CN115953235A - Risk index statistical method and device, storage medium and electronic equipment - Google Patents
Risk index statistical method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN115953235A CN115953235A CN202211665530.9A CN202211665530A CN115953235A CN 115953235 A CN115953235 A CN 115953235A CN 202211665530 A CN202211665530 A CN 202211665530A CN 115953235 A CN115953235 A CN 115953235A
- Authority
- CN
- China
- Prior art keywords
- overdue
- rolling
- client
- bad
- amount
- 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
Images
Abstract
The invention provides a risk index statistical method and device, a storage medium and electronic equipment, comprising: determining a client needing risk index statistics, calculating a statistical interval according to a preset first time period and a preset second time period, and determining each corresponding target debt item of the client in the statistical interval; accumulating newly-generated expenditure sums of each target debt item in a first time period to obtain an expenditure total, accumulating bad sums of each target debt item in a statistical interval to obtain a bad total, and accumulating overdue sums of each target debt item in the statistical interval to obtain an overdue total; and calculating to obtain the rolling reject ratio and the rolling overdue ratio corresponding to the client based on the total amount of the support, the bad total amount and the overdue total amount. By applying the method, the defects of diluting and hiding actual risks are reduced by counting risk indexes such as rolling reject ratio, rolling overdue rate and the like of the client, and the actual risk condition of the current business can be objectively and accurately reflected.
Description
Technical Field
The invention relates to the technical field of finance, in particular to a risk index statistical method and device, a storage medium and electronic equipment.
Background
With the change of economic situation, the function of small and micro enterprises in economic development is increasingly prominent, and in order to meet the financing requirements of the small and micro enterprises, a plurality of general financial loan products are successively released by various banks to promote the construction of a general financial system and promote the development of the small and micro enterprises. The small and micro enterprises loan has the characteristics of high frequency, small amount, batch and recycling, and banks need to monitor the loan condition of each small and micro enterprise and the running condition of loan products in real time in order to promote the stable quality of credit assets, strengthen the loan risk management system, optimize the bank loan products and process potential customer risks.
The existing risk index monitoring method is mainly based on statistics and monitoring of time points, but the method is easy to hide and dilute risks and cannot reflect the actual risk condition of the loan in time.
Disclosure of Invention
In view of the above, the present invention provides a risk index statistical method, by which risk indexes such as a rolling reject rate and a rolling overdue rate of a client can be counted, so that a risk statistical result can more accurately reflect an actual risk condition of a current business.
The invention also provides a risk index statistical device for ensuring the realization and the application of the method in practice.
A risk indicator statistical method comprises the following steps:
determining a client needing risk index statistics, and calculating to obtain a statistical interval according to a preset first time period and a preset second time period; determining each corresponding target debt item of the client in the statistical interval;
calculating the payment amount accumulated in the first time period of each target debt item, and adding the payment amounts corresponding to the target debt items to obtain the total payment amount;
adding bad money accumulated in the counting interval of each target debt item to obtain a bad sum, and adding overdue money accumulated in the counting interval of each target debt item to obtain an overdue sum; the overdue amount is the loan amount which is not paid after exceeding a preset time threshold; the bad amount is a loan amount which meets the preset bad loan amount condition;
and calculating the rolling defective rate and the rolling overdue rate corresponding to the client based on the total payment, the defective total and the overdue total, and determining the rolling defective rate and the rolling overdue rate as the risk index result of the client.
The method above, optionally, the determining each target debt item corresponding to the customer in the statistical interval includes:
acquiring all debt items corresponding to the client;
determining each debt with credit balance at the initial point of the counting period as a target debt in all the debts.
Optionally, in the method, the determining that the scroll reject rate and the scroll overdue rate correspond to the customer further includes:
and inputting the statistical interval serving as a parameter into a pre-constructed risk index statistical model to obtain the rolling reject ratio and the rolling overdue ratio corresponding to the client.
The above method, optionally, further includes:
obtaining the total loan amount corresponding to the end time point of the counting interval, obtaining the bad loan balance consisting of all bad money corresponding to the end time point, and obtaining the overdue loan balance consisting of all overdue money;
determining the reject rate and the overdue rate corresponding to the time point of the customer at the end of the term according to the total loan amount, the bad loan balance and the overdue loan balance;
and constructing a risk index statistical system of the client based on the reject ratio, the overdue rate, the rolling reject ratio and the rolling overdue rate.
The method described above, optionally, further includes:
acquiring the payment times of each target debt item in the statistical interval and the fund occupation time corresponding to each target debt item;
and determining risk characteristics of the client based on the rolling reject ratio, the rolling overdue ratio and the number of times of payment and the fund occupation time of each target debt item, wherein the risk characteristics comprise risk and risk absence.
A risk indicator statistics apparatus, comprising:
the first determining unit is used for determining a client needing risk index statistics and calculating a statistical interval according to a preset first time period and a preset second time period; determining each corresponding target debt item of the client in the statistical interval;
the first calculation unit is used for calculating the payment amount accumulated in the first time period of each target debt item, and the payment amounts corresponding to the target debt items are added to obtain the total payment amount;
the second calculating unit is used for adding bad money generated by each target debt item in the counting interval to obtain a bad total, and adding overdue money generated by each target debt item in the counting interval to obtain an overdue total; the overdue amount is the loan amount which is not paid after exceeding a preset time threshold; the bad amount is a loan amount which meets the preset bad loan amount condition;
and the second determining unit is used for calculating the rolling defective rate and the rolling overdue rate corresponding to the client based on the total payment amount, the defective total amount and the overdue total amount, and determining the rolling defective rate and the rolling overdue rate as risk index results of the client.
The above apparatus, optionally, the first determining unit includes:
the acquiring subunit is used for acquiring all debt items corresponding to the client;
and the determining subunit is used for determining each debt item with credit balance at the initial time point of the counting period as a target debt item in all the debt items.
The above apparatus, optionally, further comprises:
the acquisition unit is used for acquiring the total loan amount corresponding to the end time point of the counting interval, and acquiring the bad loan balance consisting of all bad money corresponding to the end time point and the overdue loan balance consisting of all overdue money;
a third determining unit, configured to determine a reject rate and an overdue rate corresponding to the end point of the client according to the total amount of the end loan, the balance of the bad loan, and the balance of the overdue loan;
and the building unit is used for building a risk index statistical system of the client based on the reject ratio, the overdue rate, the rolling reject ratio and the rolling overdue rate.
A storage medium, the storage medium comprising stored instructions, wherein when the instructions are executed, a device where the storage medium is located is controlled to execute the risk indicator statistical method.
An electronic device comprising a memory and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by one or more processors to perform the risk indicator statistics method.
Compared with the prior art, the invention has the following advantages:
based on the embodiment provided by the invention, in the process of carrying out risk index statistics, a client needing risk index statistics is determined, a statistical interval is obtained by calculation according to a preset first time period and a preset second time period, and each corresponding target debt item of the client in the statistical interval is determined; accumulating newly-generated expenditure sums of each target debt item in a first time period to obtain an expenditure total, accumulating bad sums of each target debt item in a statistical interval to obtain a bad total, and accumulating overdue sums of each target debt item in the statistical interval to obtain an overdue total; the overdue amount is a loan amount which is not paid for exceeding a preset time threshold, and the bad amount is a loan amount which meets a preset bad loan amount condition. And calculating to obtain the rolling defective rate and the rolling overdue rate corresponding to the client based on the total amount of the support, the defective amount and the overdue amount, so as to obtain the risk index result of the client.
By applying the method provided by the embodiment of the invention, risk indexes such as rolling reject ratio, rolling overdue ratio and the like of the client are counted, the influences of bad or overdue loans generated by stocks before the starting time in the counting period and new services generated in the counting period are avoided, the conditions of overdue or bad performance not generated at the end of the observation period are fully considered, the defects of dilution and actual risk hiding are reduced, and the actual risk condition of the current service is more accurately reflected by the counting result of the risk indexes.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flowchart of a method for risk indicator statistics according to an embodiment of the present invention;
FIG. 2 is a flowchart of another method of a risk indicator statistics method according to an embodiment of the present invention;
FIG. 3 is a flowchart of another method of a risk indicator statistics method according to an embodiment of the present invention;
FIG. 4 is a block diagram of a risk indicator statistic device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In this application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and the terms "comprises", "comprising", or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The invention is operational with numerous general purpose or special purpose computing device environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multi-processor apparatus, distributed computing environments that include any of the above devices or equipment, and the like.
The embodiment of the invention provides a risk indicator statistical method, which can be applied to various system platforms, wherein an execution main body of the method can be a computer terminal or a processor of various mobile devices, and a flow chart of the method is shown in fig. 1 and specifically comprises the following steps:
s101: determining a client needing risk index statistics, and calculating to obtain a statistical interval according to a preset first time period and a preset second time period; and determining corresponding target debt items of the client in the statistical interval.
In the embodiment provided by the invention, the risk monitoring is carried out on the loan clients, the potential risks of the clients are processed in time, and the stability of the bank loan service is maintained. Determining a client needing risk detection, and selecting the loan condition of the client within a period of time to carry out risk monitoring, wherein the period of time needing risk monitoring is a statistical interval and consists of a first time period and a second time period which are preset. The first time period is an observation period, usually 1 year, and is mainly used for extracting the performance characteristics of the client in the first time period; the second time period is the presentation period, typically 30 days, and is a time window defining a good or bad label, and if the loan risk indicator for the client during that time period meets the criteria for being at risk, the client is defined as a risky client, and if the loan risk indicator for the client during that time period is within the criteria, the client is defined as a non-risky client. Specifically, whether the client is a risk client or not is judged, all target debt items of the client in the statistical interval need to be acquired firstly, and a result is obtained according to the operation condition of each target debt item of the client.
Optionally, the determining of the target debt items corresponding to the customer in the statistical interval includes:
acquiring all debt items corresponding to the client;
determining each debt with credit balance at the initial point of the counting period as a target debt in all the debts.
Specifically, all debt items corresponding to the client needing risk monitoring are obtained in a bank loan system, the debt items are financial instruments with credit risks born by debtors, the debt items are loan products, all the obtained debt items are obtained, each target debt item is a debt item with credit balance at the starting time point of a statistical interval, and the credit balance refers to the remaining amount of the credit-type loan after the client pays a certain loan, and the amount of the credit-type loan is added with the amount signed but not issued under a contract. The balance refers to the amount that is occurring.
S102: and calculating the payment amount accumulated in the first time period of each target debt item, and adding the payment amounts corresponding to the target debts items to obtain the total payment amount.
S103: and adding bad money accumulated in the counting interval of each target debt item to obtain a bad total, and adding overdue money accumulated in the counting interval of each target debt item to obtain an overdue total.
Wherein the overdue amount is the loan amount which is not paid for and exceeds a preset time threshold; the bad amount is a loan amount which meets the preset bad loan amount condition.
In the embodiment provided by the invention, the total payment amount is equal to the sum of the newly generated payment amounts accumulated in all the target debt items in the observation period; the bad sum is equal to the sum of the bad sums accumulated in all the target debt items within the counting period; the overdue total amount is equal to the sum of the overdue amounts accumulated in all the target debt items within the counting period.
Specifically, the total amount of overdue is usually counted as the sum of the amounts of the respective target debt which is overdue for more than 5 days (inclusive). And each target debt item with bad sum meets the loan sum of the bad loan amount condition, wherein the loan meeting the bad loan amount condition can be a loan which is overdue and exceeds the contractual limit, or a loan which is not overdue or is overdue and does not meet the specified age but the production operation is terminated, the project is built, or a loan which is set as a standing loan according to the relevant specification.
S104: and calculating the rolling defective rate and the rolling overdue rate corresponding to the client based on the total payment, the defective total and the overdue total, and determining the rolling defective rate and the rolling overdue rate as the risk index result of the client.
In the embodiment provided by the invention, according to a formula: rolling fraction of badness = bad total/total used; and (3) calculating the rolling overdue rate = the overdue total amount/the total amount for use to obtain the rolling reject ratio and the rolling overdue rate corresponding to the client, so as to obtain a risk index result of the client.
For example, the risk indicator statistics on the customer nail is required, and the specific process is that if the report period is 2021 year 6 month 30 day, the observation period is 2020 year 5 month 1 day to 2021 year 5 month 31 day, the presentation period is 2021 year 6 month 1 day to 2021 year 6 month 30 day, and the statistical period is 2020 year 5 month 1 day to 2021 year 6 month 30 day;
target debt = { debt with credit balance granted 5/1/2020 };
total payment = new payment amount accumulated for the target debt during the observation period (i.e. 5/1/2020 to 5/31/2021);
bad sum = the sum of bad amounts accumulated in the target debt during the statistical period (i.e. 5/1/2020 to 6/30/2021);
overdue total = overdue amount total of the target debt is accumulated in the statistical period (i.e. 5/1/2020 to 6/2021/30/2020).
And finally according to the formula: scroll reject rate = reject total/total used; and (4) rolling overdue rate = overdue total/total for use, and calculating to obtain the rolling reject rate and the rolling overdue rate of the statistical result of the risk indexes of the client A.
Based on the embodiment provided by the invention, in the process of carrying out risk index statistics, a client needing risk index statistics is determined, a statistical interval is calculated according to a preset observation period and a preset presentation period, and each corresponding target debt item of the client in the statistical interval is determined; adding new payment accumulated in the observation period of each target debt to obtain total payment, adding bad money accumulated in the statistical interval of each target debt to obtain bad total, and adding overdue money accumulated in the statistical interval of each target debt to obtain overdue total; the overdue amount is a loan amount which is not paid for exceeding a preset time threshold, and the bad amount is a loan amount which meets a preset bad loan amount condition. And calculating to obtain the rolling defective rate and the rolling overdue rate corresponding to the client based on the total amount of the support, the defective amount and the overdue amount, so as to obtain the risk index result of the client.
By applying the method provided by the embodiment of the invention, risk indexes such as rolling reject ratio, rolling overdue ratio and the like of the client are counted, the influences of bad or overdue loans generated by stocks before the starting time in the counting period and new services generated in the counting period are avoided, the conditions of overdue or bad performance not generated at the end of the observation period are fully considered, the defects of dilution and actual risk hiding are reduced, and the actual risk condition of the current service is more accurately reflected by the counting result of the risk indexes.
In the embodiment of the present invention, optionally, the determining the scroll reject rate and the scroll overdue rate corresponding to the client further includes:
and inputting the statistical interval serving as a parameter into a pre-constructed risk index statistical model to obtain the rolling reject rate and the rolling overdue rate corresponding to the client.
Specifically, risk indexes such as rolling reject rate, rolling overdue rate and the like are solidified in a model form by relying on a big data technology, the process of calculating the rolling reject rate and the rolling overdue rate is packaged into a risk index statistical model, an operation user autonomously selects parameters such as a statistical period and the like, the risk index statistical model directly counts and calculates risk index results in the corresponding statistical period, and the rolling reject rate and the rolling overdue rate corresponding to the client are obtained.
By applying the method, the risk index result can be automatically obtained by inputting the parameters into the risk index statistical model, the manual calculation process is reduced, and the accuracy and efficiency of risk index statistics are improved.
As shown in fig. 2, the embodiment provided by the present invention, optionally, further includes:
s201: and acquiring the total loan amount corresponding to the end time point of the counting interval, acquiring the bad loan balance consisting of all bad money corresponding to the end time point, and acquiring the overdue loan balance consisting of all overdue money.
S202: and determining the reject rate and the overdue rate corresponding to the customer at the end point according to the total loan amount, the bad loan balance and the overdue loan balance.
S203: and constructing a risk index statistical system of the client based on the reject ratio, the overdue rate, the rolling reject ratio and the rolling overdue rate.
Specifically, the total loan amount, the bad loan balance and the overdue loan balance at the end time point of the statistical interval are calculated. As described above, if the statistical period is from 2020 to 5/1/2021 to 6/30/2021, the total amount of the loan of the client at the time point is counted at 6/30/2021, and the balance of the bad loan composed of all the bad money amounts of the client at the time point and the balance of the overdue loan composed of all the overdue money amounts are counted at the time point.
According to the formula: reject ratio = reporting end bad loan balance/reporting end loan total; overdue rate = reporting end overdue loan balance/reporting end loan total, and the reject rate and the overdue rate corresponding to the customer at the end point are calculated.
The rolling reject ratio and the rolling overdue rate are combined with other risk indexes such as the traditional reject ratio and the overdue rate, a risk index statistical system of the client is constructed, the actual risk level of the current client can be described more accurately, objectively and comprehensively, the research and judgment of actual risk expectation is strengthened, and a risk management monitoring system is perfected.
It should be further noted that by comparing the rolling default rate and the rolling overdue rate in different statistical periods, the risk change trend and the evolution law of specific passenger groups and target debt items in different periods are analyzed and mastered, and meanwhile, the weak items of the risk management work are reflected in time, so that the method is targeted and accurately planned, and the risk management work effect is improved.
Secondly, through analyzing the risk characteristics of the bad and overdue customers, different dimensions such as industries and credit granting products are distinguished, the existing big data customer obtaining channel source and model parameters can be further optimized, high-quality credit customers are further selected, identification of the risk customers in the customer access link is promoted, the customers are prevented from being accessed with diseases, product resource allocation is optimized, risk control is strengthened from the source, and credit asset quality is promoted to be stable.
As shown in fig. 3, the embodiment provided by the present invention, optionally, further includes:
s301: and acquiring the payment times of each target debt item in the statistical interval and the fund occupation time corresponding to each target debt item.
S302: and determining risk characteristics of the client based on the rolling reject ratio, the rolling overdue ratio and the number of times of payment and the fund occupation time of each target debt item, wherein the risk characteristics comprise risk and risk absence.
Specifically, the number of times of payment of each target debt item in the statistical interval and the fund occupation time of each target debt item in the statistical interval are counted, and whether the client has the risk or not can be judged according to a preset judgment reference standard by combining the rolling reject ratio and the rolling overdue ratio which are obtained through calculation.
Compared with the traditional credit business, the method has the characteristics of ' returning with borrowing and ' circularly using ' of target debt related business products, brings the factors for circularly using into data analysis and statistics, and observes the correlation between the number of times of target debt using and the fund occupation period and the badness and overdue of the client in the statistical period so as to enhance the post-credit tracking and management of the related client and discover the potential client risk as early as possible.
The specific implementation procedures and derivatives thereof of the above embodiments are within the scope of the present invention.
Corresponding to the method described in fig. 1, an embodiment of the present invention further provides a risk indicator statistics apparatus, which is used for implementing the method in fig. 1 specifically, the risk indicator statistics apparatus provided in the embodiment of the present invention may be applied to a computer terminal or various mobile devices, and a schematic structural diagram of the risk indicator statistics apparatus is shown in fig. 4, and specifically includes:
the first determining unit is used for determining a client needing risk index statistics and calculating a statistical interval according to a preset first time period and a preset second time period; determining each corresponding target debt item of the client in the statistical interval;
the first calculation unit is used for calculating the payment amount accumulated by each target debt item in the first time period, and the payment amounts corresponding to the target debt items are added to obtain the total payment amount;
the second calculating unit is used for adding bad money accumulated in the counting interval of each target debt item to obtain a bad sum, and adding overdue money accumulated in the counting interval of each target debt item to obtain an overdue sum; the overdue amount is the loan amount which is not paid after exceeding a preset time threshold; the bad amount is a loan amount which meets the preset bad loan amount condition;
and the second determining unit is used for calculating the rolling defective rate and the rolling overdue rate corresponding to the client based on the total payment amount, the defective total amount and the overdue total amount, and determining the rolling defective rate and the rolling overdue rate as risk index results of the client.
According to the device provided by the embodiment of the invention, in the process of carrying out risk index statistics, a first determining unit determines a client needing risk index statistics, a statistical interval is obtained by calculation according to a preset first time period and a preset second time period, and each corresponding target debt item of the client in the statistical interval is determined; then, a first calculating unit and a second calculating unit add up newly-generated payment amount of each target debt item in a first time period to obtain payment total, add up bad money amount of each target debt item in a statistical interval to obtain bad total, and add up overdue money amount of each target debt item in the statistical interval to obtain overdue total; the overdue amount is a loan amount which is not paid for exceeding a preset time threshold, and the bad amount is a loan amount which meets a preset bad loan amount condition. And finally, calculating to obtain the rolling defective rate and the rolling overdue rate corresponding to the client by the second determining unit based on the total amount of the support, the defective amount and the overdue amount, so as to obtain a risk index result of the client.
By applying the device provided by the embodiment of the invention, risk indexes such as rolling reject ratio and rolling overdue rate of the client are counted, the defects that the report time point index result and the dilution and the actual risk are hidden by expanding the loan scale base number in the method are overcome, the newly increased conditions of bad loans and overdue loans of the client in a counting interval are monitored, the risk can be found and exposed in time and in an early manner, and the risk indexes are more objective.
In an embodiment of the invention, in the risk indicator statistic apparatus, optionally, the first determining unit includes:
the acquiring subunit is used for acquiring all debt items corresponding to the client;
and the determining subunit is used for determining each debt item with credit balance at the initial time point in the counting period as a target debt item in all the debt items.
In an embodiment of the present invention, the risk indicator statistic apparatus further includes:
the acquisition unit is used for acquiring the total loan amount corresponding to the end time point of the counting interval, and acquiring the bad loan balance consisting of all bad money corresponding to the end time point and the overdue loan balance consisting of all overdue money;
a third determining unit, configured to determine a reject rate and an overdue rate corresponding to the end point of the client according to the total amount of the end loan, the balance of the bad loan, and the balance of the overdue loan;
and the building unit is used for building a risk index statistical system of the client based on the reject ratio, the overdue rate, the rolling reject ratio and the rolling overdue rate.
The specific working processes of each unit and sub-unit in the risk indicator statistical apparatus disclosed in the above embodiment of the present invention can refer to the corresponding contents in the risk indicator statistical method disclosed in the above embodiment of the present invention, and are not described herein again.
The embodiment of the invention also provides a storage medium, which comprises a stored instruction, wherein when the instruction runs, the equipment where the storage medium is located is controlled to execute the risk index statistical method.
An embodiment of the present invention further provides an electronic device, a schematic structural diagram of which is shown in fig. 5, and the electronic device specifically includes a memory 501 and one or more instructions 502, where the one or more instructions 502 are stored in the memory 501, and are configured to be executed by one or more processors 503 to perform the following operations for the one or more instructions 502:
determining a client needing risk index statistics, and calculating to obtain a statistical interval according to a preset first time period and a preset second time period; determining corresponding target debt items of the client in the statistical interval;
calculating the payment amount of each target debt item accumulated in the first time period, and adding the payment amounts corresponding to the target debt items to obtain the total payment amount;
adding bad money accumulated in the counting interval of each target debt item to obtain a bad sum, and adding overdue money accumulated in the counting interval of each target debt item to obtain an overdue sum; the overdue amount is the loan amount which is not paid after exceeding a preset time threshold; the bad amount is a loan amount which meets the preset bad loan amount condition.
And calculating the rolling defective rate and the rolling overdue rate corresponding to the client based on the total payment, the defective total and the overdue total, and determining the rolling defective rate and the rolling overdue rate as risk index results of the client.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both.
To clearly illustrate this interchangeability of hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A risk indicator statistical method is characterized by comprising the following steps:
determining a client needing risk index statistics, and calculating to obtain a statistical interval according to a preset first time period and a preset second time period; determining corresponding target debt items of the client in the statistical interval;
calculating the payment amount accumulated in the first time period of each target debt item, and adding the payment amounts corresponding to the target debt items to obtain the total payment amount;
adding bad money accumulated in the counting interval of each target debt item to obtain a bad sum, and adding overdue money accumulated in the counting interval of each target debt item to obtain an overdue sum; the overdue amount is the loan amount which is not paid for and exceeds a preset time threshold; the bad amount is a loan amount which meets the preset bad loan amount condition;
and calculating the rolling defective rate and the rolling overdue rate corresponding to the client based on the total payment, the defective total and the overdue total, and determining the rolling defective rate and the rolling overdue rate as the risk index result of the client.
2. The method of claim 1, wherein said determining respective target debt items corresponding to said customer during said statistical interval comprises:
acquiring all debt items corresponding to the client;
and determining each debt with credit balance at the initial time point of the counting period as a target debt in all the debts.
3. The method of claim 1, wherein the determining the scroll fraction of defectiveness and the scroll overdue rate corresponding to the customer further comprises:
and inputting the statistical interval serving as a parameter into a pre-constructed risk index statistical model to obtain the rolling reject rate and the rolling overdue rate corresponding to the client.
4. The method of claim 1, further comprising:
obtaining the total loan amount corresponding to the end time point of the counting interval, obtaining the bad loan balance consisting of all bad money corresponding to the end time point, and obtaining the overdue loan balance consisting of all overdue money;
determining the reject rate and the overdue rate corresponding to the time point of the customer at the end of the term according to the total loan amount, the bad loan balance and the overdue loan balance;
and constructing a risk index statistical system of the client based on the reject ratio, the overdue rate, the rolling reject ratio and the rolling overdue rate.
5. The method of claim 1, further comprising:
acquiring the payment times of each target debt item in the statistical interval and the fund occupation time corresponding to each target debt item;
and determining risk characteristics of the client based on the rolling reject ratio, the rolling overdue ratio and the number of times of payment and the fund occupation time of each target debt item, wherein the risk characteristics comprise risk and risk absence.
6. A risk indicator statistic device, comprising:
the first determining unit is used for determining a client needing risk index statistics and calculating a statistical interval according to a preset first time period and a preset second time period; determining each corresponding target debt item of the client in the statistical interval;
the first calculation unit is used for calculating the payment amount accumulated in the first time period of each target debt item, and the payment amounts corresponding to the target debt items are added to obtain the total payment amount;
the second calculating unit is used for adding bad money accumulated in the counting interval of each target debt item to obtain a bad sum, and adding overdue money accumulated in the counting interval of each target debt item to obtain an overdue sum; the overdue amount is the loan amount which is not paid after exceeding a preset time threshold; the bad amount is a loan amount which meets the preset bad loan amount condition;
and the second determining unit is used for calculating the rolling defective rate and the rolling overdue rate corresponding to the client based on the total payment amount, the defective total amount and the overdue total amount, and determining the rolling defective rate and the rolling overdue rate as risk index results of the client.
7. The apparatus of claim 6, wherein the first determining unit comprises:
the acquiring subunit is used for acquiring all debt items corresponding to the client;
and the determining subunit is used for determining each debt item with credit balance at the initial time point in the counting period as a target debt item in all the debt items.
8. The apparatus of claim 6, further comprising:
the acquisition unit is used for acquiring the total loan amount corresponding to the end time point of the counting interval, and acquiring the bad loan balance consisting of all bad money corresponding to the end time point and the overdue loan balance consisting of all overdue money;
a third determining unit, configured to determine a reject rate and an overdue rate corresponding to the end point of the customer according to the total amount of the end loan, the bad loan balance, and the overdue loan balance;
and the building unit is used for building a risk index statistical system of the client based on the reject ratio, the overdue rate, the rolling reject ratio and the rolling overdue rate.
9. A storage medium, comprising stored instructions, wherein when executed, the storage medium controls a device on which the storage medium is located to perform the risk indicator statistical method according to any one of claims 1-5.
10. An electronic device comprising a memory and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by the one or more processors to perform the risk indicator statistical method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211665530.9A CN115953235A (en) | 2022-12-23 | 2022-12-23 | Risk index statistical method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211665530.9A CN115953235A (en) | 2022-12-23 | 2022-12-23 | Risk index statistical method and device, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115953235A true CN115953235A (en) | 2023-04-11 |
Family
ID=87281865
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211665530.9A Pending CN115953235A (en) | 2022-12-23 | 2022-12-23 | Risk index statistical method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115953235A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117853232A (en) * | 2024-03-07 | 2024-04-09 | 杭银消费金融股份有限公司 | Credit risk abnormity inspection attribution early warning method and system |
-
2022
- 2022-12-23 CN CN202211665530.9A patent/CN115953235A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117853232A (en) * | 2024-03-07 | 2024-04-09 | 杭银消费金融股份有限公司 | Credit risk abnormity inspection attribution early warning method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110009417B (en) | Target customer screening method, device, equipment and computer readable storage medium | |
| WO2007133685A2 (en) | Collaterized debt obligation evaluation system and method | |
| US8984022B1 (en) | Automating growth and evaluation of segmentation trees | |
| CN110415106A (en) | A kind of performance analysis forecast assessment system of management body | |
| CN111709828A (en) | Resource processing method, device, equipment and system | |
| CN113034046A (en) | Data risk metering method and device, electronic equipment and storage medium | |
| CN115953235A (en) | Risk index statistical method and device, storage medium and electronic equipment | |
| US8078529B1 (en) | Evaluating customers' ability to manage revolving credit | |
| CN116823471A (en) | Transaction policy return method and device, electronic equipment and storage medium | |
| JP6771513B2 (en) | Devices and methods for calculating default probability and programs for it | |
| CN111950915A (en) | Method and device for evaluating workload of bank outlet teller | |
| CN115994819A (en) | Risk customer identification method, apparatus, device and medium | |
| CN114119107A (en) | Steel trade enterprise transaction evaluation method, device, equipment and storage medium | |
| US8595114B2 (en) | Account level interchange effectiveness determination | |
| CN113807943A (en) | Multi-factor valuation method, system, medium and equipment for bad assets | |
| CN114626940A (en) | Data analysis method and device and electronic equipment | |
| CN113409129A (en) | Financing admission risk assessment method, device and computer storage medium | |
| JP7298286B2 (en) | Model providing program, model providing method and model providing apparatus | |
| CN114240599A (en) | Loan calculation method and device, computer equipment and storage medium | |
| CN114240605A (en) | Loan calculation method and device, computer equipment and storage medium | |
| CN113393328A (en) | Method and device for assessing pre-financing and pre-loan approval and computer storage medium | |
| CN113421014A (en) | Target enterprise determination method, device, equipment and storage medium | |
| CN111738818A (en) | Method, equipment and storage medium for re-detection of credit after credit | |
| US20230394069A1 (en) | Method and apparatus for measuring material risk in a data set | |
| JP2019067297A (en) | Information processing apparatus and program |
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 |