CN116385137A - Enterprise anti-fraud risk assessment method and system based on power information data - Google Patents
Enterprise anti-fraud risk assessment method and system based on power information data Download PDFInfo
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Abstract
The invention discloses an enterprise anti-fraud risk assessment system based on power information data, which comprises a risk early warning index library, wherein the risk early warning index library comprises an ammeter power consumption data module, a user history payment data module, a user history checking data module, customer power reservation information and enterprise power data; the risk early warning index library screens the information to construct a financial post-loan operation risk evaluation model; the financial post-credit operational risk assessment model comprises a post-credit financial verification model and a post-credit operational risk scoring model. The invention adopts an integrated model to determine a post-loan operation risk monitoring model based on enterprise power related data, adopts a real-time data docking mode to open up various behavior data related to the enterprise power, and forms a modularized, expandable and standardized post-loan scoring early-warning system of the enterprise based on post-loan operation analysis and monitoring of the enterprise power data.
Description
Technical Field
The invention relates to the technical field of financial risk monitoring, in particular to an enterprise anti-fraud risk assessment method and system based on electric power information data.
Background
Anti-fraud is a service that identifies fraud including transaction fraud, phishing, telephone fraud, card theft, number theft, etc. Online anti-fraud is an essential part of internet finance, and common anti-fraud systems are: a user behavior risk recognition engine, a credit investigation system, a blacklist system and the like. On the one hand, the electric power is used as a necessary resource for enterprise operation, and when an enterprise company registers and an enterprise bank opens an account, corresponding enterprise information can be subjected to auxiliary verification according to the reserved information of the electric power information system. On the other hand, the development and innovation of financial business puts forward higher requirements for enterprise identity authentication and verification. The traditional finance has certain shortages to public business and the risk management exposes a plurality of defects, and severely restricts the development of related business. Traditional banks search for the development cooperation with various enterprises with data, search for data capable of effectively restoring the actual operation conditions of the enterprises, improve the management and control of risks in the public business process by utilizing a big data modeling technology, keep the balanced development of risks and benefits, and provide a higher safety boundary for public financial services. In the traditional banking business, the information checking of the enterprise lacks an effective online approach, and corresponding work is often carried out in a mode of checking by a client manager and the like.
With the further development of B-side services, fraud approaches have emerged more specialized. In order to develop corresponding business, specialized institutions are commissioned to cooperate so as to meet corresponding supervision requirements, and a great challenge is provided for bank fund safety and money back-flushing monitoring. It can be said that building a new scenario based on big data of enterprise power will be the development direction of credit wind control modeling technology.
In the traditional bank credit system, corresponding information checking and monitoring are carried out on public businesses, and the method is mainly based on due-job investigation reports carried out by the businesses before corresponding business handling, has limitation on understanding the actual business of the businesses, is low in fake-making cost of clients, and lacks timely and effective prevention means for potential business risks (empty businesses) of the businesses.
The enterprise anti-fraud risk assessment method based on the power information data in the prior art has the following defects:
the current enterprise type financial business entity information verification mainly adopts an off-line business personnel on-site investigation mode to carry out corresponding work, and then corresponding data information is summarized and fed back to corresponding departments. The traditional technical mode has the characteristics of long period, large manpower investment, poor information real-time performance and the like, lacks cross verification of multiparty information, has larger subjectivity opinion in the business current state evaluation of enterprises, and lacks a relatively standard online risk approval verification system.
Disclosure of Invention
The invention aims to provide an enterprise anti-fraud risk assessment method and system based on power information data, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the enterprise anti-fraud risk assessment system based on the power information data comprises a risk early warning index library, wherein the risk early warning index library comprises an ammeter power consumption data module, a user history payment data module, a user history checking data module, customer power reservation information and enterprise power data;
the risk early warning index library screens the information to construct a financial post-loan operation risk evaluation model;
the financial post-credit operational risk assessment model comprises a post-credit financial verification model and a post-credit operational risk scoring model.
Preferably, the post-loan financial verification model and the post-loan operation risk scoring model construct an enterprise financial post-loan scoring early warning system.
Preferably, the customer power reservation information, the enterprise power data and other big data establish different risk dimension index libraries, and divide modules representing different risk dimensions, and in each module, the risk early warning index library performs univariate analysis through financial tags of enterprise customers to calculate the distinguishing capability of each index on good and bad people, so as to screen out indexes with business applicability, economic meaning compliance and better statistical characteristics into a model;
based on the modeling index determined by univariate analysis, analyzing the relation between the variable combination and risk of the module, and determining a financial post-loan operation risk monitoring model based on enterprise power related data by using a machine learning algorithm and an integrated model;
by adopting a real-time data docking mode, various behavior data related to the electric power of the enterprise are communicated, and a modularized, expandable and standardized enterprise post-financial credit scoring early warning system based on post-financial credit operation analysis and monitoring of the electric power data of the enterprise is formed.
Preferably, the method of the enterprise anti-fraud risk assessment system specifically comprises the following steps:
step one, collecting ammeter electricity utilization data, user history payment data, user history checking data and the like of enterprise users;
step two, constructing a risk early warning index library based on financial business scenes according to the related data of enterprises, and realizing model index business and scene;
step three, combining the financial business labels of enterprises, carrying out feature screening on the risk early warning index library constructed in the step two, selecting indexes with stronger differentiation degree of the financial business labels, carrying out model training, respectively constructing post-loan operation risk evaluation models, wherein the post-loan operation risk evaluation models are respectively composed of post-loan financial verification models and post-loan operation risk scoring models;
firstly, setting a threshold interval for a verification model;
secondly, for the post-loan operation risk scoring model, adopting a machine learning mode to generate index weights, module weights and the like required by the model;
finally, a post-loan grading early warning system of the enterprise finance is constructed by the verification model and the post-loan operation risk grading model;
step four, acquiring various data required in the step three in real time through an internal interface system, and providing data preparation for corresponding calculation;
step five, according to the model generated by training in the step three, corresponding parameters are deployed in corresponding computing environments, and real-time monitoring is provided for the financial post-loan operation risk of the enterprise by combining the real-time data prepared in the step four;
and step six, providing real-time monitoring service for the financial institutions according to the real-time monitoring in the step five.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, different risk dimension index libraries are established through big data such as customer power reservation information and enterprise power data, modules representing different risk dimensions are divided, in each module, the risk early warning index library performs univariate analysis to calculate the distinguishing capability of each index to good and bad people through financial labels of enterprise customers, so that indexes with business applicability, meeting economic meanings and good statistical characteristics enter a model, the relation between the variable combination of the modules and risks is analyzed based on the modeling indexes determined by univariate analysis, a machine learning algorithm is utilized, an integrated model is adopted to determine a financial post-loan operation risk monitoring model based on enterprise power related data, various behavior data related to the enterprise is opened up by adopting a real-time data butt joint mode, and a modularized, expandable and standardized post-loan enterprise grading early warning system based on post-loan operation analysis and monitoring of the enterprise power data is formed.
Drawings
FIG. 1 is a diagram of the steps of a method of the enterprise anti-fraud risk assessment system of the present invention;
FIG. 2 is a risk early warning index library of the present invention;
fig. 3 is a diagram of a post-loan operation risk assessment model of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", etc., are directions or positional relationships based on the drawings, are merely for convenience of description of the present invention and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a specific direction, be configured and operated in a specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "provided," "connected," and the like are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Referring to fig. 1, 2 and 3, an embodiment of the present invention provides an enterprise anti-fraud risk assessment system based on power information data, which includes a risk early warning index library, wherein the risk early warning index library includes an ammeter power consumption data module, a user history payment data module, a user history inspection data module, customer power reservation information and enterprise power data;
the risk early warning index library screens the information to construct a financial post-loan operation risk evaluation model;
the financial post-credit operational risk assessment model comprises a post-credit financial verification model and a post-credit operational risk scoring model.
And the post-loan financial verification model and the post-loan operation risk scoring model construct an enterprise financial post-loan scoring early warning system.
Further, the customer power reservation information, the enterprise power data and other big data establish different risk dimension index libraries, and the modules representing different risk dimensions are divided, and in each module, the risk early warning index library performs univariate analysis through financial labels of enterprise customers to calculate the distinguishing capability of each index on good and bad people, so that indexes with business applicability, economic meaning compliance and better statistical characteristics are screened out and enter a model;
based on the modeling index determined by univariate analysis, analyzing the relation between the variable combination and risk of the module, and determining a financial post-loan operation risk monitoring model based on enterprise power related data by using a machine learning algorithm and an integrated model;
by adopting a real-time data docking mode, various behavior data related to the electric power of the enterprise are communicated, and a modularized, expandable and standardized enterprise post-financial credit scoring early warning system based on post-financial credit operation analysis and monitoring of the electric power data of the enterprise is formed.
Further, the method of the enterprise anti-fraud risk assessment system comprises the following specific steps:
step one, collecting ammeter electricity utilization data, user history payment data, user history checking data and the like of enterprise users;
step two, constructing a risk early warning index library based on financial business scenes according to the related data of enterprises, and realizing model index business and scene;
step three, combining the financial business labels of enterprises, carrying out feature screening on the risk early warning index library constructed in the step two, selecting indexes with stronger differentiation degree of the financial business labels, carrying out model training, respectively constructing post-loan operation risk evaluation models, wherein the post-loan operation risk evaluation models are respectively composed of post-loan financial verification models and post-loan operation risk scoring models;
firstly, setting a threshold interval for a verification model;
secondly, for the post-loan operation risk scoring model, adopting a machine learning mode to generate index weights, module weights and the like required by the model;
finally, a post-loan grading early warning system of the enterprise finance is constructed by the verification model and the post-loan operation risk grading model;
step four, acquiring various data required in the step three in real time through an internal interface system, and providing data preparation for corresponding calculation;
step five, according to the model generated by training in the step three, corresponding parameters are deployed in corresponding computing environments, and real-time monitoring is provided for the financial post-loan operation risk of the enterprise by combining the real-time data prepared in the step four;
and step six, providing real-time monitoring service for the financial institutions according to the real-time monitoring in the step five.
In a first embodiment, the method of the enterprise anti-fraud risk assessment system specifically includes the following steps:
step one, collecting ammeter electricity utilization data, user history payment data, user history checking data and the like of enterprise users;
step two, constructing a risk early warning index library based on financial business scenes according to the related data of enterprises, and realizing model index business and scene;
step three, combining the financial business labels of enterprises, carrying out feature screening on the risk early warning index library constructed in the step two, selecting indexes with stronger differentiation degree of the financial business labels, carrying out model training, respectively constructing post-loan operation risk evaluation models, wherein the post-loan operation risk evaluation models are respectively composed of post-loan financial verification models and post-loan operation risk scoring models;
firstly, setting a threshold interval for a verification model;
secondly, for the post-loan operation risk scoring model, adopting a machine learning mode to generate index weights, module weights and the like required by the model;
finally, a post-loan grading early warning system of the enterprise finance is constructed by the verification model and the post-loan operation risk grading model;
step four, acquiring various data required in the step three in real time through an internal interface system, and providing data preparation for corresponding calculation;
step five, according to the model generated by training in the step three, corresponding parameters are deployed in corresponding computing environments, real-time monitoring is provided for the post-financial-loan operation risk of the enterprise by combining the real-time data prepared in the step four, and early warning signals are sent out after the financial loan is stored;
and step six, providing real-time monitoring service for the financial institutions according to the real-time monitoring in the step five.
In a second embodiment, the method of the enterprise anti-fraud risk assessment system specifically includes the following steps:
step one, collecting ammeter electricity utilization data, user history payment data, user history checking data and the like of enterprise users;
step two, constructing a risk early warning index library based on financial business scenes according to the related data of enterprises, and realizing model index business and scene;
step three, combining the financial business labels of enterprises, carrying out feature screening on the risk early warning index library constructed in the step two, selecting indexes with stronger differentiation degree of the financial business labels, carrying out model training, respectively constructing post-loan operation risk evaluation models, wherein the post-loan operation risk evaluation models are respectively composed of post-loan financial verification models and post-loan operation risk scoring models;
firstly, setting a threshold interval for a verification model;
secondly, for the post-loan operation risk scoring model, adopting a machine learning mode to generate index weights, module weights and the like required by the model;
finally, a post-loan grading early warning system of the enterprise finance is constructed by the verification model and the post-loan operation risk grading model;
step four, acquiring various data required in the step three in real time through an internal interface system, and providing data preparation for corresponding calculation;
step five, according to the model generated by training in the step three, corresponding parameters are deployed in corresponding computing environments, real-time monitoring is provided for the post-financial-loan operation risk of the enterprise by combining the real-time data prepared in the step four, and after a warning signal of no is sent after the financial loan is stored;
step six, providing real-time monitoring service for financial institutions according to the real-time monitoring in the step five
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (4)
1. An enterprise anti-fraud risk assessment system based on power information data is characterized in that: the system comprises a risk early warning index library, a power management module and a power management module, wherein the risk early warning index library comprises an ammeter electricity consumption data module, a user history payment data module, a user history checking data module, customer power reservation information and enterprise power data;
the risk early warning index library screens the information to construct a financial post-loan operation risk evaluation model;
the financial post-credit operational risk assessment model comprises a post-credit financial verification model and a post-credit operational risk scoring model.
2. An enterprise anti-fraud risk assessment system based on power information data as in claim 1, wherein: and the post-loan financial verification model and the post-loan operation risk scoring model construct an enterprise financial post-loan scoring early warning system.
3. An enterprise anti-fraud risk assessment system based on power information data as in claim 2, wherein: the customer power reservation information and the enterprise power data and other big data establish different risk dimension index libraries, the modules representing different risk dimensions are divided, in each module, the risk early warning index library performs univariate analysis and calculation on the distinguishing capability of each index to good and bad people through financial labels of enterprise customers, and therefore indexes with business applicability, economic meaning compliance and good statistical characteristics are screened out and enter the model;
based on the modeling index determined by univariate analysis, analyzing the relation between the variable combination and risk of the module, and determining a financial post-loan operation risk monitoring model based on enterprise power related data by using a machine learning algorithm and an integrated model;
by adopting a real-time data docking mode, various behavior data related to the electric power of the enterprise are communicated, and a modularized, expandable and standardized enterprise post-financial credit scoring early warning system based on post-financial credit operation analysis and monitoring of the electric power data of the enterprise is formed.
4. A method of an enterprise anti-fraud risk assessment system based on power information data according to any of claims 1-3, wherein: the method for the enterprise anti-fraud risk assessment system comprises the following specific steps:
step one, collecting ammeter electricity utilization data, user history payment data, user history checking data and the like of enterprise users;
step two, constructing a risk early warning index library based on financial business scenes according to the related data of enterprises, and realizing model index business and scene;
step three, combining the financial business labels of enterprises, carrying out feature screening on the risk early warning index library constructed in the step two, selecting indexes with stronger differentiation degree of the financial business labels, carrying out model training, respectively constructing post-loan operation risk evaluation models, wherein the post-loan operation risk evaluation models are respectively composed of post-loan financial verification models and post-loan operation risk scoring models;
firstly, setting a threshold interval for a verification model;
secondly, for the post-loan operation risk scoring model, adopting a machine learning mode to generate index weights, module weights and the like required by the model;
finally, a post-loan grading early warning system of the enterprise finance is constructed by the verification model and the post-loan operation risk grading model;
step four, acquiring various data required in the step three in real time through an internal interface system, and providing data preparation for corresponding calculation;
step five, according to the model generated by training in the step three, corresponding parameters are deployed in corresponding computing environments, and real-time monitoring is provided for the financial post-loan operation risk of the enterprise by combining the real-time data prepared in the step four;
and step six, providing real-time monitoring service for the financial institutions according to the real-time monitoring in the step five.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116862661A (en) * | 2023-07-20 | 2023-10-10 | 苏银凯基消费金融有限公司 | Digital credit approval and risk monitoring system based on consumption financial scene |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116862661A (en) * | 2023-07-20 | 2023-10-10 | 苏银凯基消费金融有限公司 | Digital credit approval and risk monitoring system based on consumption financial scene |
| CN116862661B (en) * | 2023-07-20 | 2024-04-26 | 苏银凯基消费金融有限公司 | Digital credit approval and risk monitoring system based on consumption financial scene |
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