TWI795809B - Business evaluation system and method therefore - Google Patents

Abstract

The disclosure provides a business evaluation system. The business evaluation system includes a server, an input interface, a business evaluation computer. The server stores a plurality of evaluation factors, the evaluation factors are respectively corresponds to a plurality of weight values. The input interface communication connect to the server, and the input interface is configured to receive a plurality pieces of operation data, the pieces of operation data respectively correspond to the evaluation factors. The business evaluation computer communication connect to the server, the business evaluation computer stores a machine learning module. The machine learning module calculates the score of the business evaluation project according to the pieces of operation data and the weight values corresponding to the evaluation factors.

Description

Business Evaluation System and Its Method

The content of this case is about a business evaluation system, especially a business evaluation system with machine learning.

When financial institutions launch new businesses or services, if they can accurately assess the type of business and the timing of the launch, it will promote the operation and management of financial institutions and increase business. How to provide the best service category and time point is an important issue in this field.

This disclosure provides a business evaluation system. The business evaluation system includes a server, an input interface and a business evaluation host. The server stores a plurality of evaluation factors of the service evaluation item, wherein the evaluation factors respectively correspond to a plurality of weights. The input interface is communicatively connected to the server, and the input interface is used to receive multiple pieces of operation data, wherein the pieces of operation data respectively correspond to the evaluation factors. The business evaluation host is connected to the server by communication, and the business evaluation host stores a machine learning model, and the machine learning model calculates the business evaluation item based on the operation data corresponding to the evaluation factors and the weights corresponding to the evaluation factors , and this type of machine learning model is further used to calculate the first rate of change based on one of the operating data corresponding to one of the evaluation factors and a piece of historical operating data, and based on the score and the historical score A second rate of change is calculated, and the machine learning model adjusts the weights according to a difference between the first rate of change and the second rate of change.

This disclosure provides a business valuation methodology. The business valuation methodology consists of the following steps. A plurality of pieces of operation data are received through the input interface, wherein the pieces of operation data respectively correspond to a plurality of evaluation factors of the business evaluation item. A plurality of weights respectively corresponding to the evaluation factors are obtained from the server by the business evaluation host. The machine learning model is executed by the business evaluation host. A score of the business evaluation item is calculated by a machine learning model according to the pieces of operation data corresponding to the evaluation factors and the weights corresponding to the evaluation factors. A machine learning model is used to calculate the first rate of change based on one piece of operating data and a piece of historical operating data corresponding to one of the evaluation factors, and calculate the second rate of change based on the score and the historical score. The machine learning model adjusts the weights according to the difference between the first rate of change and the second rate of change.

To sum up, the business evaluation system in this disclosure document can calculate the scores of credible business evaluation items, which can be used as business management as the direction of future profit strategy and layout of bank branches.

The following is a detailed description of the embodiment in conjunction with the accompanying drawings to better understand the aspect of the case, but the provided embodiment is not used to limit the scope of the case, and the description of the structure and operation is not used to limit it. The sequence of execution, any structure recombined by components to produce devices with equivalent functions are all within the scope of this case.

In addition, the words "comprising", "including", "having", "containing", etc. used in this article are all open terms, meaning "including but not limited to". In addition, "and/or" used herein includes any one and all combinations of one or more items in the relevant listed items.

Please refer to FIG. 1 . FIG. 1 is a business evaluation system 100 according to an embodiment of the present disclosure. As shown in FIG. 1 , the business evaluation system 100 includes a business evaluation host 110 , an input interface 120 , and a server 130 . The server 130 stores a plurality of business items. Business items can be adjusted according to the type of financial institution to be evaluated. For example, the business project takes enterprise credit as an example. The analysis and evaluation of enterprise finance credit can include multiple analysis categories. For example, the analysis categories of enterprise finance credit include the overall business execution situation, business-specific customer financing situation, and operation management situation. Each analysis category can include multiple evaluation factors. For example, the overall business execution situation includes five evaluation factors, which are the average total financing balance, the average total amount, the average total number of accounts, the average total income, and the proportion of total income in the whole bank. than, but not limited to. Different evaluation factors have corresponding weights. The sum of the weights of all evaluation factors of the same analysis category is the weight of the analysis category. Distinct analysis categories can have distinct weights. The sum of weights for all analysis categories can be 100%. The above weights can be stored in the server.

The input interface 120 is communicatively connected with the business evaluation host 110 through a wired or wireless network, and the electronic device S and the input interface 120 can communicate with the business evaluation host 110 . The electronic device S can be realized by a personal computer, a mobile communication device or a tablet computer. The user can input the operation data associated with the business item into the business evaluation host 110 through the electronic device S and the input interface 120 .

The business evaluation host 110 includes a processor 112 , an internal memory 114 and an external memory 116 . The internal memory 114 is used to store the machine learning model 114a, and the external memory 116 is used to store operation data. The machine learning model 114a includes neural network-like, psycho-linguistic programming, fuzzy logic model, hidden Markov model, decision tree, Bayesian algorithm, conditional random field or support vector machine.

The processor 112 is electrically coupled to the internal memory 114 and the external memory 116 . The processor 112 communicates with the input interface 120 and the server 130 through a wired or wireless network.

Please also refer to FIG. 2 . FIG. 2 is a flowchart of a business evaluation method S100 implemented according to the present disclosure. The business evaluation method S100 includes steps S110-S130. In step S110, a plurality of pieces of operation data are received through the input interface, and the operation data respectively correspond to a plurality of evaluation factors. In step S120, a plurality of weights respectively corresponding to the evaluation factors are obtained by the service evaluation host. In step S130, the business evaluation host executes the machine learning model to adjust the weights, and calculates the adjusted score of the business evaluation item according to the adjusted weights.

For a better understanding of this disclosed document, please also refer to the following Table 1. In step S110, a plurality of pieces of operation data D1-D5 are received through the input interface 120, and the operation data D1-D5 respectively correspond to a plurality of evaluation factors F11-F15. In some embodiments, the input interface 120 also receives a plurality of pieces of operation data D6-D10, and these operation data D6-D10 also correspond to a plurality of evaluation factors F11-F15. In some other embodiments, the operation data D6-D10 have been stored in the external memory 116, and the processor 112 can retrieve the operation data D6-D10 from the external memory 116. Moreover, in step S120 , a plurality of weights respectively corresponding to the evaluation factors F11 - F15 are obtained by the business evaluation host 110 . Overall business performance evaluation factors Average Total Financing Balance (F11) Average Total Quota (F12) Average total number of households (F13) Average Total Return (F14) Ratio of total revenue to the whole bank (F15) Weights(%) 4 4 4 4 4 January 2018 (unit: ten thousand yuan) current month average current month average current month average current month average current month average 75 (D1) 156 (D6) 83 (D2) 114 (D7) 92 (D3) 94 (D8) 101 (D4) 158 (D9) 97 (D5) 109 (D10) Percentage of the month to the average 75/156=0.4807 83/114=0.7280 92/94=0.9787 101/158=0.6392 97/109=0.8899 Score 0.4807*4 =1.92 points 0.7280*4 =2.91 points 0.9787*4 =3.91 points 0.6392*4 =2.56 points 0.8899*4 =3.56 points Table I

As shown in Table 1, the evaluation factors F11~F15 covered by the overall business execution situation are the average total financing balance, the average total amount, the average total number of accounts, the average total income, and the proportion of total income to the whole bank.

In some embodiments, the average total financing balance (assessment factor F11 ) represents the ratio of the total financing balance of the month to be evaluated (for example, the current month) to the average total financing balance of the past five years. The average total amount (assessment factor F12) represents the ratio of the total amount of the month to be evaluated (for example, the current month) to the average total amount of the past five years. The average total number of households (assessment factor F13) represents the ratio of the total number of households in the month to be evaluated (for example, the current month) to the average total number of households in the past five years. The average total income (assessment factor F14) represents the ratio of the total income of the month to be evaluated (for example, the current month) to the average total income of the past five years. The proportion of total income in the whole bank (assessment factor F15) represents the proportion of the total income in the month to be evaluated (for example, the current month) to the average total income of the past five years.

It should be noted that the operation data D1~D5 are the data of the month to be evaluated (for example, the current month), and the operation data D6~D10 can be the numerical average of multi-year data in the same month as the operation data D1~D5. In some embodiments, the operating data D6-D10 may be the numerical average of data in the same month as the operating data D1-D5 within the past five years. For example, the operation data D1~D5 are the operation data in January 2010, and the operation data D6~D10 are the operation data in January 104, January 105, January 106, January 107, and January 108 The numerical average of the data.

In some embodiments, the evaluation factors F11-F15 each have a weight of 4%, and the overall business execution situation of one of the three major categories of business evaluation items adds up the respective weights of the evaluation factors F11-F15, and the overall business execution situation will have a weight of 20%. the weight of.

In step S120 , a plurality of weights respectively corresponding to the evaluation factors F11 - F15 are obtained by the service evaluation host 110 . For example, the service evaluation host 110 obtains multiple weights respectively corresponding to the evaluation factors F11-F15 from the server 130. For example, the multiple weights of the evaluation factors F11-F15 are all 4%, as shown in Table 1.

In step S130, the business evaluation host 110 executes the machine learning model 114a to adjust the weights, and calculates the adjusted scores of the business evaluation items according to the adjusted weights.

For better understanding of step S130 , please also refer to FIG. 3 . FIG. 3 is a flow chart of step S130 in the business evaluation method in FIG. 2 according to an embodiment of the present disclosure. Step S130 includes steps S132-S138. In step S132, a machine learning model is used to calculate the scores of the business evaluation items according to the pieces of operating data and the weights. In step S134, the machine learning model is used to calculate the first change rate according to one piece of operation data and one piece of historical operation data among the pieces of operation data, and to calculate the second change rate according to the score of the business evaluation item and a historical score. In step S134, it is determined whether the difference between the first rate of change and the second rate of change is greater than an expected value. In step S137 , the machine learning model is used to preferentially adjust the weight with the highest value and the weight with the lowest value, so that the difference between the first rate of change and the second rate of change is smaller than the expected value. In step S138, these weights are not adjusted.

In step S132 , the scores of the business evaluation items are calculated by using the machine learning model according to the corresponding operation data D1 - D10 and weights of the evaluation factors F11 - F15 . Taking the evaluation factor F11 (average total financing balance) as an example, the operating data D1 of the total financing balance in January 2018 is 750,000, and the operating data D6 of the average total financing balance in January of each year from 2014 to 2010 is 1.56 million. The proportion of the total financing balance in January 2018 to the average total financing balance in January of each year from 2014 to 2010 is the operating data D1 divided by the operating data D6=75/156=0.4807, and the evaluation factor F11 (average total financing balance) It has a weight of 4%, so the score of the evaluation factor F11 (average total financing balance) in January 2018 is 0.4807*4=1.92 points.

To give another example, take the evaluation factor F12 (average total quota) as an example, the operating data D2 of the total quota in January 2018 is 830,000, and the average operating data D7 of the total quota in January of each year from 2014 to 2010 is 1.14 million . The proportion of the total quota in January 2018 to the average total quota in January of each year from 2010 to 2018 is the operating data D2 divided by the operating data D7=83/114=0.7280, and the evaluation factor F12 (average total quota) has 4% Therefore, the score of the evaluation factor F12 (average total amount) in January 2018 is 0.7280*4=2.91 points. By analogy, in January 2018, the scores of assessment factor F13 (average total number of households), assessment factor F14 (average total income), and assessment factor F15 (proportion of total income to the entire bank) were 3.91 points and 2.56 points and 3.56 points.

It should be noted that when the average proportion of the current month exceeds the set value (eg, 1), the calculation will be performed with the set value (eg, 1).

In this way, when the highest score of the overall business execution situation is 20 points (that is, with 20% weight), according to the respective scores of evaluation factors F11~F15, the machine learning model 114a can calculate the overall business execution situation in January 2018 The score is 1.92+2.91+3.91+2.56+3.56=14.86 points.

In some embodiments, the weights of the other two categories of business evaluation items, customer financing situation by business and operation management situation can be set at 36% and 44% respectively, please refer to Table 2 and Table 3. Customer financing by business evaluation factors Average time cost (hours) (F21) Average income (10,000 yuan) (F22) Change rate of average number of households (%) (F23) Weights(%) 6 6 6 January 108 current month average current month average current month average 508 (D11) 701 (D17) 2,621 (D12) 2,942 (D18) 7.4 (D13) 5.3 (D19) Percentage of the month to the average 508/701=0.7246 2621/2942=0.8909 7.4/5.3=1.3962 Score 0.7246*6 =4.35 points 0.8909*6 =5.35 points 1*6 = 6 points Customer financing by business evaluation factors Average quota change rate (%) (F24) Average transfer amount (10,000 yuan) (F25) Average financing balance (10,000 yuan) (F26) Weights 6% 6% 6% January 108 current month average current month average current month average 122.6 (D14) 164.3 (D20) 30,021 (D15) 25,021 (D21) 31,620 (D16) 30,580 (D22) Percentage of the month to the average 122.6/164.3=0.7462 30021/25021=1.998 31620/30580=1.0340 Score 0.7462*6% =4.47 points 1*6% = 6 points 1*6% = 6 points Table II

As shown in Table 2, in the second largest category, customer financing by business, the evaluation factors F21~F26 each have a weight of 6%, and the financing of customers by business, one of the three major categories of business evaluation items, adds up to the evaluation factors For the respective weights of F21~F26, the financing situation of customers by business will have a weight of 36%. Moreover, the operating data D11~D22 also correspond to the assessment factors F21~F26 respectively. Calculating the scores of customer financing conditions (assessment factors F21-F26) by business in steps S110-S132 is similar to the way of calculating the overall business execution conditions (F11-F15) in steps S110-S132, and will not be repeated here.

In some embodiments, the average time cost of each business (assessment factor F21) represents the time required from new customer development or quota application initiation to quota approval in the month to be evaluated (eg, the current month). In some embodiments, the average time cost of each business (assessment factor F21) can be the value obtained by subtracting the time cost of the month to be evaluated (for example, the current month) from the highest value of time cost in the past five years, and this value accounts for the average time cost of the past five years ratio calculation.

In some embodiments, the average revenue of each business (assessment factor F22) represents the ratio of the revenue of the month to be evaluated (for example, the current month) to the average revenue of the past five years. The change rate of the average number of households of each business (assessment factor F23) represents the ratio of the change rate of the number of households in the month to be evaluated (for example, the current month) to the average change rate of the number of households in the past five years. The average quota change rate of each business (assessment factor F24) represents the ratio of the quota change rate of the month to be evaluated (for example, the current month) to the average quota change rate of the past five years. The average transfer amount of each business (evaluation factor F25) represents the ratio of the transfer amount in the month to be evaluated (for example, the current month) to the average transfer amount in the past five years. The average financing balance of each business (assessment factor F26) represents the ratio of the financing balance of the month to be evaluated (for example, the current month) to the average financing balance of the past five years.

Please refer to Table 2, the scores of evaluation factors F21~F26 are 4.35 points, 5.35 points, 6 points, 4.47 points, 6 points, 6 points.

In this way, when the highest score of customer financing by business category is 36 points (that is, with 36% weight), according to the respective scores of evaluation factors F21~F26, the machine learning model 114a can calculate the number of customers by business category in January 2018 The score of the financing situation is 4.35+5.35+6+4.47+6+6=32.17 points. Operation management situation evaluation factors Average Risk Master Rating (F31) Average number of households (F32) Average existing customer maintenance situation/Quantitative self-score of new customer development effectiveness (F33) Average Future Operating Forecast Self-Score (F34) Weights(%) 11 11 11 11 January 108 current month average current month average current month average current month average 4.3 (D23) 5.1 (D27) 63 (D24) 82 (D28) 6.2 (D25) 10 (D29) 7.3 (D26) 11 (D30) The average ratio of the current month 4.3/5.1=0.8431 63/82=0.7682 6.2/10=0.62 7.3/11=0.6636 Score 0.8431*11 =9.27 points 0.7682*11 =8.45 points 0.62*11 =6.82 points 0.6636*11 =7.30 points Table three

As shown in Table 3, in the third category, operation management situation, the evaluation factors F31~F34 each have a weight of 11%, and one of the three major categories of business evaluation items, business-specific customer financing, adds up to the evaluation factors F31~F34 The respective weights of F34 would have a weight of 44%. Moreover, the operating data D23~D30 also correspond to the assessment factors F31~F34 respectively. Calculating the score of the operation management situation (assessment factors F31~F34) through steps S110~S132 is similar to the method of calculating the overall business execution situation (F11~F15) through steps S110~S132, and will not be repeated here.

In some embodiments, the average main risk rating of each capital amount (assessment factor F31) represents the ratio of the main risk rating of the assessment month (for example, the current month) to the average main risk rating of the past five years. The average number of households for each capital amount (assessment factor F32) represents the ratio of the number of households in the assessment month (for example, the current month) to the average number of households in the past five years. Quantified self-score (assessment factor F33) of the average existing customer maintenance status/new customer development effectiveness of each capital amount represents the quantitative self-score score of the existing customer (new customer) maintenance status (development effectiveness) of the evaluation month (for example, the current month) accounted for in the past Five-year average existing customer (new customer) maintenance status (development effectiveness) quantified self-rating. The average future operating forecast self-score (assessment factor F34) for each capital amount represents the ratio of the future operating forecast self-score for the assessment month (eg, the current month) to the past five-year average future operating forecast self-score.

Please refer to Table 3, the scores of evaluation factors F31~F34 are 9.27 points, 8.45 points, 6.82 points and 7.30 points in sequence.

In this way, when the maximum score of the operation management situation is 44 points (that is, with 44% weight), the machine learning model 114a can calculate the score of the operation management situation in January 2018 as 9.27+8.45+6.82+7.30=31.84 point.

Therefore, the machine learning model 114a can calculate all evaluation factors F11~F15, F21~F26, and F31~F34 covered by the overall business execution status, business-specific customer financing status, and operation management status in the business evaluation item in the month to be evaluated The sum in the months to be assessed is equal to 14.86+32.17+31.84=78.87 points.

In some embodiments, the machine learning model 114a can compare the score (for example, 78.87 points) of the business project of the month to be evaluated with the score of the business project of the month in the past five years, so as to judge/assess the financial institution to be evaluated. The operating status of the current year and the same month in recent years.

Please refer to FIG. 3 again. In step S134, the machine learning model 114a calculates based on a piece of operation data D4 corresponding to the evaluation factor F14 (average total revenue) and a piece of historical operation data among the pieces of operation data D1~30 The first rate of change, and the second rate of change is calculated according to the scores of the business evaluation items and a historical score.

The formula for the first rate of change: (the score of the assessment factor F14 in the current month - the score of the assessment factor F14 in the previous month)/the score of the assessment factor F14 in the previous month. In some embodiments, the score of last month’s assessment factor F14 can be stored in server 130 or external memory 116, and processor 112 can extract the score of last month’s assessment factor F14 from server 130 or external memory 116, and execute the machine Learning Model 114a. Moreover, since in step S132 , the machine learning model 114 a can calculate the score of the evaluation factor F14 of the current month from the operating data D4 and D9 corresponding to the evaluation item F14 , the first rate of change can be calculated. In some embodiments, the score of the evaluation factor F14 in the last month may be calculated from historical operation data, which may be the operation data of the month before the month to be evaluated and corresponding to the evaluation factor F14. For example, if the month to be evaluated is January 108, the historical operation data may be December 107 and correspond to the operation data of the evaluation factor F14. The method of calculating the last month's evaluation factor F14 based on historical operating data is similar to the aforementioned steps S110-S132, and will not be repeated here. Since the score of the evaluation factor F14 in the current month is calculated based on the operation data D4 corresponding to the evaluation factor F14. In this way, the first rate of change can be calculated according to the operating data D4 corresponding to the evaluation factor F14 and the historical operating data.

In some embodiments, the formula of the second rate of change: (the average score of the business evaluation item in the current month−the average score of the business evaluation item in the previous month)/the average score of the business evaluation item in the previous month. The average score of the business evaluation items in the current month may be the average score of the business evaluation items in the desired month of evaluation for many years, for example, the average score of the business evaluation items in January of each year from 104 to 108. In addition, the average score of the business evaluation items in the previous month may be the average score of the business evaluation items in the month before the desired evaluation month for many years, for example, the average score of the business evaluation items in December of each year from 103 to 107. In other words, the average score of the business evaluation item in the previous month may be the historical average score of the business evaluation item in the month to be evaluated in the calendar year.

In some other embodiments, the formula of the second rate of change: (score of the business evaluation item of the current month−score of the business evaluation item of the previous month)/score of the business evaluation item of the previous month. The score of the business evaluation item in the previous month can be the score of the business evaluation item in the month before the month to be evaluated. For example, the month to be evaluated is January 108, and the score of the business evaluation item in the current month is the score in January 108. The score for the assessment item is the score for the business assessment item in December 107. In other words, the score of the business evaluation item in the previous month may be the historical score of the business evaluation item in the month to be evaluated in the previous year. Since the scores of the business evaluation items in the month to be evaluated (for example, the current month) have been calculated in detail in the aforementioned steps S110-S132. In this way, the second rate of change can be calculated according to the score of the business evaluation item of the current month and the score of the business evaluation item of the previous month.

In some embodiments, the average scores of the business evaluation items in the previous month and the scores of the business evaluation items in the month not to be evaluated in the current year can also be stored in the server 130 or the external memory 116, and the processor 112 can be downloaded from the server 130 or the external memory. The memory 116 extracts the average scores of the business evaluation items in the previous month and the scores of the business evaluation items in the month not to be evaluated in the current year, and executes the machine learning model 114a. Moreover, since in step S132, the machine learning model 114a can calculate the scores of the business evaluation items from the operating data D1~D30, in conjunction with the average score of the business evaluation items of the previous month and the business evaluation items of the month not to be evaluated in the current year score, the second rate of change can be calculated.

In step S136, it is determined whether the difference between the first rate of change and the second rate of change is greater than an expected value. In some embodiments, the expected value may be set at 5%.

In some embodiments, if the difference between the first rate of change and the second rate of change is greater than the expected value, proceed to step S137, and use the machine learning model 114a to preferentially adjust the one with the highest value and the one with the lowest value among the aforementioned weights, so that the first The difference between the rate of change and the second rate of change is smaller than the expected value. Next, proceed to step S132 , using the machine learning model 114 a to calculate the adjusted scores of the business evaluation items according to the pieces of operating data D1 - D30 and the adjusted weights. And continue with steps S134-S136, to verify whether the difference between the first rate of change and the second rate of change is smaller than the expected value, so as to obtain the credible adjusted score of the business evaluation item in the month to be evaluated. In this way, the machine learning model 114a in the business evaluation system 100 adjusts the weight according to the first rate of change and the second rate of change, and can more accurately calculate and evaluate the score of the business evaluation item in the month to be evaluated so as to correctly judge that the branch is the best Timing of business promotion. Specifically, multiple scores of the business evaluation item in each month can be calculated through steps S110-S130, and the highest score among the multiple scores of the business evaluation item in each month can be used as the best business promotion opportunity for the branch.

In some other embodiments, if the difference between the first rate of change and the second rate of change is greater than the expected value, the machine learning model 114a is based on the scores of each evaluation factor F11~F15, F21~F26 and F31~F34 of each business evaluation item The highest and the lowest adjust the weight of the two evaluation factors.

If the difference between the first rate of change and the second rate of change is less than or equal to the expected value, proceed to step S138 and not adjust the weights.

Please refer to FIG. 4 . FIG. 4 is a flowchart of a business evaluation method S100 according to an embodiment of the present disclosure. As shown in FIG. 4 , the business evaluation method S100 further includes steps S140 - S160 . In step S140, a plurality of operation sub-data of each of the plurality of business types are received through the input interface, and the operation sub-data respectively correspond to one part of the plurality of evaluation factors. In step S150, the machine learning model is used to calculate the scores of the business types according to the respective operation sub-data and corresponding weights of the business types. In step S160, a machine learning model is used to determine the best of the business types according to the scores of the business types.

In step S140, a plurality of operation sub-data of each of the plurality of business types are received through the input interface 120, and the operation data respectively correspond to one part of the plurality of evaluation factors. Please also refer to Table 4 below. Customer financing by business evaluation factors Average time cost (hours) (F21) Average income (10,000 yuan) (F22) Change rate of average number of households (%) (F23) Weights(%) 6 6 6 January 108 type current month average current month average current month average A 83 701 465 2,942 7.8 5.3 B 96 235 -2.4 C 169 1,085 0 D. 85 479 2 E. 75 357 0 total 508 2,621 7.4 Percentage of the month to the average 508/701=0.7246 2621/2942=0.8908 7.4/5.3=1.3962 Score total 0.7246*6=4.35 0.8908*6 =5.35 1*6 =6 A 0.71 0.95 6.32 B 0.82 0.48 -1.94 C 1.45 2.21 0 D. 0.73 0.98 1.62 E. 0.64 0.73 0 Customer financing by business evaluation factors Average quota change rate (%) (F24) Average transfer amount (10,000 yuan) (F25) Average financing balance (10,000 yuan) (F26) Weights(%) 6 6 6 January 108 type current month average current month average current month average A 22.9 164.3 3,465 25,021 6,465 30,580 B 5.6 1,235 3,235 C 73.5 23,085 29,085 D. -32.6 879 1,890 E. 53.2 1,357 945 total 122.6 30,021 41,620 Percentage of the month to the average 122.6/164.3=0.7461 30021/25021=1.1998 41620/30580=1.3610 Score total 0.7461*6 =4.47 1*6=6 1*6 =6 A 0.84 0.69 0.93 B 0.2 0.25 0.47 C 2.68 4.61 4.19 D. -1.19 0.18 0.27 E. 1.94 0.27 0.14 Table four

As shown in Table 4, business type A can be used for financing with a capital amount of less than 1 million. Business Type B can provide financing for $101 to $5 million. Business Type C can be used for financing from 501 to 30 million yuan. Business Type D can provide financing ranging from RMB 30.01 million to RMB 100 million. Business type E can provide financing of more than 100 million yuan.

In step S150 , the machine learning model 114 a calculates the scores of the business types A to E according to the respective sub-data and corresponding weights of the business types A to E. Taking business type A (financing below 1 million) as an example, in January 2018, the operation sub-data of business type A corresponding to the evaluation factor F21 (average time cost) was 83 hours, corresponding to the evaluation factor F21 (average time cost) The total operation sub-data of business type A~E of cost) is 508 hours. The proportion of business type A corresponding to evaluation factor F21 (average time cost) in business types A~E is 83 hours divided by 508 hours=0.16. The score of business type A in the evaluation factor F21 is obtained by multiplying the proportion of business type A in business types A~E corresponding to the evaluation factor F21 by the score of the evaluation factor F21 (ie, 0.16*4.35=0.71 points). Among all evaluation factors F21~F26 in customer financing by business category, the score of business type A is 0.71+0.95+6.32+0.84+0.69+0.93=10.44. The calculation methods of service types B~E are similar to those of service type A, and will not be repeated here.

In this way, among all evaluation factors F21~F26 in the customer financing situation by business category, the respective scores of business types A~E are 10.44 points, 0.28 points, 15.14 points, 2.59 points, and 3.72 points respectively.

In step S160, the machine learning model 114a is used to determine the best of the business types A˜E according to the scores of the business types. For example, the respective scores of business types A~E are 10.44 points, 0.28 points, 15.14 points, 2.59 points, and 3.72 points in sequence, and the machine learning model 114a can determine the business type C with the highest score among business types A~E It was the best in January 2018.

In some other embodiments, the machine learning model 114a compares the evaluation factors F14 (average total income) and Evaluation factor F15 (the proportion of total revenue in the whole bank), and operation management status evaluation factor F34 (average future operation forecast self-score), the one with the highest total score is the major category of business evaluation items (financing situation of customers by business category) The analysis results of predicting the best profit strategy and layout in it.

FIG. 5 is a flowchart of a business evaluation method S100 according to an embodiment of the present disclosure. The business evaluation method S100 further includes steps S170, S180, S182 and S184. In step S170, the third rate of change is calculated by the machine learning model 114a according to one of the pieces of operation sub-data and a piece of historical operation sub-data of the best of the business types A˜E.

The formula for the third rate of change: (the score of the business type N in the current month-the score of the business type N in the previous month)/the score of the business type N in the previous month, wherein the business type N can be the aforementioned business types A~E. In some embodiments, the score of last month’s business type N can be stored in the server 130 or the external memory 116, and the processor 112 can extract the last month’s score of the last month’s business type N from the server 130 or the external memory 116, and execute the machine Learning Model 114a. Moreover, since in step S132, the machine learning model 114a can calculate the score of the evaluation factor F14 of the current month from the operation sub-data corresponding to the evaluation items F21-F26, the third rate of change can be calculated. In some embodiments, the score of the business type N for the previous month may be calculated from the historical operation sub-data. In some embodiments, according to the number of evaluation items F21-F26, the historical operation sub-data can be one or more items. The historical operation sub-data may be the operation sub-data of the business type N in the month before the month to be evaluated. For example, if the month to be evaluated is January 2018, the historical operation sub-data may be the operation sub-data in December 2017. The method of calculating the historical operation sub-data is similar to the aforementioned steps S110-S132, and will not be repeated here.

In step S180, the machine learning model 114a determines whether the difference between the first rate of change and the third rate of change is greater than an expected value. In some embodiments, the expected value may be set at 5%.

In some embodiments, if the difference between the first rate of change and the third rate of change is greater than the expected value, proceed to step S182 by using the machine learning model 114a to preferentially adjust the weight with the highest value and the weight with the lowest value, so that the first The difference between the rate of change and the third rate of change is smaller than the expected value. Next, proceed to step S132 , using the machine learning model 114 a to calculate the adjusted scores of the business evaluation items according to the pieces of operating data D1 - D30 and the adjusted weights.

In some other embodiments, if the difference between the first rate of change and the third rate of change is greater than the expected value, the machine learning model 114a is based on the scores of each evaluation factor F11~F15, F21~F26 and F31~F34 of each business evaluation item The highest and the lowest adjust the weight of the two evaluation factors.

And continuing with steps S134-S136, it can be verified whether the difference between the first rate of change and the second rate of change is smaller than the expected value, thereby obtaining a more accurate and credible adjusted score for the business evaluation item.

If the difference between the first rate of change and the third rate of change is less than or equal to the expected value, proceed to step S184 and not adjust the weights.

To sum up, by adjusting the weights of the machine learning model 114a in the business evaluation system 100 according to the first rate of change and the second rate of change, it is possible to more accurately calculate and evaluate the score of the business evaluation item in the month to be evaluated to determine the branch The best time for an organization to promote its business. And the machine learning model 114a in the business evaluation system 100 can calculate the highest score among the business types A~E, which is the analysis result of the best profit strategy and layout of the bank branch. Furthermore, the best business promotion opportunity for the branch can be determined based on the highest average score of the business evaluation items in each month, and the best business promotion type for the branch can be determined based on the highest scores for each business type. In this way, the scores of credible business evaluation items and the respective scores of business types A~E calculated by the machine learning model 114a in the business evaluation system 100 can be combined into business management as the future profit strategy and layout direction of bank branches .

Although this case has disclosed the above in the way of implementation, it does not limit this case. Anyone who is familiar with this technology can make various changes and modifications without departing from the spirit and scope of this case. Therefore, the scope of protection of this case should be regarded as attached The one defined in the scope of the patent application shall prevail.

In order to make the above and other purposes, features, advantages and embodiments of the present disclosure more obvious and easy to understand, the descriptions of the attached symbols are as follows: 100: Business Evaluation System 110: Business Evaluation Host 112: Processor 114:Internal memory 114a: Machine Learning Models 116: External memory 120: input interface S: electronic device S100: Business evaluation methods S110, S120, S130, S132, S134, S136, S137, S138, S140, S150, S160, S170, S180, S182, S184: steps

In order to make the above and other purposes, features, advantages and embodiments of the present disclosure more comprehensible, the accompanying drawings are described as follows: Fig. 1 is a business evaluation system according to an embodiment of the present disclosure. FIG. 2 is a flowchart of a business evaluation method according to an embodiment of the present disclosure. FIG. 3 is a flow chart of step S130 in the business evaluation method in FIG. 2 according to an embodiment of the present disclosure. FIG. 4 is a flowchart of a business evaluation method according to an embodiment of the present disclosure. FIG. 5 is a flowchart of a business evaluation method according to an embodiment of the present disclosure.

Domestic deposit information (please note in order of depositor, date, and number) none Overseas storage information (please note in order of storage country, institution, date, and number) none

S100: Business evaluation methods

S110, S120, S130: steps

Claims (10)

A business evaluation system, comprising: a server storing a plurality of evaluation factors of a business evaluation item, wherein the evaluation factors respectively correspond to a plurality of weights; an input interface connected to the server by communication, and the input interface is used to receive A plurality of pieces of operation data, wherein these pieces of operation data respectively correspond to these evaluation factors; and a business evaluation host, which is connected to the server in communication, and the business evaluation host stores a machine learning model based on the corresponding A score of the business evaluation item is calculated based on the operation data of the evaluation factors and the weights corresponding to the evaluation factors, and the machine learning model is further used to A piece of operating data and a piece of historical operating data in the operating data calculate a first rate of change and calculate a second rate of change based on the score and a historical score, the machine learning model is based on the difference between the first rate of change and the second rate of change A difference adjusts the weights. The business evaluation system as described in Claim 1, wherein: when the difference between the first rate of change and the second rate of change is less than an expected value, the weights are not adjusted; and when the first rate of change and the When the difference between the second rate of change is greater than the expected value, the machine learning model adjusts the values of the weights so that the difference between the first rate of change and the second rate of change is smaller than the expected value, and the machine learning model is based on The adjusted weights are used to calculate the business evaluation item One adjusted score. The business evaluation system as described in claim 2, wherein the machine learning model preferentially adjusts the weight with the highest value and the lowest value, so that the difference between the first rate of change and the second rate of change is smaller than the expectation value. The business evaluation system as described in claim item 1, wherein the input interface is further used to receive a plurality of pieces of operation sub-data of each of a plurality of business types, and the pieces of operation sub-data of one of the business types respectively correspond to the one of the evaluation factors, wherein the machine learning model calculates the plurality of the business types based on one of the weights corresponding to the part of the evaluation factors and the respective operation sub-data of the business types score, and judge the best of the business types based on the scores of the business types. The business evaluation system as described in Claim 4, wherein the machine learning model is further used to calculate a third rate of change based on one of the pieces of operating data and a piece of historical operating sub-data of the best of the business types , the machine learning model judges whether a difference between the first rate of change and the third rate of change is greater than an expected value. The business evaluation system as described in Claim 5, wherein: when the difference between the first rate of change and the third rate of change is smaller than the expected value, the weights are not adjusted; and when the difference between the first rate of change and the third rate of change is greater than the expected value, the machine learning model adjusts the values of the weights so that the first rate of change and the third rate of change The difference between the three rates of change is less than the expected value, and the machine learning model calculates an adjusted score of the business evaluation item according to the adjusted weights. A business evaluation method, comprising: receiving multiple pieces of operation data through an input interface, wherein the pieces of operation data respectively correspond to a plurality of evaluation factors of a business evaluation item; A plurality of weights corresponding to the evaluation factors; a machine learning model is executed by a business evaluation host; the machine learning model is based on the pieces of operating data corresponding to the evaluation factors and the data corresponding to the evaluation factors calculating a score of the business evaluation item by weight; and calculating a first rate of change based on one of the operating data and a piece of historical operating data corresponding to one of the evaluation factors by the machine learning model, and A second rate of change is calculated according to the score and a historical score, and the machine learning model adjusts the weights according to a difference between the first rate of change and the second rate of change. The business evaluation method as described in Claim 7, wherein: when the difference between the first rate of change and the second rate of change is less than an expectation value, the weights are not adjusted; and when the difference between the first rate of change and the second rate of change is greater than the expected value, the machine learning model is used to adjust the values of the weights so that the first rate of change and the second rate of change are greater than the expected value. The difference of the second rate of change is smaller than the expected value, and an adjusted score of the business evaluation item is calculated according to the adjusted weights. The business evaluation method as described in Claim 8, wherein the machine learning model is used to preferentially adjust the weight with the highest value and the lowest value, so that the difference between the first rate of change and the second rate of change is less than the expected value. The business evaluation method as described in claim item 7, wherein a plurality of pieces of operation sub-data of each of a plurality of business types are received through the input interface, and the pieces of operation sub-data of one of the business types respectively correspond to the a part of the evaluation factors, wherein the machine learning model is used to calculate the weights of the business types according to the part of the weights corresponding to the part of the evaluation factors and the respective operation sub-data of the business types multiple scores, and determine the best of the business types based on the scores of the business types.

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