CN112037030A - Bank large-amount deposit interest rate query method and device - Google Patents
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Abstract
The invention provides a method and a device for inquiring the interest rate of a large-amount deposit of a bank. On the basis, the large-amount deposit interest rate query condition interval value input by the client is matched with the target characteristic value in the large-amount deposit historical data stored in advance to obtain the target large-amount deposit historical data most matched with the large-amount deposit request of the client, and the target large-amount deposit historical data comprising the large-amount deposit interest rate is pushed to the client to provide accurate data support for the client for negotiating the large-amount deposit interest rate, so that the client and bank staff can quickly reach the same, and the processing efficiency of large-amount deposit business is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for inquiring the interest rate of a large-amount deposit of a bank.
Background
Because the bank is difficult to hold, a preferential policy is generally made for the large-amount deposit, and different from the common deposit, the interest rate of the large-amount deposit is not fixed, and factors such as different deposit amounts, deposit time limit, client credit and the like influence the interest rate of the large-amount deposit.
In the actual process of handling the large-amount deposit business, bank staff generally provide a large-amount deposit interest rate corresponding to the deposit information of the client to the client according to experience, and the client further agrees with the bank staff about the final large-amount deposit interest rate based on the large-amount deposit interest rate.
However, for a client who does not contact the large-amount deposit service, the client may know that the large-amount deposit interest rate is not fixed, but often does not know the range of the conventional large-amount deposit interest rate, that is, the standard for the large-amount deposit interest rate agreement is not determined, and the client cannot provide corresponding data support for the client as a bank party, so that the client may repeatedly discuss with bank staff and cannot quickly reach the agreement, thereby affecting the processing efficiency of the large-amount deposit service.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for querying a large-amount deposit interest rate of a bank, which provide a large-amount deposit history data corresponding to a large-amount deposit interest rate query condition interval value for a client, and facilitate the client to know a standard of large-amount deposit interest rate agreement.
In order to achieve the above purpose, the invention provides the following specific technical scheme:
a bank large-amount deposit interest rate query method comprises the following steps:
receiving a request for inquiring the interest rate of the large amount deposit input by a client;
responding to the request for inquiring the interest rate of the large-amount deposit, and displaying a query condition of the interest rate of the large-amount deposit on a front-end interface, wherein the query condition of the interest rate of the large-amount deposit is a target feature which is extracted from a plurality of features of historical data of the large-amount deposit in advance according to a principal component analysis method and influences the interest rate of the large-amount deposit;
receiving a large deposit interest rate query condition interval value input by a client;
matching the large deposit interest rate query condition interval value with a target characteristic value in pre-stored large deposit historical data;
and pushing target large-amount deposit history data matched with the large-amount deposit interest rate query condition interval value to a client, wherein the target large-amount deposit history data comprises the large-amount deposit interest rate.
Optionally, the method further includes:
preprocessing prestored large-amount deposit historical data;
carrying out standardization processing on the preprocessed large-amount deposit historical data to generate a characteristic matrix;
calculating the correlation coefficient of the feature in the feature matrix to generate a correlation coefficient matrix among the features;
calculating the eigenvalue and eigenvector of each principal component according to the correlation coefficient matrix;
calculating the contribution rate of each principal component, sorting the principal components according to the contribution rate from high to low, and determining the first N principal components as target characteristics influencing the large deposit interest rate, wherein N is a positive integer greater than 1.
Optionally, the target characteristics include deposit amount, deposit duration, asset information, credit status, and investment information.
Optionally, before pushing the target large deposit history data matching with the large deposit interest rate query condition interval value to the customer, the method further includes:
receiving the matching number of people input by a client;
the pushing of the target large deposit history data matched with the large deposit interest rate query condition interval value to the client comprises the following steps:
sorting the target large deposit historical data according to the matching degree of the target large deposit interest rate query condition interval value from high to low;
and pushing the previous K target large-amount deposit historical data to a client, wherein K is the number of matched people.
Optionally, the pushing of the target large deposit history data matched with the large deposit interest rate query condition interval value to the customer includes:
desensitizing the target large deposit historical data;
pushing the target large deposit history data after desensitization treatment to the client.
Optionally, the method further includes:
calculating the maximum value, the minimum value and the average value of the interest rate of the large-amount deposit in the target large-amount deposit historical data;
and pushing the maximum value, the minimum value and the average value of the interest rate of the large deposit in the target large deposit historical data to the client.
A bank large amount deposit interest rate inquiry device comprises:
the query request receiving unit is used for receiving a large deposit interest rate query request input by a client;
the query request response unit is used for responding to the large deposit interest rate query request and displaying a large deposit interest rate query condition on a front-end interface, wherein the large deposit interest rate query condition is a target feature which is extracted from a plurality of features of large deposit historical data in advance according to a principal component analysis method and influences the large deposit interest rate;
the query interval value receiving unit is used for receiving the large deposit interest rate query condition interval value input by the client;
the historical data matching unit is used for matching the large deposit interest rate query condition interval value with a target characteristic value in the large deposit historical data stored in advance;
and the historical data pushing unit is used for pushing target large-amount deposit historical data matched with the large-amount deposit interest rate query condition interval value to a client, and the target large-amount deposit historical data comprises the large-amount deposit interest rate.
Optionally, the apparatus further includes a target feature determining unit, specifically configured to:
preprocessing prestored large-amount deposit historical data;
carrying out standardization processing on the preprocessed large-amount deposit historical data to generate a characteristic matrix;
calculating the correlation coefficient of the feature in the feature matrix to generate a correlation coefficient matrix among the features;
calculating the eigenvalue and eigenvector of each principal component according to the correlation coefficient matrix;
calculating the contribution rate of each principal component, sorting the principal components according to the contribution rate from high to low, and determining the first N principal components as target characteristics influencing the large deposit interest rate, wherein N is a positive integer greater than 1.
Optionally, the target characteristics include deposit amount, deposit duration, asset information, credit status, and investment information.
Optionally, the device further includes a matching number receiving unit, specifically configured to:
receiving the matched number of people input by a client before pushing the target large deposit historical data matched with the large deposit interest rate query condition interval value to the client;
the history data pushing unit is specifically configured to:
sorting the target large deposit historical data according to the matching degree of the target large deposit interest rate query condition interval value from high to low;
and pushing the previous K target large-amount deposit historical data to a client, wherein K is the number of matched people.
Optionally, the history data pushing unit is specifically configured to:
desensitizing the target large deposit historical data;
pushing the target large deposit history data after desensitization treatment to the client.
Optionally, the history data pushing unit is further configured to:
calculating the maximum value, the minimum value and the average value of the interest rate of the large-amount deposit in the target large-amount deposit historical data;
and pushing the maximum value, the minimum value and the average value of the interest rate of the large deposit in the target large deposit historical data to the client.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a method for inquiring the interest rate of a large-amount deposit of a bank, which extracts target characteristics influencing the interest rate of the large-amount deposit from a plurality of characteristics of historical data of the large-amount deposit by using a principal component analysis method, and uses the target characteristics as query conditions of the interest rate of the large-amount deposit, thereby ensuring the accuracy of the query conditions of the interest rate of the large-amount deposit. On the basis, the large-amount deposit interest rate query condition interval value input by the client is matched with the target characteristic value in the large-amount deposit historical data stored in advance to obtain the target large-amount deposit historical data most matched with the large-amount deposit request of the client, and the target large-amount deposit historical data comprising the large-amount deposit interest rate is pushed to the client to provide accurate data support for the client for negotiating the large-amount deposit interest rate, so that the client and bank staff can quickly reach the same, and the processing efficiency of large-amount deposit business is improved.
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 description of the embodiments or the prior art 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 schematic flow chart of a method for inquiring the interest rate of a large amount of bank deposit according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for determining a large deposit interest rate query condition affecting a large deposit interest rate by using a principal component analysis method according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of another method for inquiring the interest rate of the large-amount deposit of the bank according to the embodiment of the invention;
fig. 4 is a schematic structural diagram of a device for inquiring the interest rate of the large-amount deposit of the bank according to the embodiment of the 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for inquiring the interest rate of a large-amount deposit in a bank, which is applied to a large-amount deposit business processing system, can be deployed in a mobile phone bank system, an online bank system or a bank system of a network point, and provides a large-amount deposit historical data corresponding to the interval value of the large-amount deposit interest rate inquiry condition for a client by determining an accurate large-amount deposit interest rate inquiry condition, so that the client can know the agreed standard of the large-amount deposit interest rate, the large-amount deposit business processing efficiency is improved, and the client experience is improved.
Specifically, referring to fig. 1, the method for inquiring the interest rate of the large-amount deposit of the bank disclosed in this embodiment includes the following steps:
s101: receiving a request for inquiring the interest rate of the large amount deposit input by a client;
specifically, a large-amount deposit interest rate query request input by a customer through a front-end interface is received, and for a mobile banking customer, the front-end interface is a mobile terminal interface, such as a system interface of a smart phone and a system interface of a tablet personal computer; for the online banking customer, the front-end interface is a system interface of a computer.
S102: responding to the request for inquiring the interest rate of the large-amount deposit, and displaying a query condition of the interest rate of the large-amount deposit on a front-end interface, wherein the query condition of the interest rate of the large-amount deposit is a target feature which is extracted from a plurality of features of historical data of the large-amount deposit in advance according to a principal component analysis method and influences the interest rate of the large-amount deposit;
referring to fig. 2, the method for determining the query condition of the large deposit interest rate affecting the large deposit interest rate by using the principal component analysis method is as follows:
s201: preprocessing prestored large-amount deposit historical data;
the large deposit history data is generally recorded in a database, and can be acquired by calling the database.
The characteristics in the history data of the large deposit are all fields in the data table of the large deposit, such as identification, depositor, account number, deposit amount, deposit period, asset information, credit condition, investment information, reference interest rate, deposit time and the like.
The preprocessing of the large deposit history data comprises the following steps:
a. and (4) carrying out data cleaning processing on the large-amount deposit historical data, and deleting useless abnormal data and meaningless fields such as identification and the like.
b. And processing the missing data after the data cleaning processing by adopting a multiple interpolation method, and filling the missing data.
c. The data of different fields are normalized, the data of different dimensions are converted into the data with the same dimensions, the data of different orders of magnitude are converted into the data with the same orders of magnitude, and the data can be normalized by methods such as a linear function, a pair function, an inverse cotangent function and the like.
S202: carrying out standardization processing on the preprocessed large-amount deposit historical data to generate a characteristic matrix; the normalization process is a quantization process, and the generated feature matrix is as follows:
wherein p represents the serial number of the history data, n represents the serial number of the feature, xpnAnd normalizing the processed characteristic value of the nth characteristic in the p-th historical data.
S203: calculating the correlation coefficient of the feature in the feature matrix to generate a correlation coefficient matrix among the features; the calculation formula for calculating the correlation coefficient between variables is as follows:
wherein r isijRepresents a feature xiAnd xjCorrelation coefficient between rij＝rji；
i,j＝1,2,3,...,p。
The calculated correlation coefficient matrix is as follows:
s204: calculating the eigenvalue and eigenvector of each principal component according to the correlation coefficient matrix;
specifically, the eigenvalue of each principal component is calculated by solving the equation of the eigenvalue | λ E-R | ═ 0.
Wherein E is an identity matrix, lambda is a characteristic value of the principal component, and the number of the principal component is not more than p.
The feature vector calculation method comprises the following steps:
and substituting the characteristic value into an equation lambda v ═ R, wherein lambda is the characteristic value, R is a correlation coefficient matrix, v is the characteristic vector, lambda is known, R is known, and v is solved.
S205: and calculating the contribution rate of each principal component, sequencing the principal components according to the contribution rate from high to low, and determining the first N principal components as target characteristics influencing the large deposit interest rate, wherein N is a positive integer greater than 1.
Specifically, the method for calculating the contribution ratio is as follows:
through experimental verification, the target characteristics influencing the large-amount deposit interest rate comprise deposit amount, deposit duration, asset information, credit condition and investment information, namely the query condition of the large-amount deposit interest rate comprises the deposit amount, the deposit duration, the asset information, the credit condition and the investment information.
The asset information is all assets of the client, and the investment information is investment assets of the client in the bank.
S103: receiving a large deposit interest rate query condition interval value input by a client;
the interval value can be set by the system, selected by the client through a drop-down list, or directly input by the client.
The following is a specific example of the large deposit interest rate query condition interval value input by the customer:
the deposit amount interval value is 10-20 ten thousand;
the deposit deadline interval value is 1-2 years;
the asset information interval value is 40-50 ten thousand;
the credit case interval value is good;
the investment information interval value is 5-10 ten thousand.
S104: matching the large deposit interest rate query condition interval value with a target characteristic value in pre-stored large deposit historical data;
the method specifically comprises the steps of matching the large-amount deposit interest rate query condition interval value with target characteristic values in pre-stored large-amount deposit historical data one by one, and if the target characteristic values in a certain large-amount deposit historical data are all matched with the large-amount deposit interest rate query condition interval value input by a client, the certain large-amount deposit historical data are target large-amount deposit historical data.
S105: and pushing target large-amount deposit history data matched with the large-amount deposit interest rate query condition interval value to a client, wherein the target large-amount deposit history data comprises the large-amount deposit interest rate.
Under the condition that the query condition interval value of the interest rate of the large deposit input by the client is large, more target large deposit historical data can be obtained, and the client can need to browse the target large deposit historical data for a long time.
In order to improve the user experience, the embodiment provides a matching number setting function, so that the number of the target large deposit history data finally pushed to the client is within the matching number range input by the client.
Referring to fig. 3, the method for inquiring the interest rate of the large amount deposit of the bank disclosed in the embodiment includes the following steps:
s301: receiving a request for inquiring the interest rate of the large amount deposit input by a client;
s302: responding to the request for inquiring the interest rate of the large-amount deposit, and displaying a query condition of the interest rate of the large-amount deposit on a front-end interface, wherein the query condition of the interest rate of the large-amount deposit is a target feature which is extracted from a plurality of features of historical data of the large-amount deposit in advance according to a principal component analysis method and influences the interest rate of the large-amount deposit;
s303: receiving a large deposit interest rate query condition interval value input by a client;
s304: matching the large deposit interest rate query condition interval value with a target characteristic value in pre-stored large deposit historical data;
s305: receiving the matching number of people input by a client;
s306: sorting the target large deposit historical data according to the matching degree of the target large deposit interest rate query condition interval value from high to low;
s307: and pushing the previous K target large-amount deposit historical data to a client, wherein K is the number of matched people.
It can be understood that the large-amount deposit interest rate query condition interval value is matched with a target characteristic value in pre-stored large-amount deposit historical data, when the number of the obtained target large-amount deposit historical data is not more than the number of matched persons, the target large-amount deposit historical data is directly pushed to a client, when the number of the obtained target large-amount deposit historical data is more than the number of matched persons, the target large-amount deposit historical data is sorted according to the matching degree of the target large-amount deposit interest rate query condition interval value from high to low, the previous K target large-amount deposit historical data are pushed to the client, and the number of the target large-amount deposit historical data which are finally pushed to the client is not more than the number of matched persons.
Furthermore, the large-amount deposit history data may contain the name of the client and some sensitive information which can infer the name of the client, and in order to protect the privacy of the client, before the target large-amount deposit history data is pushed to the client, desensitization processing is performed on the target large-amount deposit history data, for example, sensitive data is converted into x, and the like, so that the privacy of the client is protected.
Further, in order to enable the client to visually know the large deposit interest rate corresponding to the large deposit interest rate query condition interval value input by the client, the maximum value, the minimum value and the average value of the large deposit interest rate in the target large deposit history data can be calculated, and the maximum value, the minimum value and the average value of the large deposit interest rate in the target large deposit history data can be pushed to the client.
Therefore, according to the method for inquiring the interest rate of the large-amount deposit of the bank, the target characteristics influencing the interest rate of the large-amount deposit are extracted from the characteristics of the historical data of the large-amount deposit by using the principal component analysis method, and are used as the query conditions of the interest rate of the large-amount deposit, so that the accuracy of the query conditions of the interest rate of the large-amount deposit is ensured. On the basis, the large-amount deposit interest rate query condition interval value input by the client is matched with the target characteristic value in the large-amount deposit historical data stored in advance to obtain the target large-amount deposit historical data most matched with the large-amount deposit request of the client, and the target large-amount deposit historical data comprising the large-amount deposit interest rate is pushed to the client to provide accurate data support for the client for negotiating the large-amount deposit interest rate, so that the client and bank staff can quickly reach the same, and the processing efficiency of large-amount deposit business is improved.
Based on the method for inquiring the interest rate of the large-amount deposit of the bank disclosed by the embodiment, the embodiment correspondingly discloses a device for inquiring the interest rate of the large-amount deposit of the bank, please refer to the figure, and the device comprises:
an inquiry request receiving unit 100 for receiving a request for inquiring the interest rate of the large amount deposit inputted by the customer;
the query request response unit 200 is configured to respond to the large deposit interest rate query request, and display a large deposit interest rate query condition on a front-end interface, where the large deposit interest rate query condition is a target feature affecting the large deposit interest rate, which is extracted from multiple features of the large deposit history data in advance according to a principal component analysis method;
an inquiry interval value receiving unit 300 for receiving a large deposit interest rate inquiry condition interval value inputted by a client;
a historical data matching unit 400, configured to match the large deposit interest rate query condition interval value with a target characteristic value in the large deposit historical data stored in advance;
and a history data pushing unit 500, configured to push, to the client, target large deposit history data that matches the large deposit interest rate query condition interval value, where the target large deposit history data includes the large deposit interest rate.
Optionally, the apparatus further includes a target feature determining unit, specifically configured to:
preprocessing prestored large-amount deposit historical data;
carrying out standardization processing on the preprocessed large-amount deposit historical data to generate a characteristic matrix;
calculating the correlation coefficient of the feature in the feature matrix to generate a correlation coefficient matrix among the features;
calculating the eigenvalue and eigenvector of each principal component according to the correlation coefficient matrix;
calculating the contribution rate of each principal component, sorting the principal components according to the contribution rate from high to low, and determining the first N principal components as target characteristics influencing the large deposit interest rate, wherein N is a positive integer greater than 1.
Optionally, the target characteristics include deposit amount, deposit duration, asset information, credit status, and investment information.
Optionally, the device further includes a matching number receiving unit, specifically configured to:
receiving the matched number of people input by a client before pushing the target large deposit historical data matched with the large deposit interest rate query condition interval value to the client;
the history data pushing unit 500 is specifically configured to:
sorting the target large deposit historical data according to the matching degree of the target large deposit interest rate query condition interval value from high to low;
and pushing the previous K target large-amount deposit historical data to a client, wherein K is the number of matched people.
Optionally, the history data pushing unit 500 is specifically configured to:
desensitizing the target large deposit historical data;
pushing the target large deposit history data after desensitization treatment to the client.
Optionally, the history data pushing unit 500 is further configured to:
calculating the maximum value, the minimum value and the average value of the interest rate of the large-amount deposit in the target large-amount deposit historical data;
and pushing the maximum value, the minimum value and the average value of the interest rate of the large deposit in the target large deposit historical data to the client.
According to the device for inquiring the interest rate of the large-amount deposit of the bank, the target characteristics influencing the interest rate of the large-amount deposit are extracted from the characteristics of the historical data of the large-amount deposit by using the principal component analysis method, and the target characteristics are used as the query conditions of the interest rate of the large-amount deposit, so that the accuracy of the query conditions of the interest rate of the large-amount deposit is ensured. On the basis, the large-amount deposit interest rate query condition interval value input by the client is matched with the target characteristic value in the large-amount deposit historical data stored in advance to obtain the target large-amount deposit historical data most matched with the large-amount deposit request of the client, and the target large-amount deposit historical data comprising the large-amount deposit interest rate is pushed to the client to provide accurate data support for the client for negotiating the large-amount deposit interest rate, so that the client and bank staff can quickly reach the same, and the processing efficiency of large-amount deposit business is improved.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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 phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
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 method for inquiring interest rate of large-amount deposit of a bank is characterized by comprising the following steps:
receiving a request for inquiring the interest rate of the large amount deposit input by a client;
responding to the request for inquiring the interest rate of the large-amount deposit, and displaying a query condition of the interest rate of the large-amount deposit on a front-end interface, wherein the query condition of the interest rate of the large-amount deposit is a target feature which is extracted from a plurality of features of historical data of the large-amount deposit in advance according to a principal component analysis method and influences the interest rate of the large-amount deposit;
receiving a large deposit interest rate query condition interval value input by a client;
matching the large deposit interest rate query condition interval value with a target characteristic value in pre-stored large deposit historical data;
and pushing target large-amount deposit history data matched with the large-amount deposit interest rate query condition interval value to a client, wherein the target large-amount deposit history data comprises the large-amount deposit interest rate.
2. The method of claim 1, further comprising:
preprocessing prestored large-amount deposit historical data;
carrying out standardization processing on the preprocessed large-amount deposit historical data to generate a characteristic matrix;
calculating the correlation coefficient of the feature in the feature matrix to generate a correlation coefficient matrix among the features;
calculating the eigenvalue and eigenvector of each principal component according to the correlation coefficient matrix;
calculating the contribution rate of each principal component, sorting the principal components according to the contribution rate from high to low, and determining the first N principal components as target characteristics influencing the large deposit interest rate, wherein N is a positive integer greater than 1.
3. The method of claim 2, wherein the target characteristics include a deposit amount, a deposit term, asset information, credit status, and investment information.
4. The method of claim 1, wherein prior to said pushing to the customer the target large deposit history data matching the large deposit interest rate query interval value, the method further comprises:
receiving the matching number of people input by a client;
the pushing of the target large deposit history data matched with the large deposit interest rate query condition interval value to the client comprises the following steps:
sorting the target large deposit historical data according to the matching degree of the target large deposit interest rate query condition interval value from high to low;
and pushing the previous K target large-amount deposit historical data to a client, wherein K is the number of matched people.
5. The method of claim 1, wherein pushing the target large deposit history data matching the large deposit interest rate query interval value to the customer comprises:
desensitizing the target large deposit historical data;
pushing the target large deposit history data after desensitization treatment to the client.
6. The method of claim 1, further comprising:
calculating the maximum value, the minimum value and the average value of the interest rate of the large-amount deposit in the target large-amount deposit historical data;
and pushing the maximum value, the minimum value and the average value of the interest rate of the large deposit in the target large deposit historical data to the client.
7. A bank large amount deposit interest rate inquiry device is characterized by comprising:
the query request receiving unit is used for receiving a large deposit interest rate query request input by a client;
the query request response unit is used for responding to the large deposit interest rate query request and displaying a large deposit interest rate query condition on a front-end interface, wherein the large deposit interest rate query condition is a target feature which is extracted from a plurality of features of large deposit historical data in advance according to a principal component analysis method and influences the large deposit interest rate;
the query interval value receiving unit is used for receiving the large deposit interest rate query condition interval value input by the client;
the historical data matching unit is used for matching the large deposit interest rate query condition interval value with a target characteristic value in the large deposit historical data stored in advance;
and the historical data pushing unit is used for pushing target large-amount deposit historical data matched with the large-amount deposit interest rate query condition interval value to a client, and the target large-amount deposit historical data comprises the large-amount deposit interest rate.
8. The apparatus according to claim 7, wherein the apparatus further comprises a target feature determination unit, specifically configured to:
preprocessing prestored large-amount deposit historical data;
carrying out standardization processing on the preprocessed large-amount deposit historical data to generate a characteristic matrix;
calculating the correlation coefficient of the feature in the feature matrix to generate a correlation coefficient matrix among the features;
calculating the eigenvalue and eigenvector of each principal component according to the correlation coefficient matrix;
calculating the contribution rate of each principal component, sorting the principal components according to the contribution rate from high to low, and determining the first N principal components as target characteristics influencing the large deposit interest rate, wherein N is a positive integer greater than 1.
9. The apparatus of claim 8, wherein the target characteristics include a deposit amount, a deposit term, asset information, credit status, and investment information.
10. The device according to claim 7, further comprising a matching population receiving unit, specifically configured to:
receiving the matched number of people input by a client before pushing the target large deposit historical data matched with the large deposit interest rate query condition interval value to the client;
the history data pushing unit is specifically configured to:
sorting the target large deposit historical data according to the matching degree of the target large deposit interest rate query condition interval value from high to low;
and pushing the previous K target large-amount deposit historical data to a client, wherein K is the number of matched people.
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| CN202010905592.7A CN112037030A (en) | 2020-09-01 | 2020-09-01 | Bank large-amount deposit interest rate query method and device |
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| CN202010905592.7A CN112037030A (en) | 2020-09-01 | 2020-09-01 | Bank large-amount deposit interest rate query method and device |
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| CN111383095A (en) * | 2020-03-11 | 2020-07-07 | 中国工商银行股份有限公司 | Method and device for matching public deposit products |
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| US20020069147A1 (en) * | 2000-12-01 | 2002-06-06 | Sheehan Richard G. | System for determining a useful life of core deposits and interest rate sensitivity thereof |
| JP2007334579A (en) * | 2006-06-14 | 2007-12-27 | Biwako Bank Ltd | System and method for determining interest rate of deposit |
| CN104599181A (en) * | 2015-02-11 | 2015-05-06 | 中国农业银行股份有限公司 | Method and device for querying currency products |
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