CN112669154A

CN112669154A - Foreign currency exchange business development prediction implementation method and device and computer equipment

Info

Publication number: CN112669154A
Application number: CN202011626428.9A
Authority: CN
Inventors: 黄文强; 胡传杰; 胡玮; 黄雅楠; 徐晨敏
Original assignee: Bank of China Ltd
Current assignee: Bank of China Ltd
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2021-04-16
Anticipated expiration: 2040-12-31
Also published as: CN112669154B

Abstract

The application provides a foreign currency exchange business starting prediction implementation method, a device and computer equipment, wherein before a target financial institution starts a foreign currency exchange business, passenger flow of the target financial institution within a first time period, first business handling data of a first-level client and exchange consultation information waiting prediction data aiming at the foreign currency exchange business are obtained through the computer equipment, after corresponding data characteristics are obtained through characteristic extraction of the data to be predicted, the data characteristics can be analyzed based on business starting conditions corresponding to the target financial institution to determine whether the target financial institution is suitable for starting a prediction result of the foreign currency exchange business, the situation that the target financial institution directly starts the foreign currency exchange business without investigation and analysis is avoided, and very few clients which come from the target financial institution to handle the financial institution are easy to occur, a traffic loss problem occurs.

Description

Foreign currency exchange business development prediction implementation method and device and computer equipment

Technical Field

The application relates to the technical field of data processing, in particular to a foreign currency exchange business development prediction implementation method, a foreign currency exchange business development prediction implementation device and computer equipment.

Background

Nowadays, in order to facilitate the capital requirements of customers, financial institutions (such as various banking outlets) usually offer a variety of services, for example, foreign currency exchange services, which are usually currency circulating outside the currency system. Foreign currency exchange is an over-the-counter service provided to individual customers, including the services of buying foreign currencies, selling foreign currencies, and exchanging one type of foreign currency for another. For financial institutions, profits may be realized through exchange rate changes between different currencies.

However, in practical applications, the cost for dispatching foreign currency to financial institutions is often high, but the service fee charged by some financial institutions for the client to handle the foreign currency exchange business is lower than the dispatching cost fee, so that the financial institutions can open the foreign currency exchange business to cause a loss phenomenon.

Disclosure of Invention

In view of the above, in order to solve the above technical problems, the present application provides the following technical solutions:

on one hand, the application provides a foreign currency exchange business development prediction implementation method, which comprises the following steps:

acquiring data to be predicted of a target financial institution, wherein the data to be predicted comprises passenger flow in a first time length, first business transaction data of a first-level client and exchange consultation information aiming at foreign currency exchange business;

respectively extracting characteristics of the passenger flow, the first service transaction data and the exchange consultation information to obtain corresponding data characteristics;

and analyzing the obtained data characteristics based on the service opening conditions corresponding to the target financial institution to obtain a prediction result of whether the target financial institution is suitable for opening the foreign currency exchange service, wherein the service opening conditions at least comprise service profit and loss conditions and service handling client conditions.

Optionally, the analyzing the obtained multiple data features based on the service issuing condition corresponding to the target financial institution to obtain a prediction result of whether the target financial institution is suitable for issuing the foreign currency exchange service includes:

inputting the obtained data characteristics into a foreign currency exchange business prediction model to obtain a prediction result of whether the target financial institution is suitable for issuing the foreign currency exchange business;

and the foreign currency exchange business prediction model is obtained by training sample data characteristics according to business starting conditions corresponding to the target financial institution based on a neural network algorithm.

Optionally, the inputting the obtained multiple data characteristics into a foreign currency exchange business prediction model to obtain a prediction result of whether the target financial institution is suitable for issuing the foreign currency exchange business includes:

inputting the obtained data characteristics into a foreign currency exchange business prediction model to obtain a target prediction score suitable for the target financial institution to open the foreign currency exchange business;

acquiring a score threshold corresponding to the region where the target financial institution is located;

detecting whether the target prediction score is larger than the score threshold value or not to obtain a corresponding detection result;

and generating prediction prompt information corresponding to the detection result, and sending the prediction prompt information to a preset terminal associated with the target financial institution.

Optionally, the inputting the obtained multiple data characteristics into a foreign currency exchange service prediction model to obtain a prediction result of whether the target financial institution is suitable for issuing the foreign currency exchange service, further includes:

obtaining a prediction report matched with the detection result, wherein the prediction report comprises the influence weight of each data to be predicted on the detection result;

wherein, when the detection result is that the target prediction score is less than or equal to the score threshold, the prediction report further includes a service offering condition that the data characteristic of the target financial institution is not satisfied.

Optionally, the inputting the obtained multiple data characteristics into a foreign currency exchange business prediction model to obtain a target prediction score suitable for the target financial institution to open the foreign currency exchange business includes:

inputting the obtained plurality of data characteristics into a foreign currency exchange business prediction model to obtain the characteristic prediction scores of the data characteristics, which are suitable for the target financial institution to open the foreign currency exchange business;

and according to the respective prediction weights of the data characteristics, carrying out weighted operation on the obtained characteristic prediction scores to obtain the target prediction score which is suitable for the target financial institution to start the foreign currency exchange business.

Optionally, the training process of the foreign currency exchange business prediction model includes:

acquiring sample mechanism identification information of a sample financial mechanism which has opened the foreign currency exchange service;

acquiring data to be screened related to the sample mechanism identification information, wherein the data to be screened comprises sample passenger flow volume, first sample own attribute information, second sample identity attribute information, foreign currency profit information and foreign currency scheduling cost information which are generated in a first time period, and the first sample own attribute information is identity attribute information of a first sample client applying for handling any service in the sample financial mechanism in the first time period; the second sample identity attribute information refers to identity attribute information of a second sample client applying for handling the foreign currency exchange service at the sample financial institution within the first time period;

analyzing each data to be screened, and screening sample data meeting a training condition, wherein the training condition refers to the change of the corresponding screening data, and the influence value of the training condition on foreign currency exchange business issued by the sample financial institution is greater than an influence threshold value;

respectively extracting the characteristics of the screened sample data to obtain corresponding sample characteristic vectors;

inputting the obtained multiple sample characteristic vectors into a preset neural network for learning until constraint conditions are met, and determining a foreign currency exchange business prediction model according to a neural network structure obtained by final learning;

wherein the preset neural network comprises an error back propagation neural network based on a genetic algorithm.

Optionally, the process of acquiring the service offering condition includes:

acquiring foreign currency transaction data of the region where the target financial institution is located, and handling the average passenger flow of the foreign currency exchange service within the first time length in the financial institution which opens the foreign currency exchange service in the same-level region;

determining a service profit and loss condition and a service handling client condition for the financial institution to open the foreign currency exchange service based on the foreign currency transaction data and the average passenger flow;

and determining the service opening condition aiming at the target financial institution according to the service profit and loss condition and the service handling client condition.

Optionally, the acquiring data to be predicted of the target financial institution includes:

receiving a foreign currency exchange business prediction request sent by a business terminal of a target financial institution, wherein the foreign currency exchange business prediction request carries target institution identification information of the target financial institution;

and responding to the foreign currency exchange business prediction request, and inquiring data to be predicted related to the target mechanism identification information.

In another aspect, the present application provides a foreign currency exchange business development prediction implementation apparatus, where the apparatus includes:

the system comprises a to-be-predicted data acquisition module, a to-be-predicted data acquisition module and a to-be-predicted data acquisition module, wherein the to-be-predicted data acquisition module is used for acquiring to-be-predicted data of a target financial institution, and the to-be-predicted data comprises passenger flow within a first duration, first service transaction data of first-level clients and exchange consultation information aiming at foreign currency exchange services;

the characteristic extraction module is used for respectively extracting characteristics of the passenger flow volume, the first service transaction data and the exchange consultation information to obtain corresponding data characteristics;

and the analysis and prediction module is used for analyzing the obtained data characteristics based on the service opening conditions corresponding to the target financial institution to obtain a prediction result of whether the target financial institution is suitable for opening the foreign currency exchange service, wherein the service opening conditions at least comprise service profit and loss conditions and service handling client conditions.

In yet another aspect, the present application provides a computer device, comprising: at least one communication interface, at least one memory, and at least one processor, wherein:

the memory is used for storing a program for realizing the foreign currency exchange business starting prediction realization method;

the processor is configured to load and execute the program stored in the memory to implement the steps of the foreign currency exchange business development prediction implementation method.

Therefore, the application provides a foreign currency exchange business starting prediction implementation method, a foreign currency exchange business starting prediction implementation device and computer equipment, before a target financial institution starts a foreign currency exchange business, the computer equipment can acquire the passenger flow of the target financial institution within a first time period, first business handling data of a first-level client and conversion consultation information waiting prediction data aiming at the foreign currency exchange business, after the characteristics of the data to be predicted are extracted to obtain corresponding data characteristics, the data characteristics can be analyzed based on the business starting conditions corresponding to the target financial institution to determine whether the target financial institution is suitable for starting the prediction result of the foreign currency exchange business, the situation that the target financial institution directly starts the foreign currency exchange business without investigation and analysis is avoided, and very few clients for the target financial institution to handle the financial institution are easy to occur, a traffic loss problem occurs.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

FIG. 1 is a flow chart diagram illustrating an alternative example of a foreign currency exchange business development forecasting implementation method proposed by the present application;

FIG. 2 is a flow chart diagram illustrating yet another alternative example of a foreign currency exchange business development forecasting implementation method proposed by the present application;

FIG. 3 is a flow chart diagram illustrating yet another alternative example of a foreign currency exchange business development forecasting implementation method proposed by the present application;

FIG. 4 is a flow chart diagram illustrating yet another alternative example of a foreign currency exchange transaction initiation prediction implementation method proposed in the present application;

fig. 5 is a schematic structural diagram showing an alternative example of the foreign currency exchange transaction initiation prediction implementation device proposed in the present application;

fig. 6 is a hardware configuration diagram of an alternative example of a computer device suitable for implementing the foreign currency exchange business development prediction method and apparatus proposed by the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.

It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present application may be combined with each other without conflict.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements. An element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

In the description of the embodiments herein, "/" means "or" unless otherwise specified, for example, a/B may mean a or B; "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality" means two or more than two. The terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

Additionally, flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Referring to fig. 1, a flow chart of an optional example of a method for implementing foreign currency exchange business development prediction provided by the present application is illustrated, where the method may be applied to a computer device, and the computer device may be a server or a business terminal with certain data processing capability. As shown in fig. 1, the method may include:

step S11, acquiring data to be predicted of the target financial institution;

the problem that the foreign currency exchange service set by the financial institution is damaged due to the fact that the number of clients handling the foreign currency exchange service by the financial institution is small and the service fee for handling the received service is far lower than the scheduling cost fee is solved when the financial institution falsely sets the foreign currency exchange service. The embodiment of the application provides that before a financial institution sets up the foreign currency exchange service, whether the financial institution is suitable for setting up the financial institution is predicted.

Specifically, any financial institution desiring to start a foreign currency exchange service may be marked as a target financial institution, and may initiate a foreign currency exchange service prediction request to the computer device through the service terminal, so that the computer device queries, according to target institution identification information of the target financial institution, such as a unique identifier of an institution code, carried in the request, each data to be predicted associated with the target institution identification information, that is, related data used for predicting whether the target financial institution is suitable for starting a foreign currency exchange service.

The obtained data to be predicted may include contents such as a passenger flow volume within a first time period (for example, a day, a week, etc., where the present application does not limit a specific value of the first time period), first service transaction data (for example, total number of service transactions, etc.) of a first-level client, and exchange consultation information (for example, number of consultation times) for foreign currency exchange service, where the present application does not limit the contents included in the data to be predicted, and may be determined according to a situation. The first-level customers can be important customers such as VIP customers of the target financial institution, and the level division mode of the target financial institution is not detailed in the application.

Step S12, respectively extracting characteristics of the passenger flow, the first service transaction data and the exchange consultation information contained in the data to be predicted to obtain corresponding data characteristics;

in practical application, for data types and representation modes of different types of data to be predicted, which are often different, even unqualified data contents, for convenience of subsequent processing, feature extraction may be performed on each acquired data to be predicted to obtain a corresponding feature value.

In some embodiments, the data feature obtained after the feature extraction may be a digitized feature obtained by performing feature extraction on corresponding data to be predicted, and then, a corresponding feature vector may be obtained by performing normalization processing on the digitized feature; or directly extracting the features of each data to be predicted to obtain the corresponding feature vector, that is, the data features may be feature vectors or feature parameters capable of indicating the corresponding data to be predicted, and the content of the data features is not limited in the present application.

And step S13, analyzing the obtained multiple data characteristics based on the service starting condition corresponding to the target financial institution, and obtaining a prediction result of whether the target financial institution is suitable for starting foreign currency exchange service.

In the embodiment of the present application, the service opening conditions may at least include service profit and loss conditions and service handling client conditions, that is, in the case of evaluating whether a financial institution is suitable for opening a foreign currency exchange service, it is necessary to consider not only whether the foreign currency exchange service opened by the financial institution can be profitable, but also information such as a passenger flow rate that the financial institution may apply for handling the foreign currency exchange service, and certainly, when evaluating whether a financial institution is suitable for opening a foreign currency exchange service, the service opening conditions are not limited to the two service opening conditions listed in the present application.

In practical applications, the service offering conditions may be determined comprehensively in combination with information such as the scale of the target financial institution, the economic development conditions of the region where the target financial institution is located, and regulations, so that for different financial institutions, the corresponding service offering conditions may be different, or a plurality of financial institutions having the same service offering conditions may exist, and the content of the service offering conditions corresponding to each financial institution and the determination method thereof are not limited in the present application as appropriate.

According to the mode, after the content of the service offering condition of the target financial institution is determined, the acquired data characteristics of the target financial institution in various aspects can be analyzed according to the content to determine whether the current operation conditions of the target financial institution in various aspects meet the service offering condition or not, and if the matching degree with the service offering condition is up to the certain degree, the prediction result of whether the target financial institution is suitable for offering the foreign currency exchange service or not is obtained. If the matching degree reaches a certain threshold value, the obtained prediction result can comprise parameters that the target financial institution is suitable for issuing foreign currency exchange business, and can also comprise parameters such as matching conditions of the issuing conditions of each business and the like according to needs.

Correspondingly, if the obtained matching degree does not reach the threshold value, the obtained prediction result may include that the target financial institution is not suitable for issuing foreign currency exchange services, and may further include parameters such as matching conditions of the issuing conditions of each service according to needs, so that the target financial institution can obtain an improvement scheme for the current operating condition according to the obtained prediction result content under the condition that the target financial institution wishes to continue to issue foreign currency exchange services, so that the target financial institution can more and more match the service issuing conditions, and the specific implementation process is not described in detail in the present application.

In summary, in the embodiment of the present application, before the target financial institution opens the foreign currency exchange service, the passenger flow volume of the target financial institution within the first duration, the first service transaction data of the first-level client, and the exchange consultation information waiting prediction data for the foreign currency exchange service may be obtained through the computer device, and after the corresponding data features are obtained through extracting the features of the data to be predicted, the data features may be analyzed based on the service opening conditions corresponding to the target financial institution to determine whether the target financial institution is suitable for opening the prediction result of the foreign currency exchange service, so that the problem that the target financial institution directly opens the foreign currency exchange service without investigation and analysis is easily caused, and the number of clients for the target financial institution to handle the financial institution is very small, and the problem of service loss occurs.

Referring to fig. 2, a flow chart of another optional example of the foreign currency exchange business development prediction implementation method provided in the present application is schematically illustrated, where this embodiment may be an optional detailed implementation of the foreign currency exchange business development prediction implementation method described in the foregoing embodiment, and as shown in fig. 2, the method may include:

step S21, acquiring data to be predicted of the target financial institution;

step S22, respectively extracting characteristics of the passenger flow, the first service transaction data and the exchange consultation information contained in the data to be predicted to obtain corresponding data characteristics;

regarding the implementation processes of step S21 and step S22, reference may be made to the description of corresponding parts in the foregoing embodiments, which are not described in detail in this embodiment.

Step S23, inputting the obtained data characteristics into a foreign currency exchange business prediction model to obtain a target prediction score suitable for starting foreign currency exchange business for a target financial institution;

it can be seen that, in the embodiment of the present application, the sample data characteristics may be trained according to the service development conditions corresponding to the target financial institution based on the neural network algorithm to obtain the foreign currency exchange service prediction model, so that, in practical applications, for any financial institution that does not develop a foreign currency exchange service, before the foreign currency exchange service is desired to be developed, the input multiple data characteristics of the foreign currency exchange service prediction model may be continuously processed to obtain the prediction result of whether the foreign currency exchange service is suitable for development, but the implementation method of the pre-training process of the foreign currency exchange service prediction model is not limited in the present application.

In some embodiments proposed in the present application, as shown in fig. 3, the training process of the foreign currency exchange business prediction model may include, but is not limited to, the following steps:

step S31, obtaining sample organization identification information of a sample financial organization which has opened foreign currency exchange business;

in practical application, the relevant operation information of the sample financial institutions which have already opened foreign currency exchange business at present can be collected to form sample data for training the foreign currency exchange business prediction model, under normal conditions, in order to improve the training reliability of the foreign currency exchange business prediction model and the accuracy of the prediction result, the sample data of a plurality of sample financial institutions can be obtained, and the required foreign currency exchange business prediction model is obtained through repeated training.

Therefore, the sample financial institutions to which the sample data to be acquired belongs can be determined first, and the sample financial institutions are determined according to various information such as the operation scale of the target financial institution and the economic development level of the region where the target financial institution is located; of course, the sample financial institution can be randomly selected from financial institutions which already issue foreign currency exchange business to realize the training of the prediction model, and the determination mode of the sample financial institution is not limited by the application and can be determined according to the situation.

After the required sample financial institution is determined, in order to obtain various business information of the sample financial institution, the unique identification information of the sample financial institution may be determined first and recorded as sample institution identification information to distinguish and identify different financial institutions. The sample organization identification information can be identification information with unique characteristics such as organization codes, tax acceptance numbers, credit codes and the like, and the specific content of the sample organization identification information is not limited in the application.

Step S32, acquiring data to be screened associated with the sample mechanism identification information;

in the embodiment of the present application, the acquired data to be screened may include sample passenger flow volume generated in a first time period (e.g., a day, a week, etc.), first sample identity attribute information, second sample identity attribute information, foreign currency profit information, and foreign currency scheduling cost information (e.g., cost information required by the sample financial institution to dial foreign currency from a customer to a hand in each first time period), etc., but is not limited to the data content to be screened listed in the embodiment, and may be determined as the case may be.

The first sample self-identity attribute information can be identity attribute information of a first sample client applying for handling any business in the sample financial institution in the first time length, such as information of occupation, academic calendar, age and the like of the corresponding first sample client; the second sample identity attribute information may refer to identity attribute information of a second sample client applying for handling the foreign currency exchange service at the sample financial institution within the first time period, such as occupation, academic calendar, age, and other information of the second sample client.

It is to be understood that, in the embodiment of the present application, for convenience of description, a client applying for any transaction at the sample financial institution is referred to as a first sample client, and a client applying for a foreign currency exchange transaction at the sample financial institution is referred to as a second sample client, and it is understood that the first sample client group may include the second sample client group, and the present application does not limit the specific first sample client and the second sample client.

In some embodiments provided by the application, since the passenger flow volumes for handling foreign currency exchange services in different seasons are often different in a year, in order to further improve the accuracy of the prediction model obtained by training, when the data to be screened is obtained, the data to be screened related to the identification information of the sample institution may be obtained in a corresponding time period of the past adjacent year, and the specific obtaining process is not described in detail.

Step S33, analyzing each data to be screened, and screening sample data meeting training conditions;

in practical application, for each data to be screened acquired above, there may be some data, which has little influence on whether the corresponding sample financial institution launches the foreign currency exchange service, and is almost data that can be ignored, and such data is basically useless for training the foreign currency exchange service prediction model, so that the present application may determine the training condition of the sample data for constructing the prediction model in advance according to the above, for example, the change of the corresponding screening data has an influence value on the foreign currency exchange service launched by the sample financial institution that is greater than the influence threshold (the specific numerical value is not limited, and may be determined according to the situation), but is not limited to this content of the training condition.

Then, the computer device may analyze each acquired data to be screened according to the training condition, remove useless data that do not meet the training condition, screen out data that meet the training condition as sample data, specifically, based on the content of the training condition described above, may acquire an influence value of each data to be screened on the foreign currency exchange service offered by the corresponding sample financial institution, screen data that the influence value is greater than the influence threshold value as sample data, but is not limited to this screening manner, and the present application does not limit the specific acquisition manner of the influence value.

Step S34, respectively extracting the characteristics of the screened sample data to obtain corresponding sample characteristic vectors;

regarding the implementation process of step S34, reference may be made to, but not limited to, the above description of the corresponding parts of step S12.

And step S35, inputting the obtained multiple sample feature vectors into a preset neural network for learning until constraint conditions are met, and determining a foreign currency exchange business prediction model according to the neural network structure obtained through learning finally.

In some embodiments presented in the present application, the predetermined neural network may include a Back Propagation (BP) neural network based on Genetic Algorithm (GA), but is not limited to the neural network structure, as the case may be. The present application is only exemplified by this example to illustrate the model training process.

Specifically, a BP neural network structure can be determined according to the feature number of a sample feature vector input into a preset neural network, the number of network parameters to be optimized by a genetic algorithm is further determined, then, a three-layer BP neural network can be constructed according to the kolmogorov principle, arbitrary mapping from n dimension to m dimension is completed, a hidden layer is configured, specifically, for the neural network, the feature number included in the obtained sample feature vector can be used as the number of nodes of an input layer, a prediction result of predicting whether a financial institution is suitable for starting foreign currency exchange service is used as the number of nodes of an output layer, and the number of nodes of the hidden layer is determined by adopting a trial and error method, so that the GA-BP neural network structure is determined. In order to improve accuracy of the prediction model and training efficiency, the optimal individuals output by the genetic algorithm can be used as initial weights and thresholds of the BP neural network, and then the sample feature vectors are continuously trained and learned to obtain the required foreign currency exchange business prediction model.

The constraint conditions in the training process of the foreign currency exchange business prediction model can include: the output result of the model obtained by training is convergent or stable, and/or the accuracy of the prediction result of the test data is greater than an accuracy threshold value, and the like, and the neural network model obtained by the training can be considered to meet the constraint condition; otherwise, the network parameters can be continuously adjusted and learned by using the sample data until the constraint condition is met, and a foreign currency exchange business prediction model is determined by a neural network structure obtained by learning finally. The training implementation process of the prediction model is not described in detail in the present application, and is not limited to the model training implementation described in the embodiments of the present application.

The foreign currency exchange business prediction model is obtained through pre-training in the mode and then can be stored in the computer equipment, so that the computer equipment can directly input the data characteristics into the foreign currency exchange business prediction model after obtaining a plurality of data characteristics of each mechanism of the target financial institution, and the target prediction score of the target financial institution suitable for issuing the foreign currency exchange business is obtained.

Step S24, acquiring a score threshold corresponding to the region where the target financial institution is located;

step S25, detecting whether the target prediction score is larger than a score threshold value to obtain a corresponding detection result;

based on the description of the corresponding part of the above embodiment, for financial institutions in different regions, since the economic development level, the regulation and the degree of demand for foreign currency exchange service, etc. are often different in the region, in order to improve the prediction accuracy, the present application may configure corresponding score thresholds for financial institutions in different regions in advance according to the reference factors listed in this embodiment, that is, it is determined that the financial institutions in the region are suitable for setting the predicted score threshold for foreign currency exchange service, but the present application does not limit the specific numerical value of the score threshold.

Therefore, after obtaining the target prediction score of the target financial institution, the computer equipment can compare the target prediction score with the score threshold corresponding to the region where the target financial institution is located, and if the target prediction score is greater than the score threshold, the target financial institution can be considered to be suitable for opening foreign currency exchange business; otherwise, if the target prediction score is smaller than the score threshold, the target financial institution may be considered to be unsuitable for opening the foreign currency exchange service.

In some embodiments proposed in the present application, the detection result of the step S25 may include, but is not limited to, a determination result that the target prediction score is smaller than or greater than the score threshold, and if necessary, may further include a main data feature that results in this determination result, so as to accordingly know a main factor that causes the target financial institution to be unsuitable for issuing foreign currency exchange services, and if necessary, can also assist in improving the operation condition of the target financial institution to make it more suitable for issuing foreign currency exchange services, and the like. Therefore, the present application does not limit the content of the detection result, and the content may be determined according to the circumstances.

And step S26, generating prediction prompt information corresponding to the detection result, and sending the prediction prompt information to a preset terminal associated with the target financial institution.

In combination with the above analysis, the warning prompt information may include, but is not limited to, the content of the detection result listed above, that is, the application may notify the relevant responsible person of the detection result and the prediction result in a message output prompt manner, but the specific output manner and content of the prediction prompt information are not limited, which may be determined according to the circumstances.

The preset terminal associated with the target financial institution can be a function of predicting whether foreign currency exchange business is started or not, and the preset terminal can be pre-bound electronic equipment, such as a mobile phone of a related manager, a business terminal deployed in the target financial institution and the like.

In some embodiments provided by the application, when it is required to predict whether the target financial institution is suitable for issuing a foreign currency exchange service, a foreign currency exchange service prediction request may be sent to the computer device through a service terminal of the target financial institution, and at this time, the foreign currency exchange service prediction request may carry target institution identification information of the target financial institution, so that the computer device may query data to be predicted associated with the target institution identification information by responding to the foreign currency exchange service prediction request, and a specific query implementation process is not described in detail.

Based on this, in a possible implementation manner, in the implementation process that the obtained multiple data characteristics are input into the foreign currency exchange business prediction model to obtain whether the target financial institution is suitable for issuing the prediction result of the foreign currency exchange business, a prediction report matched with the detection result may also be obtained, where the prediction report may include the influence weight of each data to be predicted on the detection result, such as a numerical value less than 1, but the present application does not limit the numerical value of the influence weight of each data to be predicted on the detection result, and may be determined as the case may be.

Under the general condition, the larger the contribution of the data to be predicted to the corresponding detection result obtained by the prediction model is, the larger the influence weight of the data to be predicted is; on the contrary, the smaller the contribution of the data to be predicted to the corresponding detection result obtained by the prediction model is, the smaller the influence weight thereof is, and the influence weight can be determined according to the output result of the prediction model, but the method is not limited to this, and the method for determining the influence weight corresponding to each data to be predicted is not limited in the present application.

In combination with the above analysis, when the detection result is that the target prediction score is less than or equal to the score threshold, the prediction report may further include a service offering condition that the data characteristics of the target financial institution are not satisfied, that is, when the target financial institution is not suitable for offering foreign currency exchange service, what aspect of the target financial institution is specifically that the target financial institution is not suitable for offering foreign currency exchange service, such as a profit problem, a passenger flow rate problem, and the like.

In still other embodiments provided by the present application, the obtained multiple data characteristics are input into the foreign currency exchange business prediction model, and a characteristic prediction score of each data characteristic for the target financial institution suitable for issuing the foreign currency exchange business can also be obtained, and then, the obtained multiple characteristic prediction scores can be subjected to weighting operation according to the respective prediction weights of the multiple data characteristics, so as to obtain a target prediction score of the target financial institution suitable for issuing the foreign currency exchange business. In this case, the output detection result may include the target prediction score and each characteristic prediction score, and may be specifically included in the prediction report and output, so that a user viewing the prediction report can intuitively know which data characteristics are not favorable for the target financial institution to start the foreign currency exchange service through each characteristic prediction score.

It should be noted that, as to how to utilize the foreign currency exchange business prediction model to obtain the implementation process of the above target prediction score, the implementation process is not limited to the above two implementation manners.

To sum up, in the embodiment of the present application, before the target financial institution opens the foreign currency exchange service, the pre-trained foreign currency exchange service prediction model is used to analyze the passenger flow rate of the target financial institution within the first time period, the first service transaction data of the first-level client, and the exchange consultation information waiting prediction data for the foreign currency exchange service, so as to obtain the target prediction score of the target financial institution suitable for opening the foreign currency exchange service, and then, the size relationship between the score threshold value and the region where the target financial institution is located is detected, so as to generate the prediction prompt information corresponding to the obtained detection result, which is sent to the preset terminal associated with the target financial institution, so as to assist the responsible person of the target financial institution to determine whether to open the foreign currency exchange service, so as to avoid the target financial institution directly opening the foreign currency exchange service, and to make it easy for the target financial institution to have very few clients to handle the financial institution, a traffic loss or the like occurs.

Referring to fig. 4, a flow chart of another optional example of the foreign currency exchange business development prediction implementation method provided in the present application is schematically illustrated, where this embodiment may be another optional detailed implementation manner of the foreign currency exchange business development prediction implementation method described in the foregoing embodiment, and as shown in fig. 4, this method may include:

step S41, acquiring data to be predicted of the target financial institution;

step S42, respectively extracting characteristics of the passenger flow, the first service transaction data and the exchange consultation information contained in the data to be predicted to obtain corresponding data characteristics;

for specific implementation processes of step S41 and step S42, reference may be made to, but not limited to, descriptions of corresponding parts in the above embodiments, and details are not repeated in this embodiment.

Step S43, acquiring foreign currency transaction data of the region where the target financial institution is located, and handling the average passenger flow of the foreign currency exchange business within the first time period in the financial institution which opens the foreign currency exchange business in the same level region;

step S44, determining the business profit and loss condition and the business handling client condition for the financial institution to open the foreign currency exchange business based on the foreign currency transaction data and the average passenger flow;

with reference to the description of the corresponding part of the above embodiment, for financial institutions in different regions, the contents of the service initiation conditions for determining whether the financial institutions are suitable for initiating foreign currency exchange services are often different, and in order to ensure that the specified service initiation conditions can be suitable for economic development of the region where the target financial institution is located, especially for development of related services related to foreign currencies. The implementation can combine the foreign currency transaction data of the region where the target financial institution is located, and refer to the financial institution which has opened the foreign currency exchange service under the same condition, apply for handling the average passenger flow rate of the foreign currency exchange service and the like in the first time period, and these information can indicate the requirement condition of the foreign currency of the user in the region where the corresponding financial institution is located, so as to formulate the service starting condition of the target financial institution.

In practical application, for requirements of different regions, certain profit may be required to be suitable for issuing foreign currency exchange service, or profit conditions may not be too much concerned, but passenger flow volume or requirements of a client for handling foreign currency exchange service are mainly concerned, and of course, conditions of other aspects may also be considered. Therefore, the method and the device can determine the business profit and loss conditions for the financial institution to open the foreign currency exchange business by combining the acquired foreign currency transaction data, and the foreign currency exchange business can be properly opened only when the profit value reaches the profit threshold value; the method is characterized in that the method is combined with the average passenger flow to determine the service handling client condition for the financial institution to open the foreign currency exchange service, if the passenger flow applying for handling the foreign currency exchange service reaches a flow threshold value, the foreign currency exchange service may be properly opened, and the like, but the method is not limited to the determination mode of the service profit and loss condition and the service handling client condition.

Step S45, determining the service opening condition aiming at the target financial institution according to the service profit and loss condition and the service handling client condition;

in the embodiment of the present application, the service opening condition for the target financial institution may be directly formed by the determined service profit and loss condition and the service handling client condition, or may be determined by combining other conditions on the basis, which may be determined according to the circumstances, and the present application is not limited thereto.

And step S46, analyzing the obtained multiple data characteristics based on the service starting condition corresponding to the target financial institution, and obtaining a prediction result of whether the target financial institution is suitable for starting foreign currency exchange service.

With reference to, but not limited to, the description of the corresponding parts of the foregoing embodiment, for example, based on the service provision condition corresponding to the target financial institution, the foreign currency exchange service prediction model is obtained through pre-training, and then the obtained multiple data features are input into the foreign currency exchange service prediction model to obtain the prediction result of whether the target financial institution is suitable for providing the foreign currency exchange service, which is not described in detail in this embodiment.

In summary, after the characteristics of the data to be predicted of the financial institutions in different regions are extracted, and the corresponding data characteristics are obtained, the data characteristics of the corresponding financial institutions can be analyzed based on the corresponding service opening conditions established for the different financial institutions, so that whether the financial institutions are suitable for opening the prediction result of the foreign currency exchange service or not can be accurately and reliably determined, the financial institutions which do not open the foreign currency exchange service are assisted to determine whether the foreign currency exchange service is to be opened or not, the situation that the target financial institutions directly open the foreign currency exchange service is avoided, the number of clients for the target financial institutions to handle the financial institutions is very small, the service loss occurs, and the like can be easily caused.

Referring to fig. 5, a schematic structural diagram of an alternative example of an apparatus for implementing a foreign currency exchange business development prediction provided by the present application, which may be applied to a computer device, as shown in fig. 5, may include:

the system comprises a to-be-predicted data acquisition module 11, a to-be-predicted data acquisition module and a to-be-predicted data acquisition module, wherein the to-be-predicted data acquisition module is used for acquiring to-be-predicted data of a target financial institution, and the to-be-predicted data comprises passenger flow within a first duration, first service transaction data of first-level clients and exchange consultation information aiming at foreign currency exchange services;

in a possible implementation manner, the to-be-predicted data obtaining module 11 may include:

the foreign currency exchange business prediction request receiving unit is used for receiving a foreign currency exchange business prediction request sent by a business terminal of a target financial institution, wherein the foreign currency exchange business prediction request carries target institution identification information of the target financial institution;

and the data to be predicted query unit is used for responding to the foreign currency exchange business prediction request and querying the data to be predicted related to the target mechanism identification information.

The characteristic extraction module 12 is configured to perform characteristic extraction on the passenger flow volume, the first service transaction data, and the exchange consultation information respectively to obtain corresponding data characteristics;

and the analysis and prediction module 13 is configured to analyze the obtained multiple data features based on the service opening condition corresponding to the target financial institution, so as to obtain a prediction result of whether the target financial institution is suitable for opening the foreign currency exchange service, where the service opening condition at least includes a service profit and loss condition and a service handling client condition.

In a possible implementation manner, in order to obtain the service launch condition, the apparatus may further include:

the conditional data acquisition module is used for acquiring foreign currency transaction data of the region where the target financial institution is located and handling the average passenger flow of the foreign currency exchange service within the first time length in the financial institution which is used for handling the foreign currency exchange service in the same-level region;

the sub-condition determining module is used for determining a service profit and loss condition and a service handling client condition aiming at the foreign currency exchange service opened by the financial institution based on the foreign currency transaction data and the average passenger flow;

and the service starting condition determining module is used for determining the service starting condition aiming at the target financial institution according to the service profit and loss condition and the service handling client condition.

In some embodiments, the analysis and prediction module 13 may include:

the model prediction unit is used for inputting the obtained data characteristics into a foreign currency exchange business prediction model to obtain a prediction result of whether the target financial institution is suitable for issuing the foreign currency exchange business;

In a possible implementation manner, the model prediction unit may include

A target prediction score obtaining unit, configured to input the obtained multiple data characteristics into a foreign currency exchange business prediction model, so as to obtain a target prediction score suitable for the target financial institution to open the foreign currency exchange business;

optionally, the target prediction score obtaining unit may include:

a characteristic prediction score obtaining subunit, configured to input the obtained multiple data characteristics into a foreign currency exchange service prediction model, and obtain a characteristic prediction score that each data characteristic is suitable for the target financial institution to issue the foreign currency exchange service;

and the weighting operation subunit is used for performing weighting operation on the obtained plurality of characteristic prediction scores according to the respective prediction weights of the plurality of data characteristics to obtain the target prediction score suitable for the foreign currency exchange service of the target financial institution.

The score threshold value acquisition unit is used for acquiring a score threshold value corresponding to the region where the target financial institution is located;

the detection unit is used for detecting whether the target prediction score is larger than the score threshold value or not to obtain a corresponding detection result;

and the prediction prompt information generating unit is used for generating prediction prompt information corresponding to the detection result and sending the prediction prompt information to a preset terminal associated with the target financial institution.

In another possible implementation manner, the model prediction unit may further include:

a prediction report obtaining unit, configured to obtain a prediction report that matches the detection result, where the prediction report includes an influence weight of each piece of data to be predicted on the detection result;

In some embodiments of the present application, in order to implement the training of the foreign currency exchange business prediction model, the foreign currency exchange business development prediction implementation apparatus provided by the present application may further include:

the sample institution identification information acquisition module is used for acquiring sample institution identification information of a sample financial institution which has opened the foreign currency exchange service;

the data to be screened acquiring module is used for acquiring the data to be screened related to the sample mechanism identification information;

the data to be screened comprises sample passenger flow volume, first sample self-attribute information, second sample identity attribute information, foreign currency profit information and foreign currency scheduling cost information which are generated within a first time period, wherein the first sample self-attribute information is identity attribute information of a first sample client applying for handling any business at a sample financial institution within the first time period; the second sample identity attribute information refers to identity attribute information of a second sample client applying for handling the foreign currency exchange service at the sample financial institution within the first time period;

the sample data screening module is used for analyzing each data to be screened and screening sample data meeting training conditions, wherein the training conditions refer to changes of corresponding screened data, and the influence value of the training conditions on foreign currency exchange business issued by the sample financial institution is greater than an influence threshold value;

a sample feature vector obtaining module, configured to perform feature extraction on the screened sample data respectively to obtain corresponding sample feature vectors;

the model training module is used for inputting the obtained multiple sample characteristic vectors into a preset neural network for learning until constraint conditions are met, and determining a foreign currency exchange business prediction model according to a neural network structure obtained by learning finally;

It should be noted that, various modules, units, and the like in the embodiments of the foregoing apparatuses may be stored in the memory as program modules, and the processor executes the program modules stored in the memory to implement corresponding functions, and for the functions implemented by the program modules and their combinations and the achieved technical effects, reference may be made to the description of corresponding parts in the embodiments of the foregoing methods, which is not described in detail in this embodiment.

The present application further provides a readable storage medium, where a program is stored, and when the program is executed by a processor, the method implements each step of the method for implementing foreign currency exchange business development prediction, and a specific implementation process may refer to descriptions of corresponding parts in the foregoing method embodiments, which is not described in detail in this embodiment.

Referring to fig. 6, a schematic diagram of a hardware structure of a computer device suitable for the foreign currency exchange business development prediction implementation method and apparatus provided by the present application is shown, where the computer device may be a business terminal or a server with certain data processing capability. The server may be an independent physical server, a service cluster integrated by a plurality of physical servers, or a cloud server supporting cloud computing capability, and the service device may implement communication with a service terminal or other devices through a wired or wireless network, so as to meet actual data interaction requirements.

The service terminal may be an electronic device deployed in a service processing system of a target financial institution, for a worker to handle a service for a client, or an electronic device capable of implementing internal management of the target financial institution, such as a smart phone, a tablet computer, a Personal Computer (PC), a netbook, a Personal Digital Assistant (PDA), a desktop computer, and the like.

As shown in fig. 6, the computer device proposed by the present application may include: at least one communication interface 21, at least one memory 22, and at least one processor 23, wherein:

the communication interface 21, the memory 22 and the processor 23 may all be connected to a communication bus to realize data interaction therebetween, and the connection relationship of the lines inside the computer device is not described in detail in this application, and may be determined according to the communication requirements of the actual application scenario.

The communication interface 21 may include an interface of a communication module, such as a GSM module, a WIFI module, an interface for implementing data communication of a mobile communication network (such as a 5G network or a 6G network), and the like, so that the computer device implements data interaction with other devices through such a communication interface; certainly, the communication interface 21 may further include interfaces such as a USB interface and a serial/parallel interface, which are used to implement data interaction between internal components of the computer device, for example, various intermediate data generated or required in the execution process of the foreign currency exchange business development prediction implementation method provided by the present application may be determined according to requirements of an actual application scenario, and details of the present application are not described in detail herein.

The memory 22 may store a program formed by a plurality of instructions for implementing the foreign currency exchange service development prediction implementation method provided in the embodiment of the present application, and the processor 23 invokes and loads the program stored in the memory 22, so as to implement the foreign currency exchange service development prediction implementation method provided in the corresponding embodiment of the present application, and the specific implementation process may refer to, but is not limited to, the description of the corresponding embodiment above.

In the present embodiment, the memory 22 may include a high speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device or other volatile solid state storage device. The processor 23 may be a Central Processing Unit (CPU), an application-specific integrated circuit (ASIC), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices.

Optionally, in a case that the computer device is a service terminal, the computer device provided in the embodiment of the present application may further include various input components, various output components, an alarm component, an image acquisition component, and the like, and may be determined according to a service function type and the like supported by the service terminal, which is not described in detail herein.

It should be understood that the structure of the computer device described in the above embodiments of the present application does not constitute a limitation to the computer device in the embodiments of the present application, and in practical applications, the computer device may include more or less components than the structure shown in fig. 6 and described in the above embodiments, and the present application is not specifically described herein.

Finally, it should be noted that, in the present specification, the embodiments are described in a progressive or parallel manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. The device and the computer equipment disclosed by the embodiment correspond to the method disclosed by the embodiment, so that the description is relatively simple, and the relevant points can be referred to the method part for description.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. 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 application. Thus, the present application 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

1. A foreign currency exchange business starting prediction implementation method is characterized by comprising the following steps:

2. The method as claimed in claim 1, wherein the analyzing the obtained plurality of data features based on the service request condition corresponding to the target financial institution to obtain the prediction result of whether the target financial institution is suitable for issuing the foreign currency exchange service comprises:

3. The method of claim 2, wherein said inputting the obtained plurality of said data characteristics into a foreign currency exchange transaction prediction model to obtain a prediction of whether the target financial institution is eligible to initiate the foreign currency exchange transaction comprises:

4. The method of claim 3, wherein said inputting the obtained plurality of said data characteristics into a foreign currency exchange transaction prediction model to obtain a prediction of whether the target financial institution is eligible to initiate the foreign currency exchange transaction further comprises:

5. The method of claim 3, wherein said inputting the obtained plurality of said data characteristics into a foreign currency exchange transaction prediction model to obtain a target prediction score for the target financial institution suitable for the development of the foreign currency exchange transaction comprises:

6. The method according to any one of claims 2 to 5, wherein the training process of the foreign currency exchange business prediction model comprises the following steps:

7. The method according to any one of claims 1 to 5, wherein the obtaining of the service offering condition comprises:

8. The method according to any one of claims 1 to 5, wherein the acquiring data to be predicted of the target financial institution comprises:

9. A foreign currency exchange business development prediction implementation device is characterized by comprising:

10. A computer device, characterized in that the computer device comprises: at least one communication interface, at least one memory, and at least one processor, wherein:

the memory is used for storing a program for realizing the foreign currency exchange business development forecasting realization method according to any one of claims 1 to 8;

the processor is used for loading and executing the program stored in the memory so as to realize the steps of the foreign currency exchange business development prediction realization method according to any one of claims 1 to 8.