features and advantages of the present invention are described in detail in the following embodiments. The content is sufficient to enable anyone familiar with the relevant art to understand the technical content of the present invention and implement it accordingly, and according to the content disclosed in this specification, the scope of patent application and the drawings Anyone who is familiar with relevant skills can easily understand the purpose and advantages of the present invention. The following examples further illustrate the viewpoints of the present invention in detail, but do not limit the scope of the present invention by any viewpoint.

Please refer to Figure 1. Figure 1 is a structural diagram of the intelligent exploration system 1 for enterprise financial customers. As shown in FIG. 1, the enterprise financial customer intelligence exploration system 1 includes a memory 11, a computing unit 13 and a display interface 15. The memory 11 can be any kind of hard disk, or other device with storage function, used to store customer data containing multiple financial characteristics, multiple initial screening conditions, multiple initial weights, comprehensive screening conditions, comprehensive weights, At least one rating range, comprehensive score, reference score and preset adjustment range. The arithmetic unit 13 may be a central processor unit (CPU) or other devices with arithmetic functions, and is electrically connected to the memory 11 for calculating and analyzing customer data stored in the memory 11. In detail, the operator 13 can generate an initial score based on the initial screening conditions and the initial weights, and then generate a comprehensive score based on the initial scores, comprehensive screening conditions, and comprehensive weights, and can compare the comprehensive score with at least one The rating range is used to generate rating results for this customer profile as a reference for evaluating potential customers. In addition, when the difference value between the comprehensive score and the reference score falls within the preset adjustment range, the calculator 13 can selectively adjust the above-mentioned weights and filtering conditions according to the difference value, so that the difference between the comprehensive score and the reference score The values can approximate each other. The display interface 15 can be any display device that can be connected to the arithmetic unit 13, for example, a screen that is electrically connected to the arithmetic unit 13, or a flat-panel display that can communicate with the arithmetic unit 13 through wireless transmission. In the present invention, the display interface 15 is used to receive the rating result from the arithmetic unit 13 and present the rating result. To put it simply, the enterprise financial customer intelligence exploration system 1 disclosed in the present invention uses the computing unit 13 as its core to calculate and analyze customer data stored in the memory 11 to generate various analysis results and present them on the display interface 15. So that users can find potential customers more efficiently through the analysis results.

For a detailed description of the analysis method of enterprise financial customer smart exploration system 1, please refer to Figure 2. Fig. 2 is a flowchart of an embodiment of a method for intelligent exploration of corporate finance customers. First, please refer to step S11: use the calculator 13 to obtain customer data containing multiple financial characteristics from the memory 11, and generate an initial score based on the financial characteristics, multiple initial screening conditions, and multiple initial weights; where, The above-mentioned multiple initial screening conditions are associated with the financial characteristics, and respectively correspond to the initial weighted value, and the initial weighted amount is calculated through the initial weighted value. It should be noted that, according to different financial characteristics, the number of the initial screening conditions and the value of the initial weighting amount are also different. For example, the financial characteristic can be the revenue growth rate of the customer, and the initial screening criteria can be distinguished according to the value range of the revenue growth rate, and different initial weighting values can be set for different value ranges, and the revenue The actual value of the growth rate is multiplied by the initial weight to obtain the initial weight. After the arithmetic unit 13 generates the initial score, please refer to step S13: the arithmetic unit 13 generates a comprehensive score based on the initial score, comprehensive screening conditions, and comprehensive weight; the comprehensive score can be a comprehensive screening based on the multiple financial characteristics mentioned above The conditions are further weighted calculations based on the preliminary score of the customer data and the comprehensive weighted amount. For example, suppose that the financial characteristics of the customer data include revenue growth rate, operating profit rate, and pre-tax net profit rate, and a preliminary score is generated, and the initial weights of the above three financial characteristics are all positive numbers (representing the overall customer Revenue is positive growth), the calculator 13 performs a weighted calculation on the preliminary score to highlight the financial potential of the customer.

Please continue to refer to Figure 2. After the arithmetic unit 13 generates a comprehensive score, please refer to step S15: use the arithmetic unit 13 to compare the comprehensive score with the reference score and generate a difference value; the reference score can be a number of customers with long-term and stable contacts with the company, based on actual cooperation experience It is established so that the arithmetic unit 13 can compare the calculation result with actual experience based on the difference value. After the arithmetic unit 13 generates a difference value, please refer to step S17: when the difference value falls within the preset adjustment range, the arithmetic unit 13 selectively adjusts the initial weighting amounts and the comprehensive weighting amount according to the difference value, and The financial characteristics, the initial screening conditions, the comprehensive screening conditions, and the selectively adjusted initial weights and comprehensive weights produce an adjusted comprehensive score; in simple terms, when the difference value falls within the preset adjustment range, It means that there may be a certain degree of gap between the comprehensive score and the reference score. Therefore, the above-mentioned screening conditions or weighting amount need to be modified to make the calculation result closer to the actual situation. After the arithmetic unit 13 generates the adjusted comprehensive score, please refer to step S19: update the comprehensive score generated in step S13 with the adjusted comprehensive score; wherein the adjusted comprehensive score is determined by the arithmetic unit 13 based on the selective adjustment of the initial weight and the comprehensive weight produce. Continuing, please refer to step S21: Use the calculator 13 to compare the comprehensive score and at least one rating range to generate a rating result; where the rating range can be any statistical value that can distinguish multiple comprehensive ratings, such as all customers The average value of the comprehensive score of the data, or the previous standard, average standard, and base standard, etc., are not limited in the present invention. After the arithmetic unit 13 generates the rating result, please refer to step S23: use the arithmetic unit 13 to send the comprehensive score to the memory 11 and send the rating result to the display interface 15 for the display interface 15 to present the rating result; In the step, the rating results can be presented on the display interface 15 in the form of a list, and the comprehensive score can be stored in the memory 11 in the form of a list.

It should be noted that the above-mentioned steps S15 to S19 are achieved by adjusting each filter condition and each weighting amount, so that the comprehensive score can approach the reference score. Therefore, when the comprehensive score is very close to the reference score, this method can omit the above-mentioned steps S15 to S19, and only perform steps S13, S15, S21, and S23 to generate a comprehensive score and rating result, so as to reduce the time spent in calculation.

In addition, it should be added that each financial feature has a feature value or a preset value, where the feature value is related to the initial screening conditions of the financial feature, and only one of the initial screening conditions corresponds to At the characteristic value. For example, for the revenue growth rate, operating profit rate, and pre-tax net profit rate mentioned in the previous paragraph, the relevant information on financial characteristics is a data presented in the form of a percentage, and this data is the financial condition The characteristic value, and by the initial screening condition corresponding to the characteristic value, the initial weighting of the financial condition can be further calculated; this type of financial characteristic can be called "numerical financial characteristic". On the other hand, because some financial conditions data have a large range of differences (for example, debt ratio), or the information is presented in the form of ratings (for example, TCRI ratings), the financial conditions with the above characteristics are initially selected Conditions can be presented in the form of data range or rating range, and different data ranges or rating ranges can correspond to different initial weights to increase the integrity of this calculation method; in addition, this type of financial feature can also be called "Serial Financial Features."

需注意的是，營收成長率A、營業利益率B和稅前純益率C的初始加權量為高皆為15分，並且於本實施例中，初始評分的算法為所有金融特徵對應的初始加權量的總和。表1所示的金融特徵中，其中營收成長率A、營業利益率B和稅前純益率C 為前述的「數值型金融特徵」，負債比率D 與TCRI評等E 則為「級數型金融特徵」。具體來說，「數值型金融特徵」係根據該特徵值取得該數值型金融特徵的運算結果；舉例來說，假設營收成長率A為10%，其特徵值即為10%，則運算器13以該特徵值(10%)乘上初始加權值(15)，得到其初始加權量(1.5分)。另一方面，「級數型金融特徵」係根據預設值而取得該級數型金融特徵的運算結果；舉例來說，負債比率D與TCRI評等E的預設值皆為1，當負債比率D為150%，則運算器13以該預設值(1)乘上初始加權值(5)，得到其初始加權量(5分)。In order to specifically explain the calculation process of the smart prospecting method for enterprise financial customers, please refer to FIG. 3, which is a schematic diagram of the operation of the smart prospecting method for enterprise financial customers according to an embodiment of the present invention. A to E shown in Figure 3 are different financial characteristics, a1 to e3 are the initial screening conditions, aw1 to ew3 are the initial weights corresponding to each initial screening condition, IS1 and IS2 are the initial scores, sw1 and sw2 It is a comprehensive weight, and SS1 and SS2 are comprehensive scores. In this embodiment, the aforementioned financial characteristics include revenue growth rate A, operating profit rate B, pre-tax net profit rate C, debt ratio D, and TCRI rating E. The corresponding screening conditions and weights are as follows: Table 1

It should be noted that the initial weighted amounts of revenue growth rate A, operating profit rate B, and pre-tax net profit rate C are all 15 points, and in this embodiment, the initial scoring algorithm is the initial value corresponding to all financial characteristics. The sum of the weighted quantities. Among the financial characteristics shown in Table 1, revenue growth rate A, operating profit rate B, and pre-tax net profit rate C are the aforementioned "numerical financial characteristics", and debt ratio D and TCRI rating E are "series" Financial characteristics". Specifically, the "numerical financial feature" is based on the feature value to obtain the calculation result of the numerical financial feature; for example, if the revenue growth rate A is 10%, the feature value is 10%, then the calculator 13 Multiply the characteristic value (10%) by the initial weight value (15) to obtain the initial weight value (1.5 points). On the other hand, "series financial characteristic" is to obtain the calculation result of the series financial characteristic according to the preset value; for example, the default value of debt ratio D and TCRI rating E are both 1, when debt If the ratio D is 150%, the arithmetic unit 13 multiplies the initial weighting value (5) by the preset value (1) to obtain the initial weighting amount (5 points).

另一方面，假設次要目標客群為企業之營收為正成長且皆有獲利，自有資金充足負債比率低，並且TCRI信用評等屬中等，則第二組綜合篩選條件和綜合加權量可設定如表3。 表3

需注意的是，因表3所示的資料係針對次要目標客群(Group 2)而設計，故其綜合加權值可較主要目標客群(Group 1)的綜合加權值低（如表2所示）。To further illustrate the comprehensive screening conditions and comprehensive weighting amount, please continue to refer to Figure 3. In order to more effectively develop customers with financial potential, users can design different comprehensive screening conditions and comprehensive weights based on different initial screening conditions. For example, suppose that the primary target customer group is to lock the company’s overall revenue growth and profitability, its own capital sufficient debt ratio is low, and the TCRI credit rating is excellent, then the first set of comprehensive selection criteria And the comprehensive weight can be set as Table 2 Table 2

On the other hand, assuming that the secondary target customer group is that the company’s revenue is positive growth and both profitable, the self-funded debt ratio is low, and the TCRI credit rating is medium, then the second set of comprehensive selection criteria and comprehensive weighting The amount can be set as shown in Table 3. table 3

It should be noted that because the data shown in Table 3 is designed for the secondary target group (Group 2), its comprehensive weight can be lower than that of the main target group (Group 1) (see Table 2 Shown).

由表2可得知，根據各金融特徵所對應的初始加權量，甲客戶的初始評分IS1=3+3.75+1.5+5+10=23.25分，且甲客戶符合主要目標客群(Group 1)的綜合篩選條件，因此甲客戶的綜合評分SS1=23.25*150%=34.875分。 表5

由表3可得知，乙客戶的初始評分IS2=3+2.25+1.5+5+5=16.75分，且乙客戶符合次要目標客群(Group 2)的綜合篩選條件，因此乙客戶的綜合評分SS2=16.75*140%=23.45分。To illustrate the above list, this paragraph will take two customer data shown in Table 4 and Table 5 as examples. Table 4

It can be seen from Table 2 that according to the initial weighted amount corresponding to each financial characteristic, the initial score of customer A is IS1=3+3.75+1.5+5+10=23.25 points, and customer A meets the main target customer group (Group 1) Therefore, the comprehensive score of customer A is SS1=23.25*150%=34.875 points. table 5

It can be seen from Table 3 that the initial score of customer B is IS2=3+2.25+1.5+5+5=16.75 points, and customer B meets the comprehensive screening criteria of the secondary target customer group (Group 2), so the comprehensive Score SS2=16.75*140%=23.45 points.

In order to specifically illustrate the subsequent calculation process, in this embodiment, the aforementioned difference value is defined as: difference value = reference score-comprehensive score. Assuming that the reference score of customer A is 40 points, the reference score of customer B is 30 points, and the preset adjustment range is that the difference value is greater than 5 points (not including 5 points), so it can be learned that the difference value of customer A is 5.125, and customer B The customer difference value is 6.55; because the difference values of the two fall within the preset adjustment range, the calculator 13 will selectively adjust the above-mentioned initial weighting amount and comprehensive weighting amount, recalculating and generating an adjusted comprehensive score, Replace the original comprehensive score. For example, in this embodiment, the comprehensive scores of the two customer data are lower than the reference scores, so the calculator 13 can increase the initial weighted value of the revenue growth rate A (ie, aw1 in Figure 3), or both The comprehensive weighted values of Group 1 and Group 2 (ie sw1 and sw2 in Figure 3) are used to generate a new comprehensive score and compare it with the reference score again to gradually make the comprehensive score approach the reference score. On the other hand, suppose that customer A is a customer with stable contacts and good cooperation records, because its reference score may be more indicative than that of customer B, so the calculator 13 can first adjust the initial weighting amount and comprehensive Weighted amount. After the comprehensive score of customer A approaches the reference score, the adjusted initial weighted amount and comprehensive weighted amount are used to calculate the data of customer B to make the entire calculation process more efficient.

It is worth mentioning that the aforementioned financial characteristics can also include account receivable turnover rate, quick ratio, cash flow ratio, net value ratio, company establishment period, industry boom trends (weather conditions) or other special conditions (such as progress Index data such as the list of export high-performance manufacturers, government-supported industries, credit insurance fund guarantees, high-quality public works manufacturers or award-winning start-ups, etc.). More screening conditions and weights can be set, with more diversified Calculate the conditions to find out a list of potential customers of different natures.

In summary, the present invention is to provide a smart prospecting system and method for enterprise financial customers, which can analyze multiple potential customers according to different financial characteristics, compare part of the analysis results with actual data, and automatically modify the analysis method based on the comparison results , So that it can be used in the financial industry with a changing environment.

Although the present invention is disclosed in the foregoing embodiments, it is not intended to limit the present invention. All changes and modifications made without departing from the spirit and scope of the present invention fall within the scope of patent protection of the present invention. For the scope of protection defined by the present invention, please refer to the attached patent scope.

1: Enterprise financial customer intelligent exploration system 11: Memory 13: Operator 15: display interface A: Revenue growth rate B: Operating profit ratio C: Net profit before tax D: debt ratio E: TCRI rating a1, a2, b1, b2, c1, c2, d1, d2, d3, e1, e2, e3: initial filter conditions aw1, aw2, bw1, bw2, cw1, cw2, dw1, dw2, dw3, ew1, ew2, ew3: initial weight IS1, IS2: Initial score Group 1: Main target customer group Group 2: secondary target group SS1, SS2: Overall score sw1, sw2: comprehensive weighted value

Fig. 1 is a structural diagram of an enterprise-finance customer intelligent exploration system according to an embodiment of the present invention. Fig. 2 is a flowchart of an embodiment of a method for intelligent exploration of corporate finance customers. FIG. 3 is a schematic diagram of the operation of a method for intelligent exploration of enterprise financial customers according to an embodiment of the present invention.

Claims (10)

A smart exploration method for corporate finance customers, comprising: obtaining a customer data containing multiple financial characteristics from a memory by an arithmetic unit, and using the arithmetic unit to obtain a plurality of financial characteristics, a plurality of initial screening conditions, and a plurality of initial weights Generate an initial score; use the calculator to generate a comprehensive score based on the initial score, a comprehensive screening condition, and a comprehensive weight; use the calculator to compare the comprehensive score and at least one rating range to generate a ranking result And using the calculator to send the comprehensive score to the memory and send the rating result to a display interface for the display interface to present the rating result; wherein each of the financial characteristics is associated with the Many of the initial screening conditions, and the comprehensive screening condition is related to multiple of the initial screening conditions, and the initial screening conditions related to the comprehensive screening condition are related to different financial characteristics. The exploration method according to claim 1, wherein after generating the comprehensive score by the arithmetic unit and before comparing the comprehensive score and the at least one rating range by the arithmetic unit, it further comprises: comparing the comprehensive score by the arithmetic unit The comprehensive score and a reference score generate a difference value; when the difference value falls within a preset adjustment range, the arithmetic unit is used to selectively adjust the initial weighting amounts and the comprehensive weighting amount according to the difference value. The financial characteristics, the initial screening conditions, the comprehensive screening conditions, and the selectively adjusted initial weights and comprehensive weights generate an adjusted comprehensive score; and update the comprehensive score with the adjusted comprehensive score to provide The at least one rating range is compared to produce the rating result. The exploration method according to claim 1, wherein each of the financial characteristics has a characteristic value, which is associated with the initial screening conditions of the financial characteristic, and only one of the initial screening conditions corresponds to the characteristic value , There is only one of the initial weighted values belonging to the initial screening condition corresponding to the feature value, and generating the initial score by the operator includes: multiplying the feature value or a preset value of each of the financial features by The initial weighted value belonging to the initial screening condition corresponding to the feature value to obtain a plurality of calculation results, wherein the number of the calculation results is equal to the number of the financial characteristics; and the calculation results are summed to generate the initial score . The exploration method according to claim 3, wherein the financial characteristics include a numerical financial characteristic, and the calculator obtains the calculation result of the numerical financial characteristic according to the characteristic value of the numerical financial characteristic. The exploration method according to claim 3, wherein the financial features include a first-level financial feature, and the arithmetic unit obtains the calculation result of the first-level financial feature according to the preset value. A smart exploration system for enterprise financial customers, including: a memory for storing a customer data containing multiple financial characteristics, multiple initial screening conditions, multiple initial weights, a comprehensive screening condition, a comprehensive weight, at least A rating range, a comprehensive score, a reference score, and a preset adjustment range; an arithmetic unit is electrically connected to the memory to obtain the customer data, the initial screening conditions, the initial weights, and the comprehensive The screening conditions, the comprehensive weighted amount, the at least one rating range, the comprehensive score, the reference score, and the preset adjustment range are used to generate according to the financial characteristics, the initial screening conditions, and the initial weighted amounts An initial score, and generating the comprehensive score according to the initial score, the comprehensive screening condition, and the comprehensive weighting amount and sending it to the memory, and comparing the comprehensive score and at least one rating range to generate a ranking result; and The display interface is electrically connected to the arithmetic unit, and is used for receiving the rating result sent by the arithmetic unit and presenting the rating result. The surveying system according to claim 6, wherein the arithmetic unit further includes a reference score, and the arithmetic unit generates a difference value by comparing the reference score and the comprehensive score, and when the difference value falls within a preset In the adjustment range, the calculator selectively adjusts the initial weights and the comprehensive weights according to the difference value, and according to the financial characteristics, the initial screening conditions, the comprehensive screening conditions, and the selective adjustments The initial weighted amount and the comprehensive weighted amount produce an adjusted comprehensive score. The exploration system according to claim 6, wherein each of the financial characteristics further includes a characteristic value, the characteristic value is associated with the initial screening conditions of the financial characteristic, and only one of the initial screening conditions corresponds to For the characteristic value, only one of the initial weighting values belongs to the initial filtering condition corresponding to the characteristic value, and the operator obtains multiple operation results according to the characteristic value or a preset value, and generates The initial score. The exploration system according to claim 8, wherein the financial characteristics further include a numerical financial characteristic, and the arithmetic unit obtains the calculation result of the numerical financial characteristic according to the characteristic value of the numerical financial characteristic. The exploration system according to claim 8, wherein the financial features further include a first-level digital financial feature, and the arithmetic unit obtains the calculation result of the numerical financial feature according to the preset value.

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