CN116071165A - Financial product prediction method and financial product prediction device - Google Patents
Financial product prediction method and financial product prediction device Download PDFInfo
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- CN116071165A CN116071165A CN202211389988.6A CN202211389988A CN116071165A CN 116071165 A CN116071165 A CN 116071165A CN 202211389988 A CN202211389988 A CN 202211389988A CN 116071165 A CN116071165 A CN 116071165A
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Abstract
The invention discloses a prediction method and a prediction device of financial products. The prediction method comprises the following steps: acquiring historical characteristic information and historical financial information of a target client; and inputting the historical characteristic information and the historical financial information into a prediction model to obtain a first probability of the predicted characteristic information of the target client and a second probability of the predicted financial information corresponding to the predicted characteristic information. The method and the device can be applied to the data processing field and the big data analysis field, and according to the embodiment of the invention, the characteristic information of the client is predicted, and the related information of the financial product which the client wants to purchase currently is predicted according to the predicted characteristic information, so that the financial product recommended to the client accords with the current wish of the client, and the experience of the client is improved.
Description
Technical Field
The invention relates to the field of data processing and big data analysis, in particular to a prediction method and a prediction device of financial products.
Background
To help customers better manage financial resources, a business bank typically recommends appropriate financial products for the customer according to the needs of the customer, for example, a bank analyzes the needs of the customer according to information that the customer authorizes the bank to use when opening an account or financial products purchased by the customer in the past, and recommends corresponding financial products for the customer.
However, sometimes, the information of the client is not updated timely, so that financial products recommended to the client cannot meet the current-stage requirements of the client, the purchase intention of the client is reduced, and the experience of the client is affected.
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
The embodiment of the invention provides a prediction method of financial products, which predicts the characteristic information of a client and predicts the relevant information of the financial products which the client wants to purchase currently according to the predicted characteristic information, so that the financial products recommended to the client can be more in line with the current willingness of the client, and the experience of the client is improved. The prediction method comprises the following steps: acquiring historical characteristic information and historical financial information of a target client; and inputting the historical characteristic information and the historical financial information into a prediction model to obtain a first probability of the predicted characteristic information of the target client and a second probability of the predicted financial information corresponding to the predicted characteristic information.
The embodiment of the invention provides a device for financial products, which predicts the characteristic information of a client and predicts the relevant information of the financial product which the client wants to purchase currently according to the predicted characteristic information, so that the financial product recommended to the client can be more in line with the current wish of the client, and the experience of the client is improved. The prediction apparatus includes: a first acquisition unit that acquires history feature information and history financial information of a target client; and the first calculation unit inputs the historical characteristic information and the historical financial information into a prediction model to obtain a first probability of the predicted characteristic information of the target client and a second probability of the predicted financial information corresponding to the predicted characteristic information.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the prediction method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program that when executed by a processor implements the above-described prediction method.
Embodiments of the present invention also provide a computer program product comprising a computer program which, when executed by a processor, implements the above-described prediction method.
According to the embodiment of the invention, the characteristic information of the client is predicted, and the related information of the financial product which the client wants to purchase currently is predicted according to the predicted characteristic information, so that the financial product recommended to the client can be more in line with the current wish of the client, and the experience of the client is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a schematic diagram of a prediction method of financial products according to an embodiment of the present invention.
Fig. 2 is another schematic diagram of a prediction method of financial products according to an embodiment of the present invention.
Fig. 3 is another schematic diagram of a prediction method of financial products according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a predicting device for financial products according to an embodiment of the invention.
FIG. 5 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
The embodiment of the invention provides a prediction method of financial products, and fig. 1 is a schematic diagram of the prediction method of financial products in the embodiment of the invention. As shown in fig. 1, the prediction method 100 includes:
step 101: acquiring historical characteristic information and historical financial information of a target client;
step 103: and inputting the historical characteristic information and the historical financial information into a prediction model to obtain a first probability of the predicted characteristic information of the target client and a second probability of the predicted financial information corresponding to the predicted characteristic information.
Therefore, the characteristic information of the client is predicted, and the relevant information of the financial product which the client wants to purchase currently is predicted according to the predicted characteristic information, so that the financial product recommended to the client can be more in line with the current willingness of the client, and the experience of the client is improved.
In at least one embodiment, the predictive model is a model trained based on a hidden Markov model.
A hidden markov model (Hidden Markov Model, hereinafter referred to as "HMM model") is a model used to describe a markov process implying unknown parameters. Two basic assumptions of the HMM model are: (1) the assumption of homogeneous markov, i.e. the assumption that the state of a hidden markov chain at any instant t depends only on the state at the previous instant t, is independent of the states and observations at other instants, and is independent of instant t'. (2) The observability independence assumption, i.e., the assumption that an observation at any instant depends only on the state of the Markov chain at that instant, independent of other observations and states.
The HMM model can be described by 2 state sets and 3 probability matrices: (1) implicit state S, i.e. an implicit state which cannot be obtained by direct observation; (2) observable state O, i.e., a state that can be obtained by direct observation; (3) an initial state probability matrix pi, namely a probability matrix representing an implicit state at an initial moment; (4) implicit state transition probability matrix a, a ij Indicating at time t that the state is S i Under the condition of (1), the state is S at time t+1 j Probability of (2); (5) observation state transition probability matrix B, B ij Indicating at time t that the implicit state is S j Under the condition that the observation state is O i Is a probability of (2).
In at least one embodiment, the historical characteristic information includes personal information of the target customer, and the historical financial information includes information of financial products purchased by the target customer.
For example, the historical characteristic information includes a plurality of combinations of personal information of the target customer, and the historical financial information includes a type of the financial product purchased by the target customer.
In an embodiment of the present invention, the HMM model is described, for example, with the following two state sets and three probability matrices: (1) the implicit state S is, for example, classification of personal details of the client, such as gender, age, income, family member information, etc., and different personal details can be arranged and combined to form a detailed information classification, and different information classifications are respectively marked as S 1 ,S 2 …; (2) the observable state O is information of financial products purchased by clients, such as types or profitability of the financial products, and different gears of types or profitability of different financial products are marked as O 1 ,O 2 ,O 3 … …; (3) an initial state probability matrix pi, for example, a probability matrix representing personal details at an initial time; (4) an implicit state transition probability matrix a, for example, represents the probability that the personal details of the client change from class i to class j; (5) the observation state transition probability matrix B indicates, for example, a probability that the purchased financial product is of type i when the personal details of the customer are classified into class j.
In the embodiment of the invention, for solving the problem of the HMM model, a Baum-Welch algorithm is adopted for solving in at least one embodiment. For example, assume that all observation data q= { Q 1 ,q 2 ,…,q T All implicit states i= { I } 1 ,i 2 ,…,i T }, where q t ∈{O 1 ,O 2 ,…,O n }，i t ∈{S 1 ,S 2 ,…,S n }. According to Baum-Welch algorithm, using machine learningBy increasing the L function, the values of pi and A, B can be solved.
Wherein the L function is represented by the following formula (1):
each element in the initial state probability matrix pi can be calculated by the following formula (2):
each element in the hidden state transition probability matrix a can be calculated by the following formula (3):
each element in the observation state transition probability matrix B can be calculated by the following formula (4):
the process of training the HMM model is described below.
In at least one embodiment, information of the customer who provided the latest personal information and purchased the financial product may be derived from the database as a training set. The method comprises the steps of classifying and encoding historical personal information and latest personal information of clients in a training set respectively, encoding types of historical financial products purchased by the clients when the historical personal information and the latest financial products purchased when the latest personal information are obtained, and training a constructed HMM model by utilizing the historical personal information and the latest personal information of the clients, the historical financial products and the latest financial products based on machine learning.
For example, for a customer in the training set, the type of financial product he purchased can be obtained, thereby obtaining the observation data q= { Q 1 ,q 2 ,…,q T At the initial time, the personal information provided by the client can be considered to be accurate, so that an initial state probability matrix pi can be obtained, and meanwhile, by observing other clients which normally update the personal information, an observation state transition probability matrix B can be obtained. The parameters which can be obtained are imported into a machine learning HMM model, and the implicit state I and the implicit state transition probability matrix A of the client can be solved through a Baum-Welch algorithm. By calculating the parameters at the time t, the parameters at the time t+1 can be obtained, that is, the personal information state of the client at the time can be known, and then the prediction of what type of financial product the client wants to purchase later can be performed, and verification can be performed through the actual latest personal information state of the client and the latest purchased financial product.
Alternatively, the information set of the client derived from the database may be divided into a training set and a verification set, for example, the derived information set may be randomly divided in a predetermined ratio and repeated a predetermined number of times.
Therefore, based on the trained prediction model, the latest personal information of the client and the latest willingness of the client to purchase financial products can be predicted according to the historical personal information of the client. In addition, since the latest personal information of the client is predicted, the recommendation of other services can be developed by using the latest personal information.
Fig. 2 is another schematic diagram of a prediction method of financial products according to an embodiment of the present invention.
In at least one embodiment, as shown in FIG. 2, a prediction method 200 includes:
step 201: acquiring prediction characteristic information corresponding to a first probability larger than a first threshold value;
step 203: comparing the second probability of the predicted financial information corresponding to the obtained predicted characteristic information with a second threshold value;
step 205: and acquiring predicted financial information corresponding to the second probability larger than the second threshold.
Therefore, financial information which is most in line with the latest wish of the customer is obtained, and customer experience is improved.
The embodiment of the invention does not limit the values of the first threshold value and the second threshold value, and can be set according to actual needs or experience.
Fig. 3 is another schematic diagram of a prediction method of financial products according to an embodiment of the present invention.
In at least one embodiment, as shown in FIG. 3, a prediction method 300 includes:
step 301: and extracting financial products corresponding to the acquired predicted financial information.
The embodiment of the invention does not limit the specific strategy for extracting financial products, and can be set according to actual needs. For example, the predicted financial information is the type of the predicted financial product, and in step 301, a plurality of financial products corresponding to the type of the predicted financial product may be extracted from the database. In addition, the financial products with highest yield can be selected from a plurality of financial products corresponding to the predicted types of the financial products according to the order of the yield of the financial products from high to low, and the financial products can be recommended to clients. For another example, the predicted financial information is a predicted financial product yield range, and in step 301, a plurality of financial products corresponding to the predicted yield range may be extracted from the database.
According to the embodiment of the invention, the characteristic information of the client is predicted, and the related information of the financial product which the client wants to purchase is predicted according to the predicted characteristic information, so that the financial product recommended to the client can be more in line with the current wish of the client, and the experience of the client is improved.
The invention also provides a prediction device for financial products, and the principle of solving the problem of the prediction device in the embodiment of the invention is similar to that of the prediction method, so that the implementation of the prediction device can be referred to the implementation of the prediction method, and the repetition is omitted.
Fig. 4 is a schematic diagram of a predicting device for financial products according to an embodiment of the invention.
As shown in fig. 4, the prediction apparatus 400 includes a first acquisition unit 401 and a first calculation unit 402. The first acquisition unit 401 acquires history feature information and history financial information of a target client; the first calculation unit 402 inputs the historical feature information and the historical financial information into a prediction model, and obtains a first probability of the predicted feature information of the target client and a second probability of the predicted financial information corresponding to the predicted feature information.
In at least one embodiment, the predictive model is a model trained based on a hidden Markov model.
In at least one embodiment, as shown in fig. 4, the prediction apparatus 400 may further include a second acquisition unit 403, a comparison unit 404, and a third acquisition unit 405. The second obtaining unit 403 obtains prediction feature information corresponding to a first probability greater than a first threshold; the comparing unit 404 compares the second probability of the predicted financial information corresponding to the obtained predicted feature information with a second threshold; the third acquiring unit 405 acquires predicted financial information corresponding to a second probability greater than a second threshold.
In at least one embodiment, as shown in fig. 4, the prediction apparatus 400 may further include an extraction unit 406, and the extraction unit 406 extracts a financial product corresponding to the obtained predicted financial information.
In at least one embodiment, the historical characteristic information includes personal information of the target customer, and the historical financial information includes information of financial products purchased by the target customer.
In at least one embodiment, the historical characteristic information includes a plurality of combinations of personal information of the target customer, and the historical financial information includes a type of the financial product purchased by the target customer.
In addition, the prediction apparatus 400 of the embodiment of the present invention may further have a communication unit, and the prediction apparatus 400 may communicate with a database center storing client information through the communication unit, for example, obtain history feature information, history financial information, and the like of the target client.
Therefore, the characteristic information of the client is predicted, and the relevant information of the financial product which the client wants to purchase currently is predicted according to the predicted characteristic information, so that the financial product recommended to the client can be more in line with the current willingness of the client, and the experience of the client is improved.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the prediction method when executing the computer program.
FIG. 5 is a schematic diagram of a computer device according to an embodiment of the invention.
As shown in fig. 5, the computer device 500 includes a memory 501 and a processor 502, the memory 501 being coupled to the processor 502, and the memory 501 having stored therein a computer program that, when executed by the processor 502, is capable of implementing the above-described prediction methods 100 to 300.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program that when executed by a processor implements the above-described prediction method.
Embodiments of the present invention also provide a computer program product comprising a computer program which, when executed by a processor, implements the above-described prediction method.
According to the embodiment of the invention, the characteristic information of the client is predicted, and the related information of the financial product which the client wants to purchase currently is predicted according to the predicted characteristic information, so that the financial product recommended to the client can be more in line with the current wish of the client, and the experience of the client is improved.
It should be noted that, in the technical scheme of the invention, the acquisition, storage, use, processing and the like of the data all conform to the relevant regulations of national laws and regulations.
The user information in the embodiment of the invention is obtained through legal compliance approaches, and the user information is obtained, stored, used, processed and the like through user authorization consent.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, 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 schematic and/or flow chart diagrams and/or block diagrams of methods, apparatus, systems, and computer program products according to embodiments of the present invention. It will be understood that each step and/or operation and/or flow and/or block of the diagrams and/or flowchart illustration and/or block diagram illustration, and combinations of steps and/or operations and/or flow and/or blocks in the diagrams and/or flowchart illustration, 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 schematic step or steps and/or flowchart step or steps 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 diagram step or steps and/or 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 schematic diagram step or steps and/or flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (15)
1. A method for predicting financial products, the method comprising:
acquiring historical characteristic information and historical financial information of a target client;
and inputting the historical characteristic information and the historical financial information into a prediction model to obtain a first probability of the predicted characteristic information of the target client and a second probability of the predicted financial information corresponding to the predicted characteristic information.
2. The method for predicting according to claim 1, wherein,
the prediction model is a model which is obtained based on hidden Markov model training.
3. The prediction method according to claim 1, characterized in that the prediction method further comprises:
acquiring prediction characteristic information corresponding to a first probability larger than a first threshold value;
comparing the second probability of the predicted financial information corresponding to the obtained predicted characteristic information with a second threshold value;
and acquiring predicted financial information corresponding to the second probability larger than the second threshold.
4. A prediction method according to claim 3, characterized in that the prediction method further comprises:
and extracting financial products corresponding to the acquired predicted financial information.
5. The method for predicting according to claim 1, wherein,
the historical characteristic information comprises personal information of the target client, and the historical financial information comprises information of financial products purchased by the target client.
6. The prediction method according to claim 5, wherein,
the historical characteristic information includes a plurality of combinations of personal information of the target customer, and the historical financial information includes a type of the financial product purchased by the target customer.
7. A forecast device for financial products, the forecast device comprising:
a first acquisition unit that acquires history feature information and history financial information of a target client;
and the first calculation unit inputs the historical characteristic information and the historical financial information into a prediction model to obtain a first probability of the predicted characteristic information of the target client and a second probability of the predicted financial information corresponding to the predicted characteristic information.
8. The prediction apparatus according to claim 7, wherein,
the prediction model is a model which is obtained based on hidden Markov model training.
9. The prediction device according to claim 7, characterized in that the prediction device further comprises:
a second acquisition unit that acquires prediction feature information corresponding to a first probability that is greater than a first threshold;
a comparison unit for comparing the second probability of the predicted financial information corresponding to the obtained predicted characteristic information with a second threshold value;
and a third acquisition unit that acquires predicted financial information corresponding to a second probability greater than a second threshold.
10. The prediction device according to claim 9, characterized in that the prediction device further comprises:
and an extraction unit that extracts financial products corresponding to the acquired predicted financial information.
11. The prediction apparatus according to claim 7, wherein,
the historical characteristic information comprises personal information of the target client, and the historical financial information comprises information of financial products purchased by the target client.
12. The prediction apparatus according to claim 11, wherein,
the historical characteristic information includes a plurality of combinations of personal information of the target customer, and the historical financial information includes a type of the financial product purchased by the target customer.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the prediction method of any of claims 1 to 6 when executing the computer program.
14. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the prediction method of any one of claims 1 to 6.
15. A computer program product, characterized in that it comprises a computer program which, when executed by a processor, implements the prediction method of any one of claims 1 to 6.
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