CN117350865A - Financial product recommendation method, system, equipment and medium for enterprise users - Google Patents
Financial product recommendation method, system, equipment and medium for enterprise users Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention discloses a financial product recommendation method, a system, equipment and a medium for enterprise users, which are used for predicting future cash flow of the enterprise users by adopting a pre-established time sequence prediction model to obtain a prediction result; based on the prediction result, screening enterprise users capable of recommending financial products; recommending corresponding financial product combinations and configuring suggestions to enterprise users capable of recommending financial products based on a pre-established financial product recommendation model and a prediction result; the time series prediction model is established based on cash flow data of enterprise users. The invention can accurately consider the demands of enterprise users to establish a corresponding prediction model, and provides most reasonable and most appropriate financial products and schemes for the enterprise users in a point-to-point and face-to-face manner by combining the prediction results to assist the enterprise users in making decisions.
Description
Technical Field
The invention relates to the technical field, in particular to a financial product recommendation method, system, equipment and medium for enterprise users.
Background
After the financial institution opens the account, the enterprise can keep a certain demand deposit in the account for daily operation turnover of the enterprise. The current popular trends are: if the enterprise has a large amount of unused funds in the account, the bank will typically recommend corresponding financial products to the enterprise. On one hand, enterprises can utilize idle funds to obtain certain benefits; alternatively, the bank may use the portion of the funds for a loan or other investment transaction to obtain revenue; a commercial cycle model of sustainable development reciprocal reciprocity is formed.
Recommending the appropriate financial product to the appropriate enterprise user is a precise recommendation purpose. From the perspective of the enterprise user, it is much more meaningful to receive financial product information related thereto than to receive completely unrelated product information. In addition, from the bank perspective, financial product information and suggestions which are most likely to be purchased are recommended to the user, so that the viscosity of the user can be improved, and the income can be increased. For an individual user, banks typically recommend to them various types of financial products that may be of interest to the party based on user information, account status, historical purchase records, and the like. However, enterprise users and general users may vary greatly in terms of financial product purchases. General users purchase financial products, primarily considering product benefits, then risk and liquidity. Most users may accept a certain risk for higher profits, and sacrifice a certain mobility. Enterprise users will consider mobility and risk more, they need to ensure sufficient and safe funds to operate, and product returns are the inverse of what was considered last. Therefore, financial products recommended to enterprises by banks are generally mainly deposit products with low risk.
The general thought of the existing recommendation method is to start from the purchasing behavior of the user, analyze historical purchasing data, combine financial product information, build a model and recommend the most likely product purchased by the user. These methods require a certain amount of data to build the model. However, unlike individual users, enterprise users generally do not purchase financial products multiple times, and have less historical purchase data, which can be difficult to build, which is a difficult problem in recommending financial for enterprise users at present.
In addition, existing recommendation methods typically recommend products that are most interesting (or most likely to be purchased) to the user, recommending only products, and not other suggestions. Which product is purchased, how many products are purchased, are determined by the user. However, if the user purchases a separate financial product, it is difficult to meet the user's needs for funds mobility and low risk.
Disclosure of Invention
The invention aims to provide a financial product recommendation method, a system, equipment and a medium for enterprise users, so as to overcome the defects of the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a financial product recommendation method for enterprise users comprises the following steps:
predicting future cash flow of enterprise users by adopting a pre-established time sequence prediction model to obtain a prediction result;
based on the prediction result, screening enterprise users capable of recommending financial products;
recommending corresponding financial product combinations and configuring suggestions to enterprise users capable of recommending financial products based on a pre-established financial product recommendation model and a prediction result;
the time series prediction model is established based on cash flow data of enterprise users.
Further, before the time sequence prediction model is built, the method further comprises preprocessing the cash flow data of the enterprise user, wherein the preprocessing comprises the following steps:
calculating cash flow rate of the enterprise user;
processing the missing value and the abnormal value of the cash flow rate;
an average cash flow rate for the business user is calculated based on the processed cash flow rates.
Further, the calculation expression of the cash flow rate of the enterprise user is:
wherein, delta CF is taken as a time unit of month t For cash flow rate at month t, balance t Balance of account of enterprise user at the end of month t, balance t-1 Account balances for business users at the end of month (t-1).
Further, the model expression of the time series prediction model is:
a) If there is no seasonal, then:
b) If seasonal, then:
wherein, delta CF is taken as a time unit of month t For the cash flow rate of month t, ΔCF t-i Cash rheology, ΔCF, expressed as month (t-i) t-12i Cash rheology expressed as month (t-12 i), c as a constant term, ε t Expressed as random error terms, A i Expressed as autocorrelation coefficients, and p expressed as an order.
Further, the step of screening enterprise users capable of recommending financial products based on the prediction result specifically comprises the following steps:
when the average value of the predicted cash flow change rate for N months is larger than a set threshold value, the enterprise user is judged to be the enterprise user recommending financial products, and the average value is shown as the following formula:
wherein,for the predicted cash flow change rate for the next i months, i is 1 to N, +.>Is the average value of the predicted cash flow change rate for N months.
Further, the expression of the financial product recommendation model is:
s.t.
wherein w is i : representing weights, the values being between 0 and 1; m represents a short-term deposit product, and N represents a long-term deposit product; x is x i : a control variable, the value of which is not 0, namely 1; r is (r) i : product yield; r: the bank is willing to provide a rate of return.
Further, the conditions for recommending the corresponding financial product combinations to the enterprise users are as follows:
two products are recommended simultaneously, and at least one short-term deposit product is included;
the sum of the weights of the two products is 1.0;
each product has a weight greater than 0.
A financial product recommendation system for enterprise users, comprising:
the prediction module is used for predicting future cash flow of the enterprise user by adopting a pre-established time sequence prediction model to obtain a prediction result;
the screening module is used for screening enterprise users capable of recommending financial products based on the prediction result;
the product recommendation module is used for recommending corresponding financial product combinations and configuring suggestions to enterprise users capable of recommending the financial products based on a pre-established financial product recommendation model and a prediction result;
wherein the time series prediction model is established based on cash flow data of the enterprise user.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the financial product recommendation method for enterprise users when the computer program is executed.
A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the financial product recommendation method for an enterprise user.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention designs a financial product recommendation method aiming at enterprise users, which starts from the fund situation of each enterprise user, firstly establishes a cash flow prediction model to predict the cash flow situation of the enterprise in a future period, then combines the prediction results to provide the most reasonable and most suitable financial products and configuration schemes for the enterprise in a point-to-point and face-to-face manner to assist the enterprise users to make decisions, screens out potential users (users with stable accounts and certain funds), considers that the enterprise should be subjected to product recommendation under the condition if the enterprise has little fund use requirement in a future period, screens out enterprise users with no or little fund use requirement, can reduce time and labor cost, recommends the appropriate financial products to the appropriate enterprise users, meets the requirements of the users on the fund mobility and low risk, realizes accurate positioning, improves user viscosity and increases income.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a financial product recommendation method for enterprise users according to the present invention;
FIG. 2 is a flow chart of cash flow prediction according to the present invention;
FIG. 3 is a flow chart of the financial product recommendation according to the present invention.
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, a financial product recommendation method for enterprise users includes the following steps:
predicting future cash flow of enterprise users by adopting a pre-established time sequence prediction model to obtain a prediction result;
based on the prediction result, screening enterprise users capable of recommending financial products;
recommending corresponding financial product combinations and configuring suggestions to enterprise users capable of recommending financial products based on a pre-established financial product recommendation model and a prediction result;
the time series prediction model is established based on cash flow data of enterprise users.
Specifically, the process of the financial product recommendation method for enterprise users comprises two major parts: cash flow prediction and financial product recommendation.
Cash flow prediction section:
as shown in fig. 2, the steps are as follows:
step 1: reading cash flow data of enterprise users from a database or a data table;
step 2: data preprocessing, namely preparing data for establishing a model;
the cash flow data for each enterprise is processed as follows:
calculating the cash flow rate:
the cash flow rate is defined as follows
Here Balance t The account balance of the enterprise user's account at the end of the t-period is banking. If taking month as time unit, balance t Balance of account of enterprise user at the end of month t, balance t-1 For the account balance of the enterprise user at the end of month (t-1), ΔCF t The cash flow rate for that month. ΔCF t Higher represents more cash flows in and less cash out per month for enterprise users, and lower represents less cash flows in and more cash out.
And carrying out data preprocessing on the existing cash flow rate according to a conventional preprocessing mode. Wherein, the content of the data preprocessing includes but is not limited to missing value processing and abnormal value processing.
In addition, an average ΔCF of the enterprise users is calculated t The value of the sum of the values,
step 3: establishing a time sequence prediction model;
note that the time series model selection: given that the cash flow data of an enterprise is generally of a small magnitude, an autoregressive model is used. (if the dataset is more complex, it may be considered to build a machine learning model or a deep learning model).
There is a high probability that enterprise cash flows will be seasonal, and the following model is built for each enterprise cash flow:
a) If there is no seasonal, then:
b) Seasonal (monthly periodicity) exists, then:
wherein, delta CF is taken as a time unit of month t For the cash flow rate of month t, ΔCF t-i Cash rheology, ΔCF, expressed as month (t-i) t-12i Cash rheology expressed as month (t-12 i), c as a constant term, ε t Expressed as random error terms, A i Expressed as autocorrelation coefficients, and p expressed as an order.
Step 4: and predicting the cash flow conditions of the following three months by using a corresponding time sequence prediction model of each enterprise to obtain a prediction result.
The prediction result and averageAs input for the next partial financial product recommendation.
Financial product recommendation part:
as shown in fig. 3, the steps are as follows:
step 1: based on the cash flow prediction result in the last part, screening enterprise users capable of recommending financial products;
the screening conditions were as follows:
when the average value of the predicted cash flow change rate for N months is greater than a certain threshold value, determining that a certain amount of cash exists in the enterprise account, and recommending products to the enterprise account can be considered, namely:
wherein,for the predicted cash flow change rate for the next i months, if i=1,/i->Then the predicted cash flow change rate representing the next month,/->The average of the predicted cash flow rate of change over N months is shown.
If it isIf the balance is larger than a certain threshold value, the enterprise can be considered to have more stable or certain increase of the balance of the account for N months in the future, and users of the enterprise are managedAnd recommending financial products.
In one embodiment, N is 3, then
Wherein,for the predicted cash flow change rate for the next i months, if i=1,/i->Then the predicted cash flow change rate representing the next month,/->The average of the predicted cash flow change rates for 3 months is shown.
If it isIf the account balance of the enterprise is more stable or has a certain increase in the future 3 months, the enterprise user can be considered to recommend financial products.
It should be noted that the screening conditions in this example are 3 months, and longer times, say 6 months, are also contemplated; shorter times, such as 1 month or 2 months, are also possible.
Step 2: establishing a financial product recommendation model;
financial product information: since businesses prefer risk-free products, deposit products are recommended to businesses, including short-term deposit (short-term notice deposit) or long-term deposit (regular deposit), which ensure that the business has a certain liquidity of its cash assets while ensuring that the business has a profit.
Assuming that there are M short-term deposit products, the corresponding product yields are:
assume that there are NLong-term deposit products, which correspond to a product yield of:
it should be noted that two products are recommended simultaneously in consideration of the diversity of products. The following is assumed:
the sum of weights of all recommended products must be 1.0 (no lever must be opened);
the weight of each product must be greater than 0 (must not be empty);
at least one short-term deposit product is recommended.
Different for different enterprise usersThe bank gives a corresponding interest rate (namely, a total benefit rate) R;
if the average cash flow change rate value of the enterprise user is larger, the larger cash left in the enterprise account is indicated, and the larger the income obtainable by the bank is; conversely, if the average cash flow rate change value for an enterprise user is smaller, this indicates that the less cash is left in the enterprise account, the less revenue is available to the bank. Thus, for enterprise users with larger values of average cash flow change rate, banks are more prone to give higher interest rates; for enterprise users with smaller values for average cash flow change rates, banks tend to give less interest.
The expression of the financial product recommendation model is as follows:
s.t.
wherein: w (w) i : representing the weight, namely the ratio of the corresponding product in the product combination, wherein the numerical value is between 0 and 1; i=1, …, M represents a short-term deposit product, i=m+1, …, N represents a long-term deposit product; x is x i : a control variable, the value of which is not 0, namely 1; r is (r) i : product yield; r: the bank is willing to provide a rate of return.
Step 3: based on the model results, corresponding deposit product combinations are recommended to the enterprise users, as well as configuration suggestions.
The invention designs a financial product recommendation method aiming at enterprise users, which starts from the fund situation of each enterprise user, firstly establishes a cash flow prediction model to predict the cash flow situation of the enterprise in a future period, then combines the prediction results to provide the most reasonable and most suitable financial products and configuration schemes for the enterprise in a point-to-point and face-to-face manner to assist the enterprise users to make decisions, screens out potential users (users with stable accounts and certain funds), considers that the enterprise should be subjected to product recommendation under the condition if the enterprise has little fund use requirement in a future period, screens out enterprise users with no or little fund use requirement, can reduce time and labor cost, recommends the appropriate financial products to the appropriate enterprise users, meets the requirements of the users on the fund mobility and low risk, realizes accurate positioning, improves user viscosity and increases income.
Example two
The invention also provides a financial product recommendation system for enterprise users, which comprises:
the prediction model construction module is used for building a time sequence prediction model based on cash flow data of enterprise users;
the prediction module is used for predicting future cash flow of the enterprise user by adopting a time sequence prediction model to obtain a prediction result;
the screening module is used for screening enterprise users capable of recommending financial products based on the prediction result;
the product recommendation model construction module is used for building a financial product recommendation model;
and the product recommendation module is used for recommending corresponding financial product combinations and configuring suggestions to enterprise users based on the financial product recommendation model and the prediction result.
Example III
The invention also provides computer equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the financial product recommendation method for enterprise users when executing the computer program.
Example IV
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the financial product recommendation method for enterprise users according to the invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the foregoing embodiments are merely for illustrating the technical aspects of the present invention and not for limiting the scope thereof, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the present invention after reading the present invention, and these changes, modifications or equivalents are within the scope of the invention as defined in the appended claims.
Claims (10)
1. A financial product recommendation method for enterprise users is characterized by comprising the following steps:
predicting future cash flow of enterprise users by adopting a pre-established time sequence prediction model to obtain a prediction result;
based on the prediction result, screening enterprise users capable of recommending financial products;
recommending corresponding financial product combinations and configuring suggestions to enterprise users capable of recommending financial products based on a pre-established financial product recommendation model and a prediction result;
the time series prediction model is established based on cash flow data of enterprise users.
2. The financial product recommendation method for an enterprise user according to claim 1, wherein the time series prediction model is further comprised of preprocessing cash flow data of the enterprise user before the time series prediction model is built, the preprocessing comprising:
calculating cash flow rate of the enterprise user;
processing the missing value and the abnormal value of the cash flow rate;
an average cash flow rate for the business user is calculated based on the processed cash flow rates.
3. The financial product recommendation method for an enterprise user according to claim 2, wherein the calculation expression of the cash flow rate of the enterprise user is:
wherein, delta CF is taken as a time unit of month t For cash flow rate at month t, balance t Balance of account of enterprise user at the end of month t, balance t-1 Account balances for business users at the end of month (t-1).
4. The financial product recommendation method for enterprise users according to claim 1, wherein the model expression of the time series prediction model is:
a) If there is no seasonal, then:
b) If seasonal, then:
wherein, delta CF is taken as a time unit of month t For the cash flow rate of month t, ΔCF t-i Cash rheology, ΔCF, expressed as month (t-i) t-12i Cash rheology expressed as month (t-12 i), c as a constant term, ε t Expressed as random error terms, A i Expressed as autocorrelation coefficients, and p expressed as an order.
5. The financial product recommendation method for enterprise users according to claim 1, wherein the screening of enterprise users capable of conducting financial product recommendation based on the prediction result specifically comprises the following steps:
when the average value of the predicted cash flow change rate for N months is larger than a set threshold value, the enterprise user is judged to be the enterprise user recommending financial products, and the average value is shown as the following formula:
wherein,for the predicted cash flow change rate for the next i months, i is 1 to N, +.>Is the average value of the predicted cash flow change rate for N months.
6. The financial product recommendation method for enterprise users according to claim 1, wherein the expression of the financial product recommendation model is:
s.t.
wherein w is i : representing weights, the values being between 0 and 1; m represents a short-term deposit product, and N represents a long-term deposit product; x is x i : a control variable, the value of which is not 0, namely 1; r is (r) i : product yield; r: the bank is willing to provide a rate of return.
7. The financial product recommendation method for enterprise users according to claim 6, wherein the conditions for recommending corresponding financial product combinations to enterprise users are as follows:
two products are recommended simultaneously, and at least one short-term deposit product is included;
the sum of the weights of the two products is 1.0;
each product has a weight greater than 0.
8. A financial product recommendation system for enterprise users, comprising:
the prediction module is used for predicting future cash flow of the enterprise user by adopting a pre-established time sequence prediction model to obtain a prediction result;
the screening module is used for screening enterprise users capable of recommending financial products based on the prediction result;
the product recommendation module is used for recommending corresponding financial product combinations and configuring suggestions to enterprise users capable of recommending the financial products based on a pre-established financial product recommendation model and a prediction result;
wherein the time series prediction model is established based on cash flow data of the enterprise user.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the financial product recommendation method for an enterprise user as claimed in any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the financial product recommendation method for enterprise users as claimed in any one of claims 1 to 7.
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