CN112767143A - Financing product recommendation method and device based on profit contribution degree - Google Patents
Financing product recommendation method and device based on profit contribution degree Download PDFInfo
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Abstract
The invention provides a financial product recommendation method and device based on profit contribution, which are suitable for the financial field, and the method comprises the following steps: according to the financial product data and the corresponding historical purchase record data, acquiring the support degree of two financial products purchased by the same customer in the record of purchasing the financial products by the customer, the confidence degree of purchasing one financial product and purchasing the other financial product by the customer and the promotion degree of the application of the association rules of the two financial products; calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree; screening the financial products according to the profit contribution of the financial products of the bank; and recommending the filtered financing products according to the support degree, the confidence degree and the recommendation degree, wherein the recommendation efficiency is high.
Description
Technical Field
The invention relates to the field of finance, in particular to a financial product recommendation method and device based on profit contribution.
Background
At present, a customer-based collaborative filtering algorithm is used for recommending personalized financial products, which is a mainstream bank financial product recommendation algorithm, and firstly, a customer figure is perfected by the algorithm, including basic information, financial conditions, risk preference and risk bearing capacity, investment experience, individual investment style and the like of the customer, then, a basic characteristic mean value of a customer group corresponding to each bank financial product is calculated, similarity of the customer group characteristic mean value obtained based on the historical purchase record of the customer and the risk preference analysis is compared, and finally, a plurality of financial products with the highest similarity are selected as recommendations of the customer group.
The existing recommendation algorithm has two disadvantages, one is that much data needed by a client portrait is inaccurate, for example, the financial condition of the client is often lagged, and related data of risk preference, including data for risk assessment such as deposit fund change and credit limit of three parties, are often not filled seriously by the client; and secondly, the benefits of the bank are not considered in combination with interest rate trends, for example, when the expected interest rate is increased, the bank wants to recommend more long-term financing, and when the expected interest rate is decreased, the bank wants to recommend more short-term financing, so that the recommendation effect is not ideal.
Disclosure of Invention
In view of the problems in the prior art, the present invention provides a financial product recommendation method and apparatus, an electronic device, and a computer-readable storage medium based on profit contribution, which can at least partially solve the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a financial product recommendation method based on profit contribution is provided, which includes:
according to the financial product data and the corresponding historical purchase record data, acquiring the support degree of two financial products purchased by the same customer in the record of purchasing the financial products by the customer, the confidence degree of purchasing one financial product and purchasing the other financial product by the customer and the promotion degree of the application of the association rules of the two financial products;
calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree;
screening the financial products according to the profit contribution of the financial products of the bank;
and recommending the filtered financing products according to the support degree, the confidence degree and the recommendation degree.
Further, the screening of the financial products according to the profit contribution degree of the bank financial products comprises the following steps:
calculating the contribution degree of the bank financing product according to the data of the bank financing product in the preset time and the corresponding historical purchasing record data;
and screening the bank financing products with the contribution degree greater than the preset threshold value.
Further, after recommending the filtered financing product according to the support degree, the confidence degree and the recommendation degree, the method further comprises the following steps:
estimating profits according to the currently recommended customer purchase record and financial product data;
and adjusting the profit contribution degree of the bank financing product according to the estimation result.
Further, the obtaining, according to the financial product data and the corresponding historical purchase record data, the support degree of two financial products purchased by the same customer in the record of purchasing the financial products by the customer, the confidence degree of purchasing one financial product and purchasing another financial product by the customer, and the promotion degree of the application of the association rules of the two financial products includes:
preprocessing the financial product data and the corresponding historical purchasing record data to obtain a client purchasing financial product record table;
calculating the support degree of two financial products purchased by the same customer and the confidence degree of the customer who purchases one financial product and the confidence degree of the customer who purchases the other financial product according to the record table of the financial products purchased by the customer;
and calculating the promotion degree of the application of the two financial product association rules according to the client purchasing financial product record table and the confidence degree.
Further, the calculating the support degree of two financial products purchased by the same customer and the confidence degree of the customer who purchases one financial product and another financial product according to the record table of the customer purchasing financial products comprises:
dividing the number of the customers who buy two financial products simultaneously in the customer financial product purchasing record table by the total number of the customers to obtain the support degree;
and dividing the number of the customers who buy two financial products simultaneously in the customer financial product purchasing record table by the number of the customers who buy one financial product to obtain the confidence.
Further, the calculating the promotion degree of the application of the two financial product association rules according to the client purchasing financial product record table and the confidence degree comprises:
and dividing the confidence coefficient by the proportion of the customers who buy another financial product in the record table of the financial products bought by the customers to obtain the promotion degree.
Further, the financial product data comprises attribute parameters of a plurality of financial products;
the preprocessing is carried out on the financial product data and the corresponding historical purchasing record data to obtain a client purchasing financial product record table, and the method comprises the following steps:
combining a plurality of financial products according to the attribute parameters of the financial products to obtain a product list;
and integrating to obtain the record table of the financial products purchased by the customer according to the product list and the historical purchase record data.
In a second aspect, there is provided a financial product recommendation apparatus based on profit contribution, comprising:
the product analysis module is used for acquiring the support degree of two financial products purchased by the same customer, the confidence degree of a customer who purchases one financial product and another financial product and the promotion degree of the application of the association rules of the two financial products in the record of purchasing the financial products by the customer according to the financial product data and the corresponding historical purchase record data;
the recommendation degree sorting module is used for calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree;
the profit contribution screening module is used for screening the financial products according to the profit contribution of the bank financial products;
and the recommending module recommends the filtered financing products according to the support degree, the confidence degree and the recommending degree.
In a third aspect, an electronic device is provided, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the financial product recommendation method based on profit contribution degree.
In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described financial product recommendation method based on profit contribution.
The financial product recommendation method and device based on profit contribution degree provided by the invention are suitable for the financial field, and the method comprises the following steps: according to the financial product data and the corresponding historical purchase record data, acquiring the support degree of two financial products purchased by the same customer in the record of purchasing the financial products by the customer, the confidence degree of purchasing one financial product and purchasing the other financial product by the customer and the promotion degree of the application of the association rules of the two financial products; calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree; screening the financial products according to the profit contribution of the financial products of the bank; recommending the screened financial products according to the support degree, the confidence degree and the recommendation degree, wherein the products are recommended by combining a bank interest rate prediction curve through bank financial product data and historical purchase record data, and then screening the financial products based on profit contribution degree, so that the method is easy in data collection, high in data quality, high in recommendation efficiency and high in speed, and can recommend the products more conforming to bank interests by combining interest rate trend; therefore, the technical scheme can effectively avoid the risk of failure of personalized recommendation caused by inaccurate customer information and personalized characteristics, and can also combine interest rate prediction to select favorable product recommendation for banks.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. In the drawings:
FIG. 1 is a schematic diagram of an architecture between a server S1 and a customer service device B1 according to an embodiment of the present invention;
FIG. 2 is a block diagram of the server S1, customer service device B1, and database server S2 according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a financial product recommendation method in an embodiment of the present invention;
fig. 4 shows the specific steps of step S100 in the embodiment of the present invention;
FIG. 5 illustrates an example of a customer purchasing a financial product record form;
fig. 6 shows the specific steps of step S120 in the embodiment of the present invention;
fig. 7 shows the specific steps of step S110 in the embodiment of the present invention;
FIG. 8 illustrates a product list in an embodiment of the present invention;
fig. 9 shows the specific steps of step S200 in the embodiment of the present invention;
FIG. 10 shows an example of purchases of all banking products within a prescribed time T as demonstrated in an embodiment of the present invention;
FIG. 11 shows a top ten example of a bank financing product portfolio recommendation meeting a given support, confidence level set as presented in an embodiment of the present invention;
FIG. 12 is a diagram showing a relationship diagram-main diagram of a financial product with the total number of times the financial product appears as a top item and a bottom item ranked the top ten in the embodiment of the present invention;
FIG. 13 illustrates a top ten ranked financial product relationship diagram-node diagram presented in an embodiment of the present invention;
FIG. 14 illustrates a top ten ranked financial product relationship-connection line diagram presented in an embodiment of the present invention;
FIG. 15 is a diagram of an intelligent product portfolio recommendation system based on bank financing product sales correlation analysis in accordance with the present invention;
FIG. 16 is a flowchart of the processing steps of the data analysis system 2 of FIG. 15;
FIG. 17 is a block diagram of the data analysis system 2 of FIG. 15;
fig. 18 is a block diagram of the structure of a financial product apparatus in the embodiment of the present invention;
fig. 19 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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 partial embodiments of the present application, but not all 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.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The existing recommendation algorithm has two disadvantages, one is that much data needed by a client portrait is inaccurate, for example, the financial condition of the client is often lagged, and related data of risk preference, including data for risk assessment such as deposit fund change and credit limit of three parties, are often not filled seriously by the client; and secondly, the benefits of the bank are not considered in combination with interest rate trends, for example, when the expected interest rate is increased, the bank wants to recommend more long-term financing, and when the expected interest rate is decreased, the bank wants to recommend more short-term financing, so that the recommendation effect is not ideal.
In order to solve at least part of the technical problems, the invention provides a financial product recommendation method, which combines a bank interest rate prediction curve to recommend products according to bank financial product data and historical purchase record data, so that the data collection is easy, the data quality is high, the products more in line with bank interests can be recommended by combining interest rate trends, the risk of failure of personalized recommendation caused by inaccurate customer information and personalized characteristics can be effectively avoided, and the favorable product recommendation for banks can be selected by combining interest rate prediction.
In view of the above, the present application provides a financial product recommendation device, which may be a server S1, see fig. 1, where the server S1 may be in communication connection with at least one customer service device B1, and the server S1 obtains, according to the financial product data and corresponding historical purchase record data, a support degree for a customer to purchase two financial products from the same customer in a financial product record, a confidence degree for the customer who purchased one financial product to purchase the other financial product, and a promotion degree for application of two financial product association rules; calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree; screening the financial products according to the profit contribution of the financial products of the bank; and recommending the filtered financing products according to the support degree, the confidence degree and the recommendation degree. Then, the server S1 may send the recommendation result to the customer service device B1 online. The customer service device B1 may receive the recommendation online.
Additionally, referring to FIG. 2, the server S1 may also be communicatively coupled to at least one database server S2, the database server S2 being configured to store financial product data and corresponding historical purchase record data. The database server S2 transmits the financial product data and the corresponding historical purchase record data to the server S1 online, and the server S1 may receive the financial product data and the corresponding historical purchase record data online.
It is understood that the customer service device B1 may include a smart phone, a tablet electronic device, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), or the like.
The server and the client device may communicate using any suitable network protocol, including network protocols not yet developed at the filing date of this application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.
FIG. 3 is a flow chart illustrating a financial product recommendation method in an embodiment of the present invention; as shown in fig. 3, the financial product recommendation method may include the following:
step S100: according to the financial product data and the corresponding historical purchase record data, acquiring the support degree of two financial products purchased by the same customer in the record of purchasing the financial products by the customer, the confidence degree of purchasing one financial product and purchasing the other financial product by the customer and the promotion degree of the application of the association rules of the two financial products;
specifically, the financial product data includes multiple financial product names of the bank and corresponding parameters such as product numbers, profits, purchase thresholds, duration, investment varieties and the like, and the historical purchase record data includes customer numbers, customer information, corresponding financial product names and corresponding parameters such as product numbers, purchase time, purchase amount and the like.
Step S200: calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree;
the bank interest rate prediction curve is an interest rate trend obtained by comprehensive analysis of relevant departments of banks according to market change conditions.
Step S300: screening the financial products according to the profit contribution of the financial products of the bank;
step S400: and recommending the filtered financing products according to the support degree, the confidence degree and the recommendation degree.
Specifically, the recommended financial product can display the recommended financial product information on one hand and display the associated financial product information on the other hand, so that sufficient guidance is provided for customer service personnel and the customer service personnel are helped to recommend the customer service personnel to the customer.
It is worth to say that various information of financial products, as data automatically generated by banking business, is easier to be collected by the bank itself and has the highest data quality than various data required by customer portrayal; the trend prediction of the interest rate of the bank has certain accumulation, and products more in line with the bank interests can be recommended according to the expected trend of the interest rate; therefore, based on the sales volume of bank financing products, combination rules among the bank financing products are mined, product recommendation is weighted in combination with interest rate expectation while association among the products is found, the risk that personalized recommendation fails due to inaccurate customer information and personalized features can be effectively avoided, product recommendation beneficial to banks can be selected in combination with interest rate prediction, and intelligent recommendation based on the sales association and expected interest rate weighting of the bank financing products is achieved.
In an alternative embodiment, this step S300 may include the following:
(1) calculating the contribution degree of the bank financing product according to the data of the bank financing product in the preset time and the corresponding historical purchasing record data;
(2) and screening the bank financing products with the contribution degree greater than the preset threshold value.
In an optional embodiment, after step S400, the financial product recommendation method based on profit contribution further includes:
1. estimating profits according to the currently recommended customer purchase record and financial product data;
2. and adjusting the profit contribution degree of the bank financing product according to the estimation result.
Specifically, the profit contribution degree of the bank financing product in a period of time is used, and the financing product with low contribution degree is removed; the method for calculating the profit contribution of the bank financing product in a period of time comprises the following steps:
suppose that the period of selling financial products X1 and X2 … … Xn in a period of time is TX1、TX2……TXnTime limit TX1、TX2……TXnThe prediction curve of the interest rate of the inner bank is R (T)X1)、R(TX2)……R(TXn) The time-varying curve of the profits of financial products X1 and X2 … … Xn is G (T)X1)、G(TX2)……G(TXn) The sale money of the bank financing product is M1 and M2 … … Mn; the profit contribution c (Xk) of the daily financial product Xk is calculated as follows:
if the profit-change curve of the financial product X along with time is G (T)X) If not, using the predicted curve G' (T) of the change of profit with time of the financial product XX) And (4) replacing.
The lower the profit contribution degree of the financial product is, the less the bank profits through the financial product;
setting profit contribution threshold CthFinancial products below the threshold are removed.
In addition, after recommendation, the profit estimation of the financial product through the bank is used for measuring the recommendation effect:
suppose that the deadline of a financial product X is TXTime limit TXThe prediction curve of the intrinsic rate is R (T)X) The time-dependent change curve of the profit of the financial product X is G (T)X) If the sales amount of the bank financing product is M, the profit estimate P (X) of the financing product X is calculated as follows:
if the profit-change curve of the financial product X along with time is G (T)X) If not, using the predicted curve G' (T) of the change of profit with time of the financial product XX) And (4) replacing.
Fig. 4 shows the specific steps of step S100 in the embodiment of the present invention; as shown in fig. 4, this step S100 may include the following:
step S110: preprocessing the financial product data and the corresponding historical purchasing record data to obtain a client purchasing financial product record table;
within a prescribed time T, the purchase records of the customers are integrated into a list L, each record of the list L contains a customer number and a financial product, as shown in fig. 5; if the a-customer purchases 5 financial products within the time T, the list has 5 records about the a-customer.
Step S120: calculating the support degree of two financial products purchased by the same customer and the confidence degree of a user who purchases one financial product and another financial product according to the record table of the financial products purchased by the customer;
in an alternative embodiment, referring to fig. 6, this step S120 may include the following:
step S121: and dividing the number of the customers who buy the two financial products simultaneously in the customer financial product purchasing record table by the total number of the customers to obtain the support degree.
Specifically, the calculation formula of the support degree of the financial product a and the financial product B purchased by the same customer is as follows:
Rsupportthe number of customers who purchased the financing product A, B at the same time/total number of customers p (ab)/p (all).
Step S122: and dividing the number of the customers who buy two financial products simultaneously in the customer financial product purchasing record table by the number of the customers who buy one financial product to obtain the confidence.
Specifically, the confidence coefficient is the ratio of purchasing the financial product B among the persons who have purchased the financial product a, and the calculation formula is as follows:
Rconfidencthe number of customers who purchased the financial product A, B at the same time/the total number of customers who purchased the financial product a p (ab)/p (a).
Step S130: and calculating the promotion degree of the application of the two financial product association rules according to the client purchasing financial product record table and the confidence degree.
Specifically, the confidence is divided by the proportion of the customers who purchase another financial product in the record table of the financial products purchased by the customers to obtain the promotion degree.
Wherein, the promotion degree of the application of the association rules of the financial product A and the financial product B is calculated, namely the proportion of the application of the association rules and the non-application of the generated results, and the calculation formula is as follows:
Rliftthe ratio of purchasing the financial product B/the ratio of customers purchasing the financial product B among the persons who purchased the financial product a is P (B | a)/P (B | ALL).
In an alternative embodiment, referring to fig. 7, this step S110 may include the following:
step S111: combining a plurality of financial products according to the attribute parameters of the financial products to obtain a product list;
specifically, within a specified time T, the same or very close financing product varieties such as income, purchase threshold, duration, investment variety, etc. are merged to obtain a product list. According to the fact that the financial products of the bank are sold for a certain period, the financial products are basically not sold after being collected, so that a plurality of products are different in name or period number and actually belong to a financial product; the combination of the products is beneficial to improving the stability of data and algorithms; the product list is obtained after merging, as shown in fig. 8.
Step S112: and integrating to obtain the record table of the financial products purchased by the customer according to the product list and the historical purchase record data.
Specifically, during integration, products purchased by a customer in the historical purchase record data are integrated according to the product list, and then a customer number and a financial product purchased correspondingly are stored in a customer purchase product record table as a record.
In an alternative embodiment, referring to fig. 9, this step S200 may include the following:
step S210: calculating the recommendation weight of each group of financial products according to the income curve and the bank interest rate prediction curve of the financial products;
particularly, forecasting of interest rate by combining banks; suppose that the term of financial product X is TXTime limit TXThe prediction curve of the interest rate of the inner bank is R (T)X) The time-dependent change curve of the profit of the financial product X is G (T)X) Then, the recommendation weight w (X) of the financial product X is calculated as follows:
if the profit-change curve of the financial product X along with time is G (T)X) If not, using a pre-estimated curve of the profit of the financial product X along with the time changeG'(TX) And (4) replacing.
Step S220: and calculating the recommendation degree of each group of financial products according to the recommendation degree weight and the promotion degree.
In particular, according to the degree of lift RliftCalculating recommendation degree R according to recommendation degree weight W (X)recommenThe calculation formula is as follows:
Rrecommend＝Rlift×W(X)。
in an alternative embodiment, the step S400 may include:
step I: and eliminating the financial product combinations with the support degrees lower than the preset minimum support degree threshold value and the confidence degrees lower than the minimum confidence degree threshold value, and sequencing and displaying the rest financial product combinations according to the recommendation degrees.
Wherein, the financial products before being removed are the financial products after being screened according to the profit contribution degree of the financial products.
Step II: counting the occurrence frequency of each financing product in the sorted financing product combination;
step III: sorting the financial products according to the occurrence times of the financial products;
step IV: and displaying the related data of the financial products with the preset number in the financial products sequenced according to the occurrence times through a relational graph.
For example, the support degree R is obtained according to the above calculationsupportConfidence RconfidencRecommendation degree RrecommenThe method comprises the steps of presetting a minimum support threshold, such as minS 0.01, presetting a minimum confidence threshold, such as minC 0.05, removing financial product combinations which are lower than the minimum support threshold and the minimum confidence threshold, and sorting the financial product combinations which meet the conditions according to the recommendation degree, wherein the financial product combinations comprise a front item1 (namely the financial product which is arranged at the front in the combinations) and a back item2 (namely the financial product which is arranged at the back in the combinations). And counting the total number of times of each financing product as item1 and item2, and sequencing the financing products according to the total number of times from large to small to obtain the financing products ranked in the top ten.
After statistical processing, the analysis result can be displayed to the customer service staff, for example, the purchase condition of all bank financial products within a specified time T is displayed, as shown in FIG. 10.
Certainly, the bank financing product combinations meeting the given support degree and confidence degree within a period of specified time T are sorted from large to small according to the recommendation degree, and the top ten are displayed. The displayed content comprises a front item represented by item1, a back item represented by item2, the number of people who purchase item2 on the premise of purchasing item1, the support degree, the confidence degree and the recommendation degree, as shown in fig. 11.
It is worth mentioning that the top ten bank financing products can be displayed through a relationship diagram, as shown in fig. 12; in the relational graph, each node represents a financial product of a bank, and the size of the node is in direct proportion to the sum of times of taking the corresponding financial product as a front item and a back item; the edge between any two nodes represents that the two financial products have an incidence relation, namely the sale of one financial product can affect the sale of the other financial product.
In addition, in the relational graph, a mouse is moved to a node to be checked, and the name and sales volume of the financial product represented by the node are highlighted; highlighting all other financial products associated with the financial product at the same time, as shown in FIG. 13; the mouse moves to the edge to be viewed, highlights the two nodes connected by the edge, and indicates that the number of purchased financial products XXX in the customer purchasing the financial products XXX is n by using the floating characters XXX > XXX: n, as shown in FIG. 14.
When a customer purchases a certain front financing product, the corresponding back financing product can be searched as recommendation according to the content shown in FIG. 10; several financial products with more association rules can be selected as a combination to be recommended to the client at the same time.
FIG. 15 is a diagram of an intelligent product portfolio recommendation system based on bank financing product sales correlation analysis in accordance with the present invention; the intelligent product combination recommendation system is used for realizing the financial product recommendation method provided by the embodiment of the invention, and comprises the following components: the system comprises a data collection system 1, a data analysis system 2 and a data display system 3, wherein the data collection system 1 is connected with the data analysis system 2, and the data analysis system 2 is connected with the data display system 3. The data collection system 1 is deployed in branches and websites of a bank, the data analysis system 2 is deployed in a bank science and technology department, the data display system 3 is deployed in a bank science and technology department at the background, and all levels of mechanisms (a head office, a first-level branch, a second-level branch, branches and websites) of a front-end page bank of the data display system 3 can be accessed.
Specifically, the data collection system 1 is deployed at branches and websites of a bank and is responsible for collecting records of all customers who purchase any one or more financial products. The data analysis system 2 is deployed in the bank science and technology department and is responsible for preprocessing, calculating and analyzing the purchasing record data of the bank financial products collected by the data collection system 1 to obtain results.
Fig. 17 is a block diagram of the data analysis system 2 in the system of the present invention. The data analysis system 2 comprises a data preprocessing module 21, a data calculating module 22 and a data counting module 23, wherein the data preprocessing module 21 is connected with the data calculating module 22, and the data calculating module 22 is connected with the data counting module 23.
Specifically, the method comprises the following steps:
the processing flow of the data preprocessing module 21, see fig. 16, is as follows:
step S201: merging the same or similar financing products such as income, purchase threshold, duration, investment variety and the like in a specified time to obtain a product list;
specifically, within a specified time T, the same or very close financing product varieties such as income, purchase threshold, duration, investment variety, etc. are merged to obtain a product list. According to the fact that the financial products of the bank are sold for a certain period, the financial products are basically not sold after being collected, so that a plurality of products are different in name or period number and actually belong to a financial product; the combination of the products is beneficial to improving the stability of data and algorithms; and combining to obtain a product list.
Step S202: integrating the purchase records of the customers into a list L, wherein each record of the list L comprises a customer number and a financial product;
specifically, within a specified time T, the purchase records of customers are integrated into a list L, and each record of the list L comprises a customer number and a financial product; if the a-customer purchases 5 financial products within the time T, the list has 5 records about the a-customer.
The processing flow of the data calculation module 22 is as follows:
step S203: calculating the support degree;
specifically, according to the list L obtained in the step S202, the support degree of purchasing the financial products in a pairwise combination is calculated; the calculation formula of the support degree of the financial product A and the financial product B purchased by the same customer is as follows:
Rsupportnumber of customers who purchased the financing product A, B at the same time/total number of customers p (ab)/p (all)
Step S204: calculating a confidence coefficient;
specifically, according to the list L obtained in step S202, the confidence of purchasing the financial product a and purchasing the financial product B is calculated, that is, the proportion of purchasing the financial product B among the persons who have purchased the financial product a, and the calculation formula is as follows:
Rconfidencnumber of customers who purchased financing product A, B at the same time/total number of customers who purchased financing product A P (AB)/P (A)
Step S205: calculating the lifting degree;
according to the list L obtained in the step S202 and the calculation results obtained in the steps S203 and S204, the application promotion degree of the association rules of the financial product A and the financial product B is calculated, namely the proportion of the generated results of the application of the association rules and the non-application of the association rules, and the calculation formula is as follows:
Rliftthe ratio of purchasing the financial product B/the ratio of purchasing the customer of the financial product B among the persons who purchased the financial product a is P (B | a)/P (B | ALL)
Step S206: calculating recommendation weight by combining the prediction of the interest rate by the bank;
particularly, forecasting of interest rate by combining banks; suppose that the term of financial product X is TXTime limit TXIntrabank interest rate predictionThe curve is R (T)X) The time-dependent change curve of the profit of the financial product X is G (T)X) Then, the recommendation weight w (X) of the financial product X is calculated as follows:
if the profit-change curve of the financial product X along with time is G (T)X) If not, using the predicted curve G' (T) of the change of profit with time of the financial product XX) And (4) replacing.
Step S207: calculating the recommendation degree according to the promotion degree and the recommendation degree weight;
specifically, the lift degree R according to step S205liftAnd step S206, calculating recommendation degree R according to the recommendation degree weight W (X)recommenThe calculation formula is as follows:
Rrecommend＝Rlift×W(X)
the processing flow of the data statistics module 23 is as follows:
step S208: eliminating financial products which do not meet the given support degree and confidence degree, and sorting the financial product combinations which meet the conditions according to the recommendation degree;
wherein, the financial products before being removed are the financial products after being screened according to the profit contribution degree of the financial products.
Specifically, the support degree R calculated in step S203, step S204, and step S207supportConfidence RconfidencRecommendation degree RrecommenSetting a minimum support threshold minS to be 0.01 and a minimum confidence threshold minC to be 0.05, removing the financial product combinations lower than the set support and confidence, and sorting the financial product combinations meeting the conditions according to the recommendation, wherein the financial product combinations comprise a front item1 and a back item 2.
Step S209: and counting the sum of times of occurrence of each financing product as a front item and a back item, and sequencing the financing products according to the sum of times from big to small to obtain the financing products ranked in the top ten.
Specifically, according to the calculation result in step S208, counting the total number of times that each financing product appears as item1 and item2, and sorting the financing products according to the total number of times from large to small to obtain the top ten financing products.
The data display system 3 is deployed in the back stage of the bank scientific and technical department, and can be accessed by all levels of mechanisms (head office, first-level branch, second-level branch, branch and network point) of the front-end page bank; the data display system 3 is responsible for displaying the processing results of the data analysis system 2.
For example, the purchasing conditions of all the bank financial products within a specified time T are displayed, or the bank financial product combinations meeting the given support degree and confidence degree within the specified time T are sorted from large to small according to the recommendation degree, and the top ten are displayed. The displayed content comprises a front item represented by item1, a back item represented by item2, the number of people who purchase item2 on the premise of purchasing item1, support degree, confidence degree and recommendation degree.
In addition, the data display system 3 can display the bank financing products ranked in the top ten through a relationship diagram according to the calculation result in the step S207; in the relational graph, each node represents a financial product of a bank, and the size of the node is in direct proportion to the sum of times of taking the corresponding financial product as a front item and a back item; the edge between any two nodes represents that the two financial products have an incidence relation, namely the sale of one financial product can affect the sale of the other financial product.
In the relational graph, a mouse is moved to a node to be checked, and the name and sales volume of the financial product represented by the node are highlighted; and meanwhile, highlighting all other financial products related to the financial product, moving a mouse to an edge to be viewed, highlighting two nodes connected with the edge, and using the floating characters XXX > XXX: n to indicate that the quantity of purchased financial products XXX in a customer purchasing the financial product XXX is n.
In summary, the financial product recommendation method provided by the embodiment of the invention directly analyzes the sales data of the bank financial products, analyzes the association generated when the customer purchases the bank financial products, and excavates the combination rules among the products; then combining the interest rate prediction curve with the product income to calculate the recommendation degree; the method has the advantages of accurate data source, convenient data acquisition, rapid analysis, intuitive result display and multiple analysis result application modes, can effectively avoid the risk of failure of personalized recommendation caused by inaccurate customer information and personalized characteristics, and provides powerful reference for a bank customer manager to recommend financial products by combining interest rate prediction selection.
Based on the same inventive concept, the embodiment of the present application further provides a financial product recommendation device, which can be used to implement the methods described in the above embodiments, as described in the following embodiments. Because the principle of solving the problems of the financial product recommending device is similar to that of the method, the implementation of the financial product recommending device can refer to the implementation of the method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, hardware or a combination of software and hardware is also possible to conceive and implement.
Fig. 18 is a block diagram showing the configuration of a financial product recommending apparatus in the embodiment of the present invention. As shown in fig. 18, the financial product recommendation apparatus specifically includes: a product analysis module 10, a recommendation ranking module 20, a profit contribution filtering module 30, and a recommendation module 40.
The product analysis module 10 acquires the support degree of two financial products purchased by the same customer in the record of purchasing the financial products by the customer, the confidence degree of a user who purchases one financial product and purchases the other financial product and the promotion degree of the application of the association rules of the two financial products according to the financial product data and the corresponding historical purchase record data;
the recommendation degree sorting module 20 calculates the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree;
the profit contribution screening module 30 screens financial products according to the profit contribution of the bank financial products;
and the recommending module 40 recommends the filtered financing products according to the support degree, the confidence degree and the recommendation degree.
Compared with various data required by customer portrayal, various information of financial products is used as data automatically generated by bank processing business, is easier to be collected by a bank, and has the highest data quality; the trend prediction of the interest rate of the bank has certain accumulation, and products more in line with the bank interests can be recommended according to the expected trend of the interest rate; therefore, based on the sales volume of bank financing products, combination rules among the bank financing products are mined, product recommendation is weighted in combination with interest rate expectation while association among the products is found, the risk that personalized recommendation fails due to inaccurate customer information and personalized features can be effectively avoided, product recommendation beneficial to banks can be selected in combination with interest rate prediction, and intelligent recommendation based on the sales association and expected interest rate weighting of the bank financing products is achieved.
The apparatuses, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or implemented by a product with certain functions. A typical implementation device is an electronic device, which may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
In a typical example, the electronic device specifically includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the steps of the financial product recommendation method described above.
Referring now to FIG. 19, shown is a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present application.
As shown in fig. 19, the electronic apparatus 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate works and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM)) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted as necessary on the storage section 608.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, an embodiment of the present invention includes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the financial product recommendation method described above.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A financial product recommendation method based on profit contribution, comprising:
according to the financial product data and the corresponding historical purchase record data, acquiring the support degree of two financial products purchased by the same customer in the record of purchasing the financial products by the customer, the confidence degree of purchasing one financial product and purchasing the other financial product by the customer and the promotion degree of the application of the association rules of the two financial products;
calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree;
screening the financial products according to the profit contribution of the financial products of the bank;
and recommending the filtered financing products according to the support degree, the confidence degree and the recommendation degree.
2. The financial product recommendation method based on profit contribution according to claim 1 wherein the screening financial products according to the bank financial product profit contribution comprises:
calculating the contribution degree of the bank financing product according to the data of the bank financing product in the preset time and the corresponding historical purchasing record data;
and screening the bank financing products with the contribution degree greater than the preset threshold value.
3. The financial product recommendation method based on profit contribution according to claim 2, further comprising, after recommending the filtered financial products according to the support degree, the confidence degree and the recommendation degree:
estimating profits according to the currently recommended customer purchase record and financial product data;
and adjusting the profit contribution degree of the bank financing product according to the estimation result.
4. The financial product recommendation method based on profit contribution according to claim 1, wherein the obtaining of the support of two financial products purchased by the same customer in the customer's financial product purchase record, the confidence of the customer purchasing one financial product purchasing the other financial product and the promotion of the application of the two financial product association rules according to the financial product data and the corresponding historical purchase record data comprises:
preprocessing the financial product data and the corresponding historical purchasing record data to obtain a client purchasing financial product record table;
calculating the support degree of two financial products purchased by the same customer and the confidence degree of the customer who purchases one financial product and the confidence degree of the customer who purchases the other financial product according to the record table of the financial products purchased by the customer;
and calculating the promotion degree of the application of the two financial product association rules according to the client purchasing financial product record table and the confidence degree.
5. The financial product recommendation method based on profit contribution according to claim 4 wherein calculating confidence levels of two financial products purchased by the same customer, a customer who purchased one financial product purchasing another financial product from said customer purchasing financial product record table comprises:
dividing the number of the customers who buy two financial products simultaneously in the customer financial product purchasing record table by the total number of the customers to obtain the support degree;
and dividing the number of the customers who buy two financial products simultaneously in the customer financial product purchasing record table by the number of the customers who buy one financial product to obtain the confidence.
6. The financial product recommendation method based on profit contribution according to claim 5 wherein said calculating the promotion of application of two financial product association rules based on said customer purchase financial product record table and said confidence level comprises:
and dividing the confidence coefficient by the proportion of the customers who buy another financial product in the record table of the financial products bought by the customers to obtain the promotion degree.
7. The financial product recommendation method based on profit contribution according to claim 4 wherein the financial product data includes attribute parameters of a plurality of financial products;
the preprocessing is carried out on the financial product data and the corresponding historical purchasing record data to obtain a client purchasing financial product record table, and the method comprises the following steps:
combining a plurality of financial products according to the attribute parameters of the financial products to obtain a product list;
and integrating to obtain the record table of the financial products purchased by the customer according to the product list and the historical purchase record data.
8. A financial product recommendation apparatus based on profit contribution, comprising:
the product analysis module is used for acquiring the support degree of two financial products purchased by the same customer, the confidence degree of a customer who purchases one financial product and another financial product and the promotion degree of the application of the association rules of the two financial products in the record of purchasing the financial products by the customer according to the financial product data and the corresponding historical purchase record data;
the recommendation degree sorting module is used for calculating the recommendation degree of each group of financial products according to the income curve of the financial products, the bank interest rate prediction curve and the promotion degree;
the profit contribution screening module is used for screening the financial products according to the profit contribution of the bank financial products;
and the recommending module recommends the filtered financing products according to the support degree, the confidence degree and the recommending degree.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the profit contribution based method of recommending financial products according to any one of claims 1 through 7.
10. A computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the financial product recommendation method based on profit contribution according to any one of claims 1 to 7.
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