CN110415106A - A kind of performance analysis forecast assessment system of management body - Google Patents
A kind of performance analysis forecast assessment system of management body Download PDFInfo
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- CN110415106A CN110415106A CN201910668423.3A CN201910668423A CN110415106A CN 110415106 A CN110415106 A CN 110415106A CN 201910668423 A CN201910668423 A CN 201910668423A CN 110415106 A CN110415106 A CN 110415106A
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Abstract
The invention discloses a kind of performance analysis forecast assessment systems of management body, utilize disclosed data and authorization data, it refers specifically to integrate electric business platform, payment platform, financial data, tax data and electricity consumption with data such as water, data set after integration on the basis of is modeled.According to history business revenue data, difference settling time series model and xgboost regression model；The more enterprise of history business revenue data, using time series models, comprising: the pretreatment for carrying out time series makes data tranquilization and randomization；Xgboost regression model predicts the index in base period current business revenue.Then a series of analyses have been carried out to business revenue prediction result.Finally in conjunction with the business revenue of prediction, financial data and family income, liquidity ratio, current rate etc. are calculated to assess the repaying ability of enterprise, and provide reasonable loan repayment capacity range.
Description
Technical field
The invention belongs to forecast assessment fields, are related to performance analysis technology, the performance analysis of specifically a kind of management body
Forecast assessment system.
Background technique
Since the service time of little Wei enterprise is shorter, to be taken root in economic market not deeply, information announcing is few, and enterprise
Financial report authenticity needs to be considered, and little Wei enterprises and individual industrial and commercial households internal control system is also not mature enough, and lacks phase
The repaying ability answered to substantially increase business risk, while also improving commercial bank credit risk.Therefore next phase
Business performance is predicted that the repaying ability for assessing enterprise seems necessary.
So-called loan repayment ability refers to the ability that borrower fully pays off commercial bank loans principal and interest on schedule.For
It can reinforce credit risk control dynamics comprehensively, the application utilizes big data technology, carries out comprehensively to the business circumstance of enterprise
Analysis.
The industry that little Wei enterprise, industrial and commercial units set foot in is numerous, and locating industry is different, and credit requirement is also not quite similar.Its row
Credit requirement otherness caused by industry is dispersed is big；
Credit requirement timeliness requires high；
The real information of credit requirement is not easy to grasp；；
Lack effective, foot value guarantee to be assured；
Scale of operation is smaller, strength is relatively weak, and capital is less, cannot increase letter, institute by providing guarantee to assure
It is difficult to obtain effective credit aid from banking channels with a large amount of little Wei enterprises, industrial and commercial units, it is caused to develop slowly, resists external
The ability of environmental change is poor, and business risk is high.
Summary of the invention
The purpose of the present invention is to provide a kind of performance analysis forecast assessment systems of management body.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of performance analysis forecast assessment system of management body, including data collection module, model foundation unit, data
Analytical unit, processor, display unit, storage unit and administrative unit；
Wherein, the data collection module is used to collect the basic data of target user, and the basic data includes finance
Report, online trading data evaluate data, payment data, tax data and water power data on line；
Financial statement is the financial data of specification, including balance sheet, profit flow table and cash flow statement；Online trading number
According to for by the transaction data of electric business transaction platform, evaluation data are the evaluation to the transaction data of electric business transaction platform on line；
Payment data is the payment data by payment platforms such as Alipay or wechats；Water power data are monthly using for the target user
Water electricity consumption data；
The model foundation unit is used for according to basic data settling time series model and xgboost regression model；Institute
It states data analysis unit and establishes unit to basic data progress signature analysis processing for binding model, obtain new characteristic value simultaneously
The value is labeled as associated eigenvalue, prediction processing is carried out to associated eigenvalue later；Index value is related to dependent variable progress
Property analysis, dependent variable is business revenue, rejects the very weak index of correlation；By python tool to training data, that is, it is aforementioned
Independent variable, that is, basic data add dependent variable, that is, business revenue data；Specific processing step is as follows:
Step 1: Feature Selection is carried out to basic data；Selected especially by the RFECV in the library sklearn, it is laggard
Row RF calculates Importance of Factors and ranking, statistical factors importance percentage and accuracy rate, the relationship of data volume and drawing；
Step 2: handling missing values, and removal missing values are greater than 90% feature first, and missing values are lower than
90% feature then substitutes missing values using average value or mode；
Step 3: extreme value processing is carried out by python tool；
Step 4: particular time processing, the variance for finding out all mean values are greater than the businessman of threshold value, it is excessive directly to delete fluctuation
Period data；Preset time period mean value is more than preset value at this time for excessive reference；
Step 5: construction feature, data sliding window, the business revenue in several seasons before increasing；By ratio sliding window obtain ring ratio and
On year-on-year basis；To obtain associated eigenvalue；
Step 6: calling module packet included inside python, is passed to data, developing algorithm model, according to the history of enterprise
Data volume, history business revenue data value is more, and the industry that business revenue timing diagram is relatively stable, then uses time series models, in advance
Survey the business revenue of next phase, the more business revenue data for being defined as eight phases or more；
It is less to be defined as eight phases business revenue data below when history business revenue data value is less, machine learning has been respectively adopted
Algorithm random forest, lightgbm, xgboost remove building regression model with the index value in base period, are fitted current business revenue value；
Step 7: optimizing model, optimizes using sliding window is trained with three folding cross validation methods；
Step 8: business revenue tendency chart, year-on-year fluctuation tendency figure etc. are formed, by business revenue tendency chart, year-on-year fluctuation tendency icon
It is set to prediction data；
The administrative unit is used for user's typing monthly repayment amount；The data collection module is also used to obtain target use
The average cash at family flows into and average cash outflow；The data analysis unit is used to prediction data being transferred to processor；Institute
Processor is stated for getting the business revenue of prediction according to prediction data and family income and carrying out repaying ability and analyzed
As a result；
The processor is transferred to display unit progress real-time display for that will analyze result, and the processor will be for that will divide
Analysis result is transferred to storage unit and carries out real-time storage.
Further, the basic data is crawled by website and authorization obtains.
Further, the history business revenue data are the business performance index that company one arrives the n phase, are dependent variables；By spy
The associated eigenvalue obtained after sign analysis processing is independent variable.
Further, during seven model optimization of signature analysis processing step, also argument value has been carried out at normalization
Reason, wherein normalized refers to that x*=(x-x_mean)/(x_max-x_min), x_mean indicate the mean value of data, and x is just
It is a series of argument value；There will be the expression formula of dimension, by transformation, turn to nondimensional expression formula, become scalar.
Further, the family income is the family income of corporate boss；Specific repaying ability analytical procedure are as follows:
Step 1: the business revenue, family income, the average cash inflow of target user, average cash outflow of prediction are got
Monthly repayment amount；
Step 2: above-mentioned data are substituted into condition one and condition two:
Condition one: family's average cash inflow-target user average cash outflow of target user >=gold of monthly refunding
Volume
Condition two: reasonable loan repayment capacity=(business revenue+family income of prediction) × specified accounting, specify accounting be 40%~
50%；
Only meet condition one and condition two simultaneously, just can determine that have loan repayment capacity；And monthly repayment amount be less than etc.
In reasonable loan repayment capacity；
Step 3: analysis result is obtained.
Beneficial effects of the present invention:
The method of small micro- enterprise operation achievement prediction provided herein, using disclosed data and authorization data,
It refers specifically to integrate electric business platform, payment platform, financial data, tax data and electricity consumption with data such as water, in the integration
It is modeled on the basis of data set afterwards.According to history business revenue data, settling time series model and xgboost return mould respectively
Type；The more enterprise of history business revenue data, using time series models, comprising: the pretreatment for carrying out time series keeps data flat
Steadyization and randomization；Xgboost regression model predicts the index in base period current business revenue.Then to business revenue prediction result into
A series of analyses are gone.Finally in conjunction with the business revenue of prediction, financial data and family income, liquidity ratio, current rate are calculated
Etc. come the repaying ability of assessing enterprise, and provide reasonable loan repayment capacity range；The present invention is simple and effective, and is easy to practical.
Detailed description of the invention
In order to facilitate the understanding of those skilled in the art, the present invention will be further described below with reference to the drawings.
Fig. 1 is system block diagram of the invention；
Fig. 2 is financial statement data example.
Specific embodiment
As shown in Figure 1, a kind of performance analysis forecast assessment system of management body, including data collection module, model are built
Vertical unit, data analysis unit, processor, display unit, storage unit and administrative unit；
Wherein, the data collection module is used to collect the basic data of target user, and the basic data includes finance
Report, online trading data evaluate data, payment data, tax data and water power data on line；
As shown in Fig. 2, financial statement is the financial data of specification, including balance sheet, profit flow table and cash flow statement；
Online trading data are the transaction data by electric business transaction platform, and evaluation data are the number of deals to electric business transaction platform on line
According to evaluation；Payment data is the payment data by payment platforms such as Alipay or wechats；Water power data are target use
Monthly use water electricity consumption data in family；
The model foundation unit is used for according to basic data settling time series model and xgboost regression model；Institute
It states data analysis unit and establishes unit to basic data progress signature analysis processing for binding model, obtain new characteristic value simultaneously
The value is labeled as associated eigenvalue, wherein basic data is crawled by website and authorization obtains；By index value and dependent variable " battalion
Receive " correlation analysis is carried out, reject the very weak index of correlation；By python tool to training data, that is, it is above-mentioned
Independent variable, that is, basic data (transaction data evaluates data etc.) adds dependent variable, that is, business revenue data, carries out down to these training datas
The signature analysis for stating step 1 to step 5 is handled；Specific processing step is as follows:
Step 1: Feature Selection is carried out to basic data；Selected especially by the RFECV in the library sklearn, it is laggard
Row RF calculates Importance of Factors and ranking, statistical factors importance percentage and accuracy rate, the relationship of data volume and drawing；
Step 2: handling missing values, and removal missing values are greater than 90% feature first, and missing values are lower than
90% feature then substitutes missing values using average value or mode；
Step 3: extreme value processing is carried out by python tool；
Step 4: particular time processing, the variance for finding out all mean values are greater than the businessman of threshold value, it is excessive directly to delete fluctuation
Period data, the excessive mean value for referring to preset time period are more than certain threshold value, herein, can refer on behalf of all mean values be more than preset value
Week；Such as National Day；
Step 5: construction feature, data sliding window, the business revenue in several seasons before increasing；By ratio sliding window obtain ring ratio and
On year-on-year basis；
Step 6: calling module packet included inside python, is passed to data, developing algorithm model, according to the history of enterprise
Data volume, history business revenue data value is more, and the industry that business revenue timing diagram is relatively stable, the more battalion for being defined as eight phases or more
Data are received, time series models is used, predicts the business revenue of next phase；
It is less to be defined as eight phases business revenue data below when history business revenue data value is less, machine learning has been respectively adopted
Algorithm random forest, lightgbm, xgboost remove building regression model with the index value in base period, are fitted current business revenue value；
History business revenue data are exactly: company one arrives the business performance index of n phase, is dependent variable；It is handled by signature analysis
The associated eigenvalue obtained later is independent variable；
Step 7: optimizing model, optimizes using sliding window is trained with three folding cross validation methods；For example it wants
The business revenue for predicting in March, 2018, using the index value of 2011.6-2016.3 as independent variable x, 2016.6 business revenue is as strain
Measure y；It is originally and uses business revenue as dependent variable y, regard y and log2 (y), log10 (y) as dependent variable respectively in modeling, as a result send out
Existing log (y) is more preferable than y effect；During model optimization, argument value normalized is subjected to also, wherein at normalization
Reason refers to that x*=(x-x_mean)/(x_max-x_min), x_mean indicate the mean value of data, and x is exactly a series of independent variable
Value；There will be the expression formula of dimension, by transformation, turn to nondimensional expression formula, become scalar；
Step 8: business revenue tendency chart, year-on-year fluctuation tendency figure etc. are formed, by business revenue tendency chart, year-on-year fluctuation tendency icon
It is set to prediction data；
The administrative unit is used for user's typing monthly repayment amount；The data collection module is also used to obtain target use
The average cash at family flows into and average cash outflow；The data analysis unit is used to prediction data being transferred to processor；Institute
Processor is stated for getting the business revenue of prediction according to prediction data and family income and carrying out repaying ability analysis, family income
For the family income of corporate boss；Make a concrete analysis of step are as follows:
Step 1: the business revenue, family income, the average cash inflow of target user, average cash outflow of prediction are got
Monthly repayment amount；
Step 2: above-mentioned data are substituted into condition one and condition two:
Condition one: family's average cash inflow-target user average cash outflow of target user >=gold of monthly refunding
Volume
Condition two: reasonable loan repayment capacity=(business revenue+family income of prediction) × specified accounting, specify accounting be 40%~
50%；
Only meet condition one and condition two simultaneously, just can determine that have loan repayment capacity；And monthly repayment amount be less than etc.
In reasonable loan repayment capacity；
Step 3: analysis result is obtained.
The processor is transferred to display unit progress real-time display for that will analyze result, and the processor will be for that will divide
Analysis result is transferred to storage unit and carries out real-time storage.
The method of small micro- enterprise operation achievement prediction provided herein, using disclosed data and authorization data,
It refers specifically to integrate electric business platform, payment platform, financial data, tax data and electricity consumption with data such as water, in the integration
It is modeled on the basis of data set afterwards.According to history business revenue data, settling time series model and xgboost return mould respectively
Type；The more enterprise of history business revenue data, using time series models, comprising: the pretreatment for carrying out time series keeps data flat
Steadyization and randomization；Xgboost regression model predicts the index in base period current business revenue.Then to business revenue prediction result into
A series of analyses are gone.Finally in conjunction with the business revenue of prediction, financial data and family income, liquidity ratio, current rate are calculated
Etc. come the repaying ability of assessing enterprise, and provide reasonable loan repayment capacity range；The present invention is simple and effective, and is easy to practical.
Above content is only to structure of the invention example and explanation, affiliated those skilled in the art couple
Described specific embodiment does various modifications or additions or is substituted in a similar manner, without departing from invention
Structure or beyond the scope defined by this claim, is within the scope of protection of the invention.
Claims (5)
1. a kind of performance analysis forecast assessment system of management body, which is characterized in that including data collection module, model foundation
Unit, data analysis unit, processor, display unit, storage unit and administrative unit；
Wherein, the data collection module is used to collect the basic data of target user, the basic data include financial statement,
Data, payment data, tax data and water power data are evaluated in online trading data, line；
Financial statement is the financial data of specification, including balance sheet, profit flow table and cash flow statement；Online trading data are
By the transaction data of electric business transaction platform, evaluation data are the evaluation to the transaction data of electric business transaction platform on line；Payment
Data are the payment data by payment platforms such as Alipay or wechats；Water power data are monthly being used with water for the target user
Electric data；
The model foundation unit is used for according to basic data settling time series model and xgboost regression model；The number
Unit is established for binding model according to analytical unit, signature analysis processing is carried out to basic data, obtain new characteristic value and should
Value is labeled as associated eigenvalue, carries out prediction processing to associated eigenvalue later；Index value and dependent variable are subjected to correlation point
Analysis, dependent variable is business revenue, rejects the very weak index of correlation；By python tool to training data, that is, it is above-mentioned from
Variable, that is, basic data adds dependent variable, that is, business revenue data；Specific processing step is as follows:
Step 1: Feature Selection is carried out to basic data；It is selected especially by the RFECV in the library sklearn, carries out RF later
Calculate Importance of Factors and ranking, statistical factors importance percentage and accuracy rate, the relationship of data volume and drawing；
Step 2: handling missing values, and removal missing values are greater than 90% feature first, for missing values lower than 90%
Feature then substitutes missing values using average value or mode；
Step 3: by python tool carry out extreme value processing, by index value be greater than 99.9% or the value less than 0.1% be changed to
The value of 99.9% or 0.1% position；
Step 4: particular time processing, the variance for finding out predicted time section are greater than the businessman of threshold value, it is excessive directly to delete fluctuation
Period data；The excessive mean value for referring to preset time period at this time is more than preset value；
Step 5: construction feature, data sliding window, the business revenue in several seasons before increasing；By ratio sliding window obtain ring ratio and on year-on-year basis；
To obtain associated eigenvalue；
Step 6: calling module packet included inside python, is passed to data, developing algorithm model, according to the historical data of enterprise
Amount, history business revenue data value is more, and the industry that business revenue timing diagram is relatively stable, then time series models is used, under prediction
The business revenue of one phase, the more business revenue data for being defined as eight phases or more；
It is less to be defined as eight phases business revenue data below when history business revenue data value is less, machine learning algorithm has been respectively adopted
Random forest, lightgbm, xgboost remove building regression model with the index value in base period, are fitted current business revenue value；
Step 7: optimizing model, optimizes using sliding window is trained with three folding cross validation methods；
Step 8: business revenue tendency chart, year-on-year fluctuation tendency figure etc. are formed, business revenue tendency chart, year-on-year fluctuation tendency icon are set to
Prediction data；
The administrative unit is used for user's typing monthly repayment amount；The data collection module is also used to obtain target user's
Average cash flows into and average cash outflow；The data analysis unit is used to prediction data being transferred to processor；The place
Reason device is used to get the business revenue of prediction according to prediction data and family income and carries out repaying ability and analyze to obtain analysis result；
The processor is transferred to display unit progress real-time display for that will analyze result, and the processor will be for that will analyze knot
Fruit is transferred to storage unit and carries out real-time storage.
2. a kind of performance analysis forecast assessment system of management body according to claim 1, which is characterized in that the base
Plinth data are crawled by website and authorization obtains.
3. a kind of performance analysis forecast assessment system of management body according to claim 1, which is characterized in that described to go through
History business revenue data are the business performance index that company one arrives the n phase, are dependent variables；The correlation obtained after being handled by signature analysis
Characteristic value is independent variable.
4. a kind of performance analysis forecast assessment system of management body according to claim 1, which is characterized in that feature point
During analysing seven model optimization of processing step, argument value is also subjected to normalized, wherein normalized refers to:
X*=(x-x_mean)/(x_max-x_min), x_mean indicate the mean value of data, and x is exactly a series of argument value；It will
There is the expression formula of dimension, by transformation, turns to nondimensional expression formula, become scalar.
5. a kind of performance analysis forecast assessment system of management body according to claim 1, which is characterized in that the family
Front yard income is the family income of corporate boss；Specific repaying ability analytical procedure are as follows:
Step 1: get the business revenue of prediction, family income, target user average cash flow into, average cash outflow and every
Month repayment amount；
Step 2: above-mentioned data are substituted into condition one and condition two:
Condition one: family's average cash inflow-target user average cash outflow >=monthly repayment amount of target user
Condition two: reasonable loan repayment capacity=(business revenue+family income of prediction) × specified accounting, specify accounting be 40%~
50%；
Only meet condition one and condition two simultaneously, just can determine that have loan repayment capacity；And monthly repayment amount is less than or equal to close
Manage loan repayment capacity；
Step 3: analysis result is obtained.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110991926A (en) * | 2019-12-16 | 2020-04-10 | 象山电力实业有限公司 | Comprehensive energy service system based on enterprise power consumption big data analysis |
| CN111353812A (en) * | 2020-02-20 | 2020-06-30 | 中信银行股份有限公司 | Chain type intelligent marketing method and device |
| CN114154866A (en) * | 2021-12-02 | 2022-03-08 | 北京顶象技术有限公司 | Marketing enterprise financial risk early warning method and system |
| WO2022141883A1 (en) * | 2020-12-31 | 2022-07-07 | 平安科技（深圳）有限公司 | Enterprise revenue trend prediction method and apparatus, and computer device and storage medium |
-
2019
- 2019-07-23 CN CN201910668423.3A patent/CN110415106A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110991926A (en) * | 2019-12-16 | 2020-04-10 | 象山电力实业有限公司 | Comprehensive energy service system based on enterprise power consumption big data analysis |
| CN111353812A (en) * | 2020-02-20 | 2020-06-30 | 中信银行股份有限公司 | Chain type intelligent marketing method and device |
| WO2022141883A1 (en) * | 2020-12-31 | 2022-07-07 | 平安科技（深圳）有限公司 | Enterprise revenue trend prediction method and apparatus, and computer device and storage medium |
| CN114154866A (en) * | 2021-12-02 | 2022-03-08 | 北京顶象技术有限公司 | Marketing enterprise financial risk early warning method and system |
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Application publication date: 20191105 |