CN116245634A - Risk assessment method, apparatus, device and storage medium applied to retail enterprises - Google Patents
Risk assessment method, apparatus, device and storage medium applied to retail enterprises Download PDFInfo
- Publication number
- CN116245634A CN116245634A CN202310200118.8A CN202310200118A CN116245634A CN 116245634 A CN116245634 A CN 116245634A CN 202310200118 A CN202310200118 A CN 202310200118A CN 116245634 A CN116245634 A CN 116245634A
- Authority
- CN
- China
- Prior art keywords
- data
- result
- risk
- module
- enterprise
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012502 risk assessment Methods 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000003860 storage Methods 0.000 title claims abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 70
- 238000004519 manufacturing process Methods 0.000 claims abstract description 30
- 230000002159 abnormal effect Effects 0.000 claims description 57
- 238000012544 monitoring process Methods 0.000 claims description 54
- 238000004140 cleaning Methods 0.000 claims description 35
- 238000012790 confirmation Methods 0.000 claims description 31
- 238000012795 verification Methods 0.000 claims description 18
- 238000006243 chemical reaction Methods 0.000 claims description 15
- 238000012937 correction Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 abstract description 27
- 238000007405 data analysis Methods 0.000 abstract description 3
- 230000007246 mechanism Effects 0.000 abstract description 2
- 238000013473 artificial intelligence Methods 0.000 abstract 1
- 230000006399 behavior Effects 0.000 description 12
- 238000011144 upstream manufacturing Methods 0.000 description 10
- 230000008859 change Effects 0.000 description 8
- 230000003247 decreasing effect Effects 0.000 description 6
- 238000007726 management method Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000012384 transportation and delivery Methods 0.000 description 4
- 241000209202 Bromus secalinus Species 0.000 description 3
- 238000011835 investigation Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000001502 supplementing effect Effects 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012954 risk control Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 235000019800 disodium phosphate Nutrition 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/12—Accounting
- G06Q40/125—Finance or payroll
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Technology Law (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Economics (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the application belongs to the technical field of data analysis in artificial intelligence, and relates to a risk assessment method, a risk assessment device, computer equipment and a storage medium applied to retail enterprises. The supply chain data of this application directly derive from production systems such as order system, ERP system, inventory system of enterprise, processing behind the relevant mechanism obtained data, and the data forms the closed loop each other, stopped that traditional only provides financial staff and processes thick financial statement data, and the financial staff of enterprise carries out the condition of cheating through financial bore or subject adjustment's mode easily, effectively improves the reliability of data, in addition, this application is through risk decision engine control wide table data carrying out risk prediction processing, abandon traditional and rely on artificial implementation means, the treatment effeciency promotes greatly, can in time discover the possible business risk that exists of retail enterprise.
Description
Technical Field
The present disclosure relates to the field of data analysis technologies, and in particular, to a risk assessment method, apparatus, computer device, and storage medium for retail enterprises.
Background
Current methods of risk assessment for enterprises by financial institutions rely primarily on online data queries and offline research, where: the off-line investigation mainly takes site investigation, interview, physical check and asset counting as the reference; the online data investigation is performed by using limited information means to search and assist judgment, and specifically includes:
(1) Financial data analysis: cross analysis is mainly carried out through three major forms of finance (assets, liabilities and owner interests) and cash flow tables, ROE (record of information) is calculated, short-term repayment capability indexes, flow rate and other indexes are calculated, and then, comparison analysis is carried out based on the data;
(2) Enterprise base data: collecting industrial and commercial information, stock right information, judicial information, employee information and the like disclosed by an enterprise, and then manually checking dangerous information;
(3) Relevant external data: third party data (including but not limited to judicial, financial, electricity, financial, etc.) is used to determine data for credit rating, associated business data (e.g., guaranty), business liability, etc. for highly relevant personnel of the business Dong Jian.
However, the applicant finds that the data of the traditional risk assessment method is derived from three parts, namely finance, a foundation and an outside, the data source is single, mutual authentication cannot be realized, a closed loop is formed, and the opportunity is provided for subjective counterfeiting of enterprises; in addition, the processing and forming conclusion of the data are processed by manpower, the rule and the process of the data monitoring are manual, the problems can not be found in time, the monitoring period is long, and the monitoring can not be carried out according to the days, so that the condition that the monitoring is incomplete and the possible management risk of retail enterprises can not be found in time is caused. Therefore, the conventional risk assessment method has the problem of lower reliability.
Disclosure of Invention
The embodiment of the application aims to provide a risk assessment method, a risk assessment device, computer equipment and a storage medium applied to retail enterprises, so as to solve the problem that the reliability of the traditional risk assessment method is low.
In order to solve the above technical problems, the embodiments of the present application provide a risk assessment method applied to retail enterprises, which adopts the following technical schemes:
receiving a risk assessment request sent by a user terminal, wherein the risk assessment request comprises an enterprise identifier to be assessed;
reading a production system, and acquiring supply chain data corresponding to the enterprise identifier to be evaluated in the production system;
carrying out data structuring treatment on the supply chain data according to a checking algorithm to obtain a structured data result;
performing data cleaning treatment on the structured data result according to a cleaning algorithm to obtain monitoring wide-table data;
performing risk prediction processing on the monitoring wide table data according to a risk decision engine to obtain a risk prediction result;
and outputting the risk prediction result to the user terminal.
Further, the step of performing data structuring processing on the supply chain data according to a verification algorithm to obtain a structured data result includes the following steps:
Carrying out non-empty confirmation processing on the key field of the supply chain data according to a non-empty verification algorithm to obtain a non-empty data result;
performing numerical range confirmation processing on the numerical field of the supply chain data according to a numerical range verification algorithm to obtain a numerical range data result;
performing outlier confirmation processing on the type field of the supply chain data according to a data outlier checking algorithm to obtain an outlier data result;
and taking the correct data of the numerical range data result, the numerical range data result and the abnormal value data result as the structured data result.
Further, after the step of performing outlier confirmation processing on the type field of the supply chain data according to the data outlier checking algorithm to obtain an outlier data result, the method further includes the following steps:
calculating the total error data amount of the numerical range data result, the numerical range data result and the abnormal value data result;
and if the total error data amount is larger than a preset threshold value of the total supply chain data of the current day, outputting a data risk warning signal to the user terminal.
Further, the step of performing data cleaning processing on the structured data result according to a cleaning algorithm to obtain monitoring wide table data specifically includes the following steps:
If the structured data result has a null field, filling a preset default value in the null field;
if the structured data result has abnormal values, carrying out value correction processing on the abnormal values;
and carrying out numerical conversion operation on the structured data result according to a preset conversion rule to obtain the monitoring wide table data.
In order to solve the above technical problems, the embodiments of the present application further provide a risk assessment device applied to a retail enterprise, which adopts the following technical scheme:
the system comprises a request receiving module, a risk assessment module and a risk assessment module, wherein the request receiving module is used for receiving a risk assessment request sent by a user terminal, and the risk assessment request comprises an enterprise identifier to be assessed;
the data acquisition module is used for reading a production system and acquiring supply chain data corresponding to the enterprise identifier to be evaluated in the production system;
the data structuring module is used for carrying out data structuring processing on the supply chain data according to a checking algorithm to obtain a structured data result;
the data cleaning module is used for performing data cleaning processing on the structured data result according to a cleaning algorithm to obtain monitoring wide-table data;
the risk prediction module is used for performing risk prediction processing on the monitoring wide table data according to a risk decision engine to obtain a risk prediction result;
And the result output module is used for outputting the risk prediction result to the user terminal.
Further, the data structuring module comprises:
the non-empty confirmation sub-module is used for carrying out non-empty confirmation processing on the key field of the supply chain data according to a non-empty verification algorithm to obtain a non-empty data result;
the numerical range confirmation sub-module is used for carrying out numerical range confirmation processing on the numerical field of the supply chain data according to a numerical range verification algorithm to obtain a numerical range data result;
the abnormal value confirmation sub-module is used for carrying out abnormal value confirmation processing on the type field of the supply chain data according to a data abnormal value correction algorithm to obtain an abnormal value data result;
and the structured data confirmation sub-module is used for taking the numerical range data result, the numerical range data result and correct data of the abnormal value data result as the structured data result.
Further, the data structuring module further comprises:
an error data total calculation operator module, configured to calculate an error data total amount of the numerical range data result, and the outlier data result;
And the warning signal output sub-module is used for outputting a data risk warning signal to the user terminal if the total error data amount is larger than a preset threshold value of the total supply chain data of the current day.
Further, the device further comprises:
the null value filling module is used for filling a preset default value in the null value if the null value field exists in the structured data result;
the numerical value correction module is used for carrying out numerical value correction processing on the abnormal numerical value if the structured data result has the abnormal numerical value;
and the numerical conversion module is used for carrying out numerical conversion operation on the structured data result according to a preset conversion rule to obtain the monitoring wide table data.
In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:
comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of a risk assessment method as described above for use in a retail establishment.
In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:
The computer readable storage medium has stored thereon computer readable instructions which when executed by a processor implement the steps of a risk assessment method as described above for application to a retail establishment.
The application provides a risk assessment method applied to retail enterprises, comprising the following steps: receiving a risk assessment request sent by a user terminal, wherein the risk assessment request comprises an enterprise identifier to be assessed; reading a production system, and acquiring supply chain data corresponding to the enterprise identifier to be evaluated in the production system; carrying out data structuring treatment on the supply chain data according to a checking algorithm to obtain a structured data result; performing data cleaning treatment on the structured data result according to a cleaning algorithm to obtain monitoring wide-table data; performing risk prediction processing on the monitoring wide table data according to a risk decision engine to obtain a risk prediction result; and outputting the risk prediction result to the user terminal. Compared with the prior art, the supply chain data of the application are directly derived from production systems such as an order system, an ERP system and an inventory system of an enterprise, related institutions acquire data and then process the data, the data form a closed loop, the situation that traditional financial staff only process thick financial statement data is stopped, and the financial staff of the enterprise easily cheats in a financial caliber or subject adjustment mode is avoided, the reliability of the data is effectively improved, in addition, the application performs risk prediction processing by monitoring wide-table data through a risk decision engine, the traditional implementation means depending on manpower is abandoned, the processing efficiency is greatly improved, and the possible operation risk of a retail enterprise can be timely found.
Drawings
For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flowchart of an implementation of a risk assessment method for a retail establishment according to an embodiment of the present application;
FIG. 3 is a flow chart of one embodiment of step S203 of FIG. 2;
FIG. 4 is a flow chart of one embodiment after step S303 in FIG. 3;
FIG. 5 is a flow chart of one embodiment of step S204 of FIG. 2;
fig. 6 is a schematic structural diagram of a risk assessment device for retail enterprises according to the second embodiment of the present application;
FIG. 7 is a schematic structural diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the risk assessment method applied to the retail enterprises provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the risk assessment device applied to the retail enterprises is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Example 1
With continued reference to fig. 2, a flowchart of an implementation of a risk assessment method applied to a retail enterprise according to an embodiment of the present application is shown, and for convenience of explanation, only a portion relevant to the present application is shown.
The risk assessment method applied to the retail enterprises comprises the following steps: step S201, step S202, step S203, step S204, step S205, and step S206.
In step S201, a risk assessment request sent by a user terminal is received, where the risk assessment request includes an enterprise identifier to be assessed.
In the embodiments of the present application, the user terminal may be a mobile terminal such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, etc., and a fixed terminal such as a digital TV, a desktop computer, etc., it should be understood that the examples of the user terminal are only for convenience of understanding and are not intended to limit the present invention.
In the embodiment of the present application, the business identifier is mainly used for uniquely identifying a retail business, and the business identifier may be set based on the pinyin initials of the business name, for example: the enterprise identifier of the Meiyijia enterprise is MYJ; the enterprise identity may also be set based on a serial number, such as, for example: 001; the business identifier may also be set in combination based on the pinyin initials and serial numbers of the business name, for example: the business identifier of the mei jia business is MYJ001, and it should be understood that the example of the business identifier herein is merely for convenience of understanding and is not intended to limit the present application.
In the embodiment of the application, the enterprise identifier to be evaluated is mainly used for identifying the enterprise object to be evaluated required by the request.
In step S202, the production system is read, and supply chain data corresponding to the enterprise identifier to be evaluated is acquired in the production system.
In the present embodiment, the production system is primarily used to store supply chain data for each retail business, where the production system includes an order system, ERP (Enterprise Resource Planning) system, inventory system, and the like.
In the embodiment of the application, the business mode of the retail enterprise needs to be researched, the operation mode and the data system of the retail enterprise are clarified, the data of the order system, the ERP system, the logistics system and the cashing system of the enterprise are researched, a chain of data flow direction of each commodity transaction (from supplier commodity feeding to allied commodity selling completion) is formed, and a data source table and a corresponding data field of each key data in the data chain process are determined.
In the embodiment of the application, the required data is accessed to an access layer of the access my data platform using the required data, and the access layer is used as a data table of the access layer.
In this embodiment of the application, supply chain data directly comes from production systems such as order system, ERP system, inventory system of enterprise, and relevant institutions acquire data and process, and data form the closed loop each other, have stopped the tradition and only have provided financial staff to process thick financial statement data, and the condition that financial staff of enterprise easily carries out the cheating through financial bore or subject adjustment's mode.
In step S203, the supply chain data is subjected to data structuring processing according to a verification algorithm, so as to obtain a structured data result.
In the embodiment of the application, a check operator is set on the accessed data, so that the accessed data is ensured to conform to the data format for the next monitoring.
In step S204, the structured data result is subjected to data cleaning processing according to the cleaning algorithm, so as to obtain monitoring wide table data.
In this embodiment of the present application, the data cleaning process refers to cleaning the verified data according to a cleaning rule, and supplementing or processing the original data with a cleaning operator to form standard numerical data meeting the operation requirement.
In this embodiment of the present application, monitoring the wide table data includes increasing or decreasing fields according to different retail enterprise conditions on specific data contents, and allowing a part of the fields to be null, which specifically includes:
(1) Allied enterprise base information: data describing basic states of an enterprise, including: business base code (marking the unique sign of the identity of the retail business), business name, business nature (allowing for individual households or individuals), business places where enterprises are located (allowed to exist in a coded manner), enterprise contact names, enterprise contact ways (contact phones), and,
(2) Upstream and logistics information of allied enterprises: enterprise affiliation time (refers to the time of an enterprise affiliation to an upstream chain management company), enterprise rating (internal rating or scoring of the enterprise at the upstream chain management company), enterprise order count, enterprise order amount, enterprise order frequency, enterprise order stability index, enterprise waste amount, enterprise return amount, enterprise main return amount, enterprise goods loss amount, enterprise violation number, and enterprise violation penalty amount;
(3) Customer and service information for the federation enterprise: describing the behaviors of enterprises and clients in the operation process, wherein the behaviors comprise developing member numbers, receiving member numbers, member consumption amounts, member pre-stored money amounts, member pre-stored account balances, member average monthly consumption amounts, receiving client numbers and client average consumption amounts;
(4) Federation enterprise business information: the enterprise revenue data is described, and comprises enterprise incomes, business orders, business profits, business tax, inventory amount, sales promotion order amount and tax-paying amount;
(5) Allied enterprise rebate information: describing that the enterprise obtains rebate information from an upstream provider, including rebate amount, rebate time, rebate use amount, rebate use time, rebate deduction amount, pre-paid rebate amount, and the like;
in the embodiment of the present application, the data of the monitoring data wide table may be classified into the following three types according to the change period:
(1) Enterprise base information: the information is relatively fixed, and only passively changes when the information occurs;
(2) Enterprise customer and service information, enterprise rebate information: change in month units;
(3) Business upstream and logistics information, business operation information: if allowed, it is preferable to be able to change in daily units;
in step S205, risk prediction processing is performed on the monitored wide table data according to the risk decision engine, so as to obtain a risk prediction result.
In the embodiment of the application, according to the risk decision rule preset by the risk decision engine, the input monitoring data is subjected to regularization processing and calculation to obtain the alarm content and level result.
In this embodiment of the present application, the calculation basis of the risk monitoring engine is a predetermined rule, where the rule includes three elements of index objects monitored by the rule, financial data matching degree, and comparison dimension, and one monitoring rule may include one index or may include multiple indexes.
In the embodiment of the present application, the index objects monitored by the rule are specifically classified into the following categories:
(1) Sales index: abnormal change of sales amount based on retail enterprises, such as decrease of sales amount by more than 15% in the same period, and fluctuation rate of sales amount relative to other retail enterprises exceeds 15%:
(2) And (3) operating cross check indexes: the quantity of the retail enterprises after adding orders and subtracting sales and losses to the monthly inventory and the system records the monthly inventory;
(3) The physical distribution index: describing that the logistics goods are not equal to the warehouse-in amount or the deviation degree exceeds 5%;
(4) Abnormal price index: describing that the commodity is delivered and the sales price is abnormal, wherein the commodity delivery price is larger than the sales price, and the commodity delivery price is larger than the sales price (after the activity);
(5) Loss index: the inventory depletion amount is greater than 10% of the total inventory amount or the depletion exceeds industry experience values.
In the embodiment of the present application, the above-mentioned financial data matching degree: the degree of matching of the supply chain data to the financial accounting data is described.
(1) Customer consumption anomaly: the average consumption amount of the clients is abnormally changed, for example, the amount is greatly increased or decreased in the same period, and the average consumption amount is greatly different from sales of other retail enterprises, has larger deviation from the past year situation, and the like;
(2) Abnormal member behavior: abnormal changes of member increase occur, including the increase or decrease of member pre-stored balance greatly in the same period;
(3) Abnormal member consumption: abnormal changes occur in member consumption, namely the consumption amount is greatly increased or decreased in the same period;
(4) Abnormal daily consumption: inventory, sales amount are abnormally changed without marketing activities;
(5) Abnormal retailers: retailers with consistent deposit and sales amounts, or retailers with consistent data such as inventory amounts, customer consumption amounts and the like exist;
(6) Abnormal rebate: the rebate total of the retail establishment is abnormal.
In the embodiment of the present application, the above-mentioned comparison dimensions can be divided into the following three categories:
(1) The same industry index: comparing the fluctuation range conditions of the same indexes in the same industry, and adopting fluctuation ratio comparison;
(2) Relative indexes of the self: comparing the fluctuation range of the same index in the same period (same month, last month, same day and last day) or the difference of the related data of the self, and adopting fluctuation ratio comparison;
(3) Absolute index of self phase: and comparing the variation data values of the same indexes in the same period (same month, same day and same day) or the difference of the related data of the same, and adopting the numerical value comparison of the amount.
In the embodiment of the present application, the risk decision engine outputs a risk level result, which is generally classified into the following 5 levels:
(1) No risk: no risk monitoring rules are triggered, meaning that there is no risk characterization on the data;
(2) Low risk: triggering a low risk monitoring rule, which means that the operation condition has certain fluctuation and needs to be observed continuously;
(3) Risk of (1): triggering a risk monitoring rule or a low risk rule of more than 10%, which means that a certain risk exists, and suggesting measures to reduce investment or cooperation;
(4) High risk: triggering a high risk monitoring rule or a medium risk rule of more than 10%, which means that an accident or a major risk event occurs in the enterprise's own operation, suggesting to suspend cooperation or requiring retail enterprises to make rectification;
(5) Extremely high risk: triggering extremely high risk monitoring rules or over 5 risk control rules means that the enterprise has serious violations or serious dilemma of operation, possibly being banked or cleared, and suggesting to stop the cooperation immediately.
In the embodiment of the application, each rule comprises three parts including an index, a comparison dimension and an output level, for example, a member behavior abnormality, a self relative index, a low risk rule forms the following rules:
The average consumption amount of the member behaviors in the current month is more than 5% and less than 7.5% compared with the average consumption amount of the member behaviors in the last month, and the risk level is judged as follows: low risk
In step S206, the risk prediction result is output to the user terminal.
In the embodiment of the application, an operation enterprise judged as a risk alarms, specifically, according to a rule of outputting a risk result, the alarm level and the content of the risk monitoring result are processed to obtain a specific alarm result, wherein the specific alarm result comprises two modules:
(1) Risk result alarm setting: setting result content to be output, including:
the specific content of the output result is as follows: if yes, outputting the index content causing the alarm, and if the calculated specific value is needed
Requirements for output results: if the low risk is not output, or 2 low risk outputs are accumulated; but high and extremely high risks do not allow for no output
(2) Risk alarm mode setting: different alarm modes are set, such as mail, instant messaging message and the like.
In an embodiment of the present application, there is provided a risk assessment method applied to a retail enterprise, including: receiving a risk assessment request sent by a user terminal, wherein the risk assessment request comprises an enterprise identifier to be assessed; reading a production system, and acquiring supply chain data corresponding to enterprise identification to be evaluated in the production system; carrying out data structuring treatment on the supply chain data according to a checking algorithm to obtain a structured data result; carrying out data cleaning treatment on the structured data result according to a cleaning algorithm to obtain monitoring wide table data; performing risk prediction processing on the monitoring wide-table data according to the risk decision engine to obtain a risk prediction result; and outputting a risk prediction result to the user terminal. Compared with the prior art, the supply chain data of the application are directly derived from production systems such as an order system, an ERP system and an inventory system of an enterprise, related institutions acquire data and then process the data, the data form a closed loop, the situation that traditional financial staff only process thick financial statement data is stopped, and the financial staff of the enterprise easily cheats in a financial caliber or subject adjustment mode is avoided, the reliability of the data is effectively improved, in addition, the application performs risk prediction processing by monitoring wide-table data through a risk decision engine, the traditional implementation means depending on manpower is abandoned, the processing efficiency is greatly improved, and the possible operation risk of a retail enterprise can be timely found.
With continued reference to fig. 3, a flowchart of one embodiment of step S203 in fig. 2 is shown, only the portions relevant to the present application being shown for ease of illustration.
In some optional implementations of this embodiment, step S203 specifically includes: step S301, step S302, step S303, and step S304.
In step S301, non-null confirmation processing is performed on the key field of the supply chain data according to a non-null verification algorithm, so as to obtain a non-null data result;
in step S302, performing a numerical range verification process on a numerical field of the supply chain data according to a numerical range verification algorithm to obtain a numerical range data result;
in step S303, performing outlier confirmation processing on the type field of the supply chain data according to the data outlier checking algorithm to obtain an outlier data result;
in step S304, the numerical range data result, the correct data of the numerical range data result, and the abnormal value data result are taken as the structured data result.
In the embodiment of the application, the check operator set on the accessed data includes the following three types:
(1) Non-empty checking: null values are not allowed for a certain field;
(2) Checking a numerical range: the data of a certain field can be checked within a certain range, such as a numerical value of 0-100, and the date cannot exceed 2049 years, 12 months, 31 days, and the like;
(3) Checking the abnormal value of data: and checking the allowed field content of a certain field, such as disallowing the occurrence of text, or symbols, and the like.
With continued reference to fig. 4, a flowchart of one embodiment after step S303 in fig. 3 is shown, and for ease of illustration, only portions relevant to the present application are shown.
In some optional implementations of the present embodiment, after step S303, further includes: step S401 and step S402.
In step S401, calculating the error data total amount of the numerical range data result, and the abnormal value data result;
in step S402, if the total amount of error data is greater than the preset threshold of the total supply chain data on the same day, a data risk warning signal is output to the user terminal.
In the embodiment of the application, the operator checking result is output:
(1) For the error data which is not in accordance with the requirements and checked by the check operator, entering a check failure flow, and alarming to inform that the data has risks when the total data quantity which is in failure of the check exceeds the data quantity which is acquired on the same day or is 5% of the total data quantity;
(2) And the data successfully checked by the operator enter the next monitoring data processing and generating process as the data input of the process.
The data research and selection form the precondition of data extraction and access and design check operator, and are not reflected in the system processing process.
With continued reference to fig. 5, a flowchart of one embodiment of step S204 of fig. 2 is shown, only the portions relevant to the present application being shown for ease of illustration.
In some optional implementations of the present embodiment, step S204 specifically includes: step S501, step S502, and step S503.
In step S501, if the structured data result has a null field, filling a preset default value in the null field;
in step S502, if the structured data result has an abnormal value, performing a value correction process on the abnormal value;
in step S503, a numerical conversion operation is performed on the structured data result according to a preset conversion rule, so as to obtain monitoring broad-table data.
In the embodiment of the application, the data cleaning includes the following three types:
(1) Filling a numerical operator: for the data field which is empty, automatically supplementing a default value according to a rule; if the null value fields are all filled in as 0, the profit amount is calculated according to four fields of sales amount-commodity amount-management fee-tax payment, etc.;
(2) Correcting a numerical operator: correcting the numerical values which do not meet the business experience but meet the result requirement of the check operator in the last step; if the value of the 'number of the monthly logistics orders' field limit exceeds 100000, the value is set as 10000;
(3) Converting a numerical operator: converting the numerical value according to a specified rule; such as the "established years" field ranging from 0-3, 1, 4-6, 2, etc.
Those skilled in the art will appreciate that implementing all or part of the processes of the methods of the embodiments described above may be accomplished by way of computer readable instructions, stored on a computer readable storage medium, which when executed may comprise processes of embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
Example two
With further reference to fig. 6, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a risk assessment apparatus for use in a retail enterprise, where the apparatus embodiment corresponds to the method embodiment shown in fig. 2, and the apparatus is particularly applicable to various electronic devices.
As shown in fig. 7, the risk assessment apparatus 200 applied to a retail business of the present embodiment includes: a request receiving module 210, a data acquisition module 220, a data structuring module 230, a data cleansing module 240, a risk prediction module 250, and a result output module 260. Wherein:
a request receiving module 210, configured to receive a risk assessment request sent by a user terminal, where the risk assessment request includes an enterprise identifier to be assessed;
the data acquisition module 220 is configured to read a production system, and acquire supply chain data corresponding to an enterprise identifier to be evaluated in the production system;
the data structuring module 230 is configured to perform data structuring processing on the supply chain data according to a verification algorithm to obtain a structured data result;
the data cleaning module 240 is configured to perform data cleaning processing on the structured data result according to a cleaning algorithm to obtain monitoring wide table data;
The risk prediction module 250 is configured to perform risk prediction processing on the monitored wide table data according to the risk decision engine to obtain a risk prediction result;
and the result output module 260 is configured to output the risk prediction result to the user terminal.
In the embodiments of the present application, the user terminal may be a mobile terminal such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, etc., and a fixed terminal such as a digital TV, a desktop computer, etc., it should be understood that the examples of the user terminal are only for convenience of understanding and are not intended to limit the present invention.
In the embodiment of the present application, the business identifier is mainly used for uniquely identifying a retail business, and the business identifier may be set based on the pinyin initials of the business name, for example: the enterprise identifier of the Meiyijia enterprise is MYJ; the enterprise identity may also be set based on a serial number, such as, for example: 001; the business identifier may also be set in combination based on the pinyin initials and serial numbers of the business name, for example: the business identifier of the mei jia business is MYJ001, and it should be understood that the example of the business identifier herein is merely for convenience of understanding and is not intended to limit the present application.
In the embodiment of the application, the enterprise identifier to be evaluated is mainly used for identifying the enterprise object to be evaluated required by the request.
In the present embodiment, the production system is primarily used to store supply chain data for each retail business, where the production system includes an order system, ERP (Enterprise Resource Planning) system, inventory system, and the like.
In the embodiment of the application, the business mode of the retail enterprise needs to be researched, the operation mode and the data system of the retail enterprise are clarified, the data of the order system, the ERP system, the logistics system and the cashing system of the enterprise are researched, a chain of data flow direction of each commodity transaction (from supplier commodity feeding to allied commodity selling completion) is formed, and a data source table and a corresponding data field of each key data in the data chain process are determined.
In the embodiment of the application, the required data is accessed to an access layer of the access my data platform using the required data, and the access layer is used as a data table of the access layer.
In this embodiment of the application, supply chain data directly comes from production systems such as order system, ERP system, inventory system of enterprise, and relevant institutions acquire data and process, and data form the closed loop each other, have stopped the tradition and only have provided financial staff to process thick financial statement data, and the condition that financial staff of enterprise easily carries out the cheating through financial bore or subject adjustment's mode.
In the embodiment of the application, a check operator is set on the accessed data, so that the accessed data is ensured to conform to the data format for the next monitoring.
In this embodiment of the present application, the data cleaning process refers to cleaning the verified data according to a cleaning rule, and supplementing or processing the original data with a cleaning operator to form standard numerical data meeting the operation requirement.
In this embodiment of the present application, monitoring the wide table data includes increasing or decreasing fields according to different retail enterprise conditions on specific data contents, and allowing a part of the fields to be null, which specifically includes:
(1) Allied enterprise base information: data describing basic states of an enterprise, including: business base code (marking the unique sign of the identity of the retail business), business name, business nature (allowing for individual households or individuals), business places where enterprises are located (allowed to exist in a coded manner), enterprise contact names, enterprise contact ways (contact phones), and,
(2) Upstream and logistics information of allied enterprises: enterprise affiliation time (refers to the time of an enterprise affiliation to an upstream chain management company), enterprise rating (internal rating or scoring of the enterprise at the upstream chain management company), enterprise order count, enterprise order amount, enterprise order frequency, enterprise order stability index, enterprise waste amount, enterprise return amount, enterprise main return amount, enterprise goods loss amount, enterprise violation number, and enterprise violation penalty amount;
(3) Customer and service information for the federation enterprise: describing the behaviors of enterprises and clients in the operation process, wherein the behaviors comprise developing member numbers, receiving member numbers, member consumption amounts, member pre-stored money amounts, member pre-stored account balances, member average monthly consumption amounts, receiving client numbers and client average consumption amounts;
(4) Federation enterprise business information: the enterprise revenue data is described, and comprises enterprise incomes, business orders, business profits, business tax, inventory amount, sales promotion order amount and tax-paying amount;
(5) Allied enterprise rebate information: describing that the enterprise obtains rebate information from an upstream provider, including rebate amount, rebate time, rebate use amount, rebate use time, rebate deduction amount, pre-paid rebate amount, and the like;
in the embodiment of the present application, the data of the monitoring data wide table may be classified into the following three types according to the change period:
(1) Enterprise base information: the information is relatively fixed, and only passively changes when the information occurs;
(2) Enterprise customer and service information, enterprise rebate information: change in month units;
(3) Business upstream and logistics information, business operation information: if allowed, it is preferable to be able to change in daily units;
In the embodiment of the application, according to the risk decision rule preset by the risk decision engine, the input monitoring data is subjected to regularization processing and calculation to obtain the alarm content and level result.
In this embodiment of the present application, the calculation basis of the risk monitoring engine is a predetermined rule, where the rule includes three elements of index objects monitored by the rule, financial data matching degree, and comparison dimension, and one monitoring rule may include one index or may include multiple indexes.
In the embodiment of the present application, the index objects monitored by the rule are specifically classified into the following categories:
(1) Sales index: abnormal change of sales amount based on retail enterprises, such as decrease of sales amount by more than 15% in the same period, and fluctuation rate of sales amount relative to other retail enterprises exceeds 15%:
(2) And (3) operating cross check indexes: the quantity of the retail enterprises after adding orders and subtracting sales and losses to the monthly inventory and the system records the monthly inventory;
(3) The physical distribution index: describing that the logistics goods are not equal to the warehouse-in amount or the deviation degree exceeds 5%;
(4) Abnormal price index: describing that the commodity is delivered and the sales price is abnormal, wherein the commodity delivery price is larger than the sales price, and the commodity delivery price is larger than the sales price (after the activity);
(5) Loss index: the inventory depletion amount is greater than 10% of the total inventory amount or the depletion exceeds industry experience values.
In the embodiment of the present application, the above-mentioned financial data matching degree: the degree of matching of the supply chain data to the financial accounting data is described.
(1) Customer consumption anomaly: the average consumption amount of the clients is abnormally changed, for example, the amount is greatly increased or decreased in the same period, and the average consumption amount is greatly different from sales of other retail enterprises, has larger deviation from the past year situation, and the like;
(2) Abnormal member behavior: abnormal changes of member increase occur, including the increase or decrease of member pre-stored balance greatly in the same period;
(3) Abnormal member consumption: abnormal changes occur in member consumption, namely the consumption amount is greatly increased or decreased in the same period;
(4) Abnormal daily consumption: inventory, sales amount are abnormally changed without marketing activities;
(5) Abnormal retailers: retailers with consistent deposit and sales amounts, or retailers with consistent data such as inventory amounts, customer consumption amounts and the like exist;
(6) Abnormal rebate: the rebate total of the retail establishment is abnormal.
In the embodiment of the present application, the above-mentioned comparison dimensions can be divided into the following three categories:
(1) The same industry index: comparing the fluctuation range conditions of the same indexes in the same industry, and adopting fluctuation ratio comparison;
(2) Relative indexes of the self: comparing the fluctuation range of the same index in the same period (same month, last month, same day and last day) or the difference of the related data of the self, and adopting fluctuation ratio comparison;
(3) Absolute index of self phase: and comparing the variation data values of the same indexes in the same period (same month, same day and same day) or the difference of the related data of the same, and adopting the numerical value comparison of the amount.
In the embodiment of the present application, the risk decision engine outputs a risk level result, which is generally classified into the following 5 levels:
(1) No risk: no risk monitoring rules are triggered, meaning that there is no risk characterization on the data;
(2) Low risk: triggering a low risk monitoring rule, which means that the operation condition has certain fluctuation and needs to be observed continuously;
(3) Risk of (1): triggering a risk monitoring rule or a low risk rule of more than 10%, which means that a certain risk exists, and suggesting measures to reduce investment or cooperation;
(4) High risk: triggering a high risk monitoring rule or a medium risk rule of more than 10%, which means that an accident or a major risk event occurs in the enterprise's own operation, suggesting to suspend cooperation or requiring retail enterprises to make rectification;
(5) Extremely high risk: triggering extremely high risk monitoring rules or over 5 risk control rules means that the enterprise has serious violations or serious dilemma of operation, possibly being banked or cleared, and suggesting to stop the cooperation immediately.
In the embodiment of the application, each rule comprises three parts including an index, a comparison dimension and an output level, for example, a member behavior abnormality, a self relative index, a low risk rule forms the following rules:
the average consumption amount of the member behaviors in the current month is more than 5% and less than 7.5% compared with the average consumption amount of the member behaviors in the last month, and the risk level is judged as follows: low risk
In the embodiment of the application, an operation enterprise judged as a risk alarms, specifically, according to a rule of outputting a risk result, the alarm level and the content of the risk monitoring result are processed to obtain a specific alarm result, wherein the specific alarm result comprises two modules:
(1) Risk result alarm setting: setting result content to be output, including:
the specific content of the output result is as follows: if yes, outputting the index content causing the alarm, and if the calculated specific value is needed
Requirements for output results: if the low risk is not output, or 2 low risk outputs are accumulated; but high and extremely high risks do not allow for no output
(2) Risk alarm mode setting: different alarm modes are set, such as mail, instant messaging message and the like.
In an embodiment of the present application, there is provided a risk assessment apparatus 200 applied to a retail enterprise, including: a request receiving module 210, configured to receive a risk assessment request sent by a user terminal, where the risk assessment request includes an enterprise identifier to be assessed; the data acquisition module 220 is configured to read a production system, and acquire supply chain data corresponding to an enterprise identifier to be evaluated in the production system; the data structuring module 230 is configured to perform data structuring processing on the supply chain data according to a verification algorithm to obtain a structured data result; the data cleaning module 240 is configured to perform data cleaning processing on the structured data result according to a cleaning algorithm to obtain monitoring wide table data; the risk prediction module 250 is configured to perform risk prediction processing on the monitored wide table data according to the risk decision engine to obtain a risk prediction result; and the result output module 260 is configured to output the risk prediction result to the user terminal. Compared with the prior art, the supply chain data of the application are directly derived from production systems such as an order system, an ERP system and an inventory system of an enterprise, related institutions acquire data and then process the data, the data form a closed loop, the situation that traditional financial staff only process thick financial statement data is stopped, and the financial staff of the enterprise easily cheats in a financial caliber or subject adjustment mode is avoided, the reliability of the data is effectively improved, in addition, the application performs risk prediction processing by monitoring wide-table data through a risk decision engine, the traditional implementation means depending on manpower is abandoned, the processing efficiency is greatly improved, and the possible operation risk of a retail enterprise can be timely found.
In some optional implementations of this embodiment, the data structuring module includes:
the non-empty confirmation sub-module is used for carrying out non-empty confirmation processing on key fields of the supply chain data according to a non-empty verification algorithm to obtain a non-empty data result;
the numerical range confirmation sub-module is used for carrying out numerical range confirmation processing on the numerical field of the supply chain data according to a numerical range verification algorithm to obtain a numerical range data result;
the abnormal value confirmation sub-module is used for carrying out abnormal value confirmation processing on the type field of the supply chain data according to the data abnormal value correction algorithm to obtain an abnormal value data result;
and the structured data confirmation sub-module is used for taking the numerical range data result, the numerical range data result and correct data of the abnormal value data result as structured data results.
In some optional implementations of this embodiment, the data structuring module further includes:
the error data total calculation operator module is used for calculating error data total of the numerical range data result, the numerical range data result and the abnormal value data result;
and the warning signal output sub-module is used for outputting a data risk warning signal to the user terminal if the total error data amount is larger than a preset threshold value of the total supply chain data of the current day.
In some optional implementations of the present embodiment, the risk assessment apparatus 100 applied to a retail enterprise further includes:
the null value filling module is used for filling a preset default value in the null value if the null value field exists in the structured data result;
the numerical value correction module is used for carrying out numerical value correction processing on the abnormal numerical value if the structured data result has the abnormal numerical value;
and the numerical conversion module is used for carrying out numerical conversion operation on the structured data result according to a preset conversion rule to obtain the monitoring wide table data.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 7, fig. 7 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 300 includes a memory 310, a processor 320, and a network interface 330 communicatively coupled to each other via a system bus. It should be noted that only computer device 300 having components 310-330 is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 310 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 310 may be an internal storage unit of the computer device 300, such as a hard disk or a memory of the computer device 300. In other embodiments, the memory 310 may also be an external storage device of the computer device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 300. Of course, the memory 310 may also include both internal storage units and external storage devices of the computer device 300. In this embodiment, the memory 310 is typically used to store an operating system and various types of application software installed on the computer device 300, such as computer readable instructions for a risk assessment method applied to a retail enterprise. In addition, the memory 310 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 320 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 320 is generally used to control the overall operation of the computer device 300. In this embodiment, the processor 320 is configured to execute computer readable instructions stored in the memory 310 or process data, such as executing computer readable instructions of the risk assessment method applied to a retail enterprise.
The network interface 330 may include a wireless network interface or a wired network interface, the network interface 330 typically being used to establish communication connections between the computer device 300 and other electronic devices.
The computer equipment that this application provided, supply chain data directly derive from production systems such as order system, ERP system, inventory system of enterprise, processing after relevant mechanism obtains the data, and the data forms the closed loop each other, stopped that traditional only provides financial staff and processes thick financial statement data, and the financial staff of enterprise carries out the condition of cheating through financial bore or subject adjustment's mode easily, effectively improve the reliability of data, in addition, this application is through risk decision engine control wide table data carrying out risk prediction processing, abandon traditional and rely on artificial implementation means, the treatment effeciency promotes greatly, can in time discover the possible business risk of retail enterprises.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of a risk assessment method as described above for a retail enterprise.
The application provides a computer readable storage medium, supply chain data directly comes from the production systems such as order system, ERP system, inventory system of enterprise, processing after relevant institution obtains the data, and the data forms the closed loop each other, traditional financial statement data that only provides financial staff processing thickness has been stopped, and the condition that financial staff of enterprise carries out the cheating through financial bore or subject adjustment's mode easily, effectively improve the reliability of data, in addition, this application is through risk decision engine control wide table data carrying out risk prediction processing, abandon traditional and rely on artificial implementation means, the treatment effeciency promotes greatly, can discover the possible business risk of retail enterprise in time.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (10)
1. A risk assessment method for a retail establishment, comprising the steps of:
receiving a risk assessment request sent by a user terminal, wherein the risk assessment request comprises an enterprise identifier to be assessed;
reading a production system, and acquiring supply chain data corresponding to the enterprise identifier to be evaluated in the production system;
Carrying out data structuring treatment on the supply chain data according to a checking algorithm to obtain a structured data result;
performing data cleaning treatment on the structured data result according to a cleaning algorithm to obtain monitoring wide-table data;
performing risk prediction processing on the monitoring wide table data according to a risk decision engine to obtain a risk prediction result;
and outputting the risk prediction result to the user terminal.
2. The risk assessment method for a retail enterprise according to claim 1, wherein the step of performing data structuring processing on the supply chain data according to a verification algorithm to obtain structured data results comprises the steps of:
carrying out non-empty confirmation processing on the key field of the supply chain data according to a non-empty verification algorithm to obtain a non-empty data result;
performing numerical range confirmation processing on the numerical field of the supply chain data according to a numerical range verification algorithm to obtain a numerical range data result;
performing outlier confirmation processing on the type field of the supply chain data according to a data outlier checking algorithm to obtain an outlier data result;
and taking the correct data of the numerical range data result, the numerical range data result and the abnormal value data result as the structured data result.
3. The risk assessment method for a retail business according to claim 2, further comprising, after the step of performing outlier confirmation processing on the type field of the supply chain data according to a data outlier checking algorithm to obtain an outlier data result, the steps of:
calculating the total error data amount of the numerical range data result, the numerical range data result and the abnormal value data result;
and if the total error data amount is larger than a preset threshold value of the total supply chain data of the current day, outputting a data risk warning signal to the user terminal.
4. The risk assessment method for retail enterprises according to claim 1, wherein the step of performing data cleaning processing on the structured data result according to a cleaning algorithm to obtain monitored broad-table data comprises the following steps:
if the structured data result has a null field, filling a preset default value in the null field;
if the structured data result has abnormal values, carrying out value correction processing on the abnormal values;
and carrying out numerical conversion operation on the structured data result according to a preset conversion rule to obtain the monitoring wide table data.
5. A risk assessment apparatus for use in a retail establishment, comprising:
the system comprises a request receiving module, a risk assessment module and a risk assessment module, wherein the request receiving module is used for receiving a risk assessment request sent by a user terminal, and the risk assessment request comprises an enterprise identifier to be assessed;
the data acquisition module is used for reading a production system and acquiring supply chain data corresponding to the enterprise identifier to be evaluated in the production system;
the data structuring module is used for carrying out data structuring processing on the supply chain data according to a checking algorithm to obtain a structured data result;
the data cleaning module is used for performing data cleaning processing on the structured data result according to a cleaning algorithm to obtain monitoring wide-table data;
the risk prediction module is used for performing risk prediction processing on the monitoring wide table data according to a risk decision engine to obtain a risk prediction result;
and the result output module is used for outputting the risk prediction result to the user terminal.
6. The risk assessment device for use in a retail establishment of claim 5, wherein the data structuring module comprises:
the non-empty confirmation sub-module is used for carrying out non-empty confirmation processing on the key field of the supply chain data according to a non-empty verification algorithm to obtain a non-empty data result;
The numerical range confirmation sub-module is used for carrying out numerical range confirmation processing on the numerical field of the supply chain data according to a numerical range verification algorithm to obtain a numerical range data result;
the abnormal value confirmation sub-module is used for carrying out abnormal value confirmation processing on the type field of the supply chain data according to a data abnormal value correction algorithm to obtain an abnormal value data result;
and the structured data confirmation sub-module is used for taking the numerical range data result, the numerical range data result and correct data of the abnormal value data result as the structured data result.
7. The risk assessment device for use in a retail establishment of claim 6, wherein the data structuring module further comprises:
an error data total calculation operator module, configured to calculate an error data total amount of the numerical range data result, and the outlier data result;
and the warning signal output sub-module is used for outputting a data risk warning signal to the user terminal if the total error data amount is larger than a preset threshold value of the total supply chain data of the current day.
8. The risk assessment device for use in a retail establishment of claim 5, further comprising:
The null value filling module is used for filling a preset default value in the null value if the null value field exists in the structured data result;
the numerical value correction module is used for carrying out numerical value correction processing on the abnormal numerical value if the structured data result has the abnormal numerical value;
and the numerical conversion module is used for carrying out numerical conversion operation on the structured data result according to a preset conversion rule to obtain the monitoring wide table data.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the risk assessment method of any of claims 1 to 4 for use in a retail establishment.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the risk assessment method of any of claims 1 to 4 applied to a retail establishment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310200118.8A CN116245634A (en) | 2023-03-03 | 2023-03-03 | Risk assessment method, apparatus, device and storage medium applied to retail enterprises |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310200118.8A CN116245634A (en) | 2023-03-03 | 2023-03-03 | Risk assessment method, apparatus, device and storage medium applied to retail enterprises |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116245634A true CN116245634A (en) | 2023-06-09 |
Family
ID=86632885
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310200118.8A Pending CN116245634A (en) | 2023-03-03 | 2023-03-03 | Risk assessment method, apparatus, device and storage medium applied to retail enterprises |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116245634A (en) |
-
2023
- 2023-03-03 CN CN202310200118.8A patent/CN116245634A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10867304B2 (en) | Account type detection for fraud risk | |
| US9886686B2 (en) | Method for using supervised model to identify user | |
| US10467631B2 (en) | Ranking and tracking suspicious procurement entities | |
| Dedman et al. | The demand for audit in private firms: recent large-sample evidence from the UK | |
| Qi et al. | A continuous‐review inventory model with disruptions at both supplier and retailer | |
| US20160292690A1 (en) | Risk manager optimizer | |
| US20150046307A1 (en) | Item level personal finance management (pfm) for discretionary and non-discretionary spending | |
| US8234186B2 (en) | Inventory location management | |
| US9129321B2 (en) | Fraud detection system audit capability | |
| US11037160B1 (en) | Systems and methods for preemptive fraud alerts | |
| CN112836964B (en) | Enterprise abnormity evaluation system and method | |
| CN112598513B (en) | Method and device for identifying stockholder risk transaction behaviors | |
| Lange et al. | Long-term market implications of data breaches, not | |
| CN109360082A (en) | A kind of method for processing business of loan system, device, computer equipment and storage medium | |
| CN118369885A (en) | System and method for improved network attack detection | |
| CN110599082A (en) | Method for providing a negotiable service by interfacing an initial supplier and a buyer via an intermediate negotiable provider | |
| Li et al. | Strategic rationing and freshness keeping of perishable products under transportation disruptions and demand learning | |
| CN117273968A (en) | Accounting document generation method of cross-business line product and related equipment thereof | |
| US20140114816A1 (en) | Multidimensional measurement basis for accounting systems | |
| CN116245634A (en) | Risk assessment method, apparatus, device and storage medium applied to retail enterprises | |
| CN114331463A (en) | Risk identification method based on linear regression model and related equipment thereof | |
| KR20150097838A (en) | Pattern generating system for account recommendation statement data generation and providing method thereof | |
| US20110022406A1 (en) | Apparatus and method for providing security in an online gaming and/or digital environment | |
| CN114119195A (en) | Cross-border e-commerce data asset management method and device, computer equipment and medium | |
| Lin | The measurement of zombie enterprise under information asymmetry–a case of China’s iron and steel industry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |