CN111768283A - Financial big data analysis method of improved collaborative filtering algorithm model - Google Patents
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Abstract
The invention provides a financial big data analysis method which is realized based on an improved collaborative filtering algorithm. The method comprises the steps of concurrently receiving financial data streams through multiple data channels, performing attribute analysis on the financial data streams, dividing the financial data streams into at least a first data set and a second data set, respectively sending the first data set and the second data set to a first data chain and a second data chain, performing collaborative matching analysis on the financial data streams by using the first data chain and the second data chain, and the like. The first data link or the second data link comprises at least one data node, each data node corresponds to payer information or payee information, the payer information comprises a first identity ID used for uniquely identifying a payer, and the payee information comprises a second identity ID used for uniquely identifying a payee. The method provided by the invention can be combined with a big data technology and a collaborative filtering technology to quickly identify abnormal financial payment data.
Description
Technical Field
The invention belongs to the technical field of big data processing, and particularly relates to a financial big data analysis method of an improved collaborative filtering algorithm model.
Background
Big data (big data) is originally an IT industry term, refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth-rate and diversified information asset which needs a new processing mode to have stronger decision-making power, insight discovery power and flow optimization capability.
With the evolution of big data technology, the concept of industry big data, such as medical big data, engineering big data, financial big data, etc., is also presented in specific industries. Financial big data is big data generated on the basis of financial activities, the most important of which are various payment events and financial data for finding an abnormality. Big data processing technique and big data management jointly established big data era, not only revolutionized traditional financial management's theory, also changed the location of financial management function in the enterprise, made financial management and control route and mode more diversified, this means financial worker's professional ability structure remolding and professional development bottleneck will have very big breakthrough.
The basis for big data is that the data is accurate, especially for financial big data, if the data is not updated timely, or is a false error, and there is no quality at all.
In the prior art, the chinese patent application with application number CN201810382074.4 has been retrieved to provide a financial analysis management system and method based on big data, the system includes: the financial statement acquisition module is used for acquiring original forms of various financial statements of a target enterprise; the financial item extraction module is used for extracting financial items of a preset type and corresponding financial amounts from the original forms of the financial statements; the financial vector generating module is used for generating a financial vector; the financial vector recognition module is used for matching the financial vector with a pre-trained financial vector recognition model; and the financial type output module is used for outputting the financial type corresponding to the target enterprise after the type of the financial vector is determined. The financial analysis management system based on big data can comprehensively extract financial indexes in financial statements and perform comprehensive analysis on the basis of machine learning, and the regularity characteristics of an analysis object in the aspect of financial information are fully excavated, so that the analysis result is more comprehensive, and meanwhile, the labor cost is saved;
the Chinese patent application with the application number of CN201810553262.9 provides an industry-oriented application-oriented big data intelligent analysis service system, which comprises a cluster management subsystem, a data acquisition subsystem, an AI analysis modeling subsystem, an AI service configuration tool, an analysis display tool and a data service subsystem. The industrial application-oriented big data intelligent analysis service system can be combined with an industrial concrete solution, quickly constructs a big data intelligent analysis application system oriented to different industrial applications, can effectively improve the industrial big data analysis processing capacity, and can be used as a basic platform for big data intelligent analysis to quickly develop various industrial big data analysis application products.
However, the inventor finds that the existing schemes only focus on data mining per se, and do not relate to the accuracy of the data per se or the identification of abnormal data.
Disclosure of Invention
In order to solve the technical problem, the invention provides a financial big data analysis method which is realized based on an improved collaborative filtering algorithm. The method comprises the steps of concurrently receiving financial data streams through multiple data channels, performing attribute analysis on the financial data streams, dividing the financial data streams into at least a first data set and a second data set, respectively sending the first data set and the second data set to a first data chain and a second data chain, performing collaborative matching analysis on the financial data streams by using the first data chain and the second data chain, and the like. The first data link or the second data link comprises at least one data node, each data node corresponds to payer information or payee information, the payer information comprises a first identity ID used for uniquely identifying a payer, and the payee information comprises a second identity ID used for uniquely identifying a payee. The method provided by the invention can be combined with a big data technology and a collaborative filtering technology to quickly identify abnormal financial payment data.
Specifically, in a first aspect of the present invention, there is provided a financial big data analysis method, which is implemented based on an improved collaborative filtering algorithm, and includes the following steps:
step S100: concurrently receiving a financial data stream over multiple data channels, the financial data stream being sent in payment events;
step S200: performing attribute analysis on the financial data stream, and dividing the financial data stream into at least a first data set and a second data set;
step S300: sending the first data set and the second data set to a first data chain and a second data chain respectively;
step S400: updating the first data chain and the second data chain based on an improved collaborative filtering algorithm;
step S500: and performing collaborative matching analysis on the financial data stream by using the first data chain and the second data chain.
In the invention, the payment event comprises information of a payer and information of a payee; the payer information comprises a payer ID number, a payment mode, a payment medium and payment request time, and the payee information comprises a payee ID number, a collection mode, a collection medium and collection response time;
the first data set comprises information of a payer, hardware information of a payment terminal, a payment mode and payment request time;
the second data set comprises payee information, hardware information of a payee terminal, a payment result and payment response time;
the first data link is established based on a first data set and the second data link is established based on a second data set.
The first data link or the second data link comprises at least one data node, each data node corresponds to payer information or payee information, the payer information comprises a first identity ID used for uniquely identifying a payer, and the payee information comprises a second identity ID used for uniquely identifying a payee.
The improved collaborative filtering algorithm comprises a time sequence updating step, a trust degree calculating step and a prediction score optimizing step;
the time sequence updating step is used for calculating the similarity between the existing data nodes in the first data chain or the second data chain and the first data stream or the second data stream based on the payment request time or the payment response time corresponding to the existing data nodes in the first data chain or the second data chain;
the trust degree calculation step calculates the trust degree between the sending network of the first data stream or the second data stream and the data connection network of the first data chain or the second data chain;
and the prediction score optimization step is used for calculating the prediction scores of the existing data nodes in the first data chain or the second data chain and the first data flow or the second data flow based on the similarity and the trust degree, and taking the data nodes with the prediction scores exceeding a preset value as the nodes recommended to be added.
If the data node corresponding to the second identity ID does not exist in the second data chain, determining whether a data node recommended to be added exists in the second data chain based on an improved collaborative filtering algorithm, specifically including:
and if the second data chain is determined to have no data node recommended to be added, marking the second data set as an abnormal data set.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is an overall flow chart of a financial big data analysis method according to an embodiment of the present invention
FIG. 2 is a flow chart of specific analysis data of the big financial data analysis method shown in FIG. 1
FIG. 3 is a first embodiment of the big financial data analysis method shown in FIG. 1
FIG. 4 is a second embodiment of the big financial data analysis method of FIG. 1
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Fig. 1 is an overall flowchart of a financial big data analysis method according to an embodiment of the present invention.
In fig. 1, the method comprises steps S100 to S500, and each step is implemented as follows:
step S100: concurrently receiving a financial data stream over multiple data channels, the financial data stream being sent in payment events;
step S200: performing attribute analysis on the financial data stream, and dividing the financial data stream into at least a first data set and a second data set;
step S300: sending the first data set and the second data set to a first data chain and a second data chain respectively;
step S400: updating the first data chain and the second data chain based on an improved collaborative filtering algorithm;
step S500: and performing collaborative matching analysis on the financial data stream by using the first data chain and the second data chain.
In the embodiment of fig. 1, the one-time payment event includes payer information and payee information; the payer information comprises a payer ID number, a payment mode, a payment medium and payment request time, and the payee information comprises a payee ID number, a collection mode, a collection medium and collection response time.
As an example, the payer ID number may be a code that can uniquely identify the payer, such as a payment card number, a payer identification number, a payer mobile phone number, and the like; payment methods/collection methods include various payment methods such as card payment, flash payment, code scanning payment, biological payment (such as fingerprint payment, code scanning payment, iris payment and the like), Bluetooth payment, cardless payment, NFC payment and the like;
the payment medium comprises a bank card, a wearable device and a portable mobile payment device;
the collection medium may include various collection devices, such as POS machines, code scanners, etc.;
the hardware information of the payment terminal comprises fixed and unchangeable hardware parameters such as the model, IMEI code and CPU model of the mobile payment equipment; the hardware information of the cash register terminal is similar to the hardware information of the cash register terminal.
The payment mode or the collection mode comprises one or the combination of card swiping payment, flash payment, code scanning payment, biological payment, Bluetooth payment, card-free payment and NFC payment.
In this embodiment, the first data chain is established based on a first data set, and the second data chain is established based on a second data set.
On the basis of fig. 1, fig. 2 is combined. Performing attribute analysis on the financial data stream, and dividing the financial data stream into at least a first data set and a second data set, namely grouping and dividing a payment event (also called a payment event);
more specifically, the first data link or the second data link includes at least one data node, each data node corresponds to payer information or payee information, the payer information includes a first identity ID for uniquely identifying a payer, and the payee information includes a second identity ID for uniquely identifying a payee.
The first data set comprises information of a payer, hardware information of a payment terminal, a payment mode and payment request time;
the second data set includes payee information, hardware information of the payee terminal, payment results, and payment response time.
Reference is next made to fig. 3-4.
In step S400, updating the first data chain based on an improved collaborative filtering algorithm specifically includes:
receiving a join request of the first data set by a current first data link, wherein the join request comprises payer information;
generating a first identity ID of the payer according to the payer information;
judging whether a data node corresponding to the first identity ID exists in the first data chain or not;
if so, adding the first data set to the data node;
and if not, giving at least one data node recommended to join from the first data chain based on an improved collaborative filtering algorithm.
In step S400, updating the second data chain based on an improved collaborative filtering algorithm specifically includes:
receiving a join request of the second data set by a current second data chain, wherein the join request comprises payee information;
generating a second identity ID of the payee according to the payee information;
judging whether a data node corresponding to the second identity ID exists in the second data chain or not;
if so, adding the second data set to the data node;
and if not, determining whether the data node recommended to be added exists from the second data chain based on an improved collaborative filtering algorithm.
If the data node corresponding to the second identity ID does not exist in the second data chain, determining whether a data node recommended to be added exists in the second data chain based on an improved collaborative filtering algorithm, specifically including:
and if the second data chain is determined to have no data node recommended to be added, marking the second data set as an abnormal data set.
The step S500 of performing a collaborative matching analysis on the financial data stream by using the first data chain and the second data chain specifically includes:
and matching the payer information and the payee information of all the second data sets marked as abnormal data sets and the corresponding first data sets to obtain a matching result.
Next, the improved collaborative filtering algorithm used in various embodiments of the present invention will be described with emphasis.
In the invention, the improved collaborative filtering algorithm introduces the ideas of time sequence updating, trust degree, prediction score optimization and structuralization on the basis of the traditional collaborative filtering algorithm.
The traditional collaborative filtering algorithm is the collaborative filtering technology used in the mail system Tapestry in the first 90 s of the 20 th century, and is mainly used for personalized recommendation.
On the basis, various documents deeply perfect the recommendation technology of collaborative filtering to improve the accuracy, and the related technical documents comprise:
L.Xiang，Q.Yuan，S.Zhao，et al.Temporal recommendation on graphs Vialong-and short-term preference fusion[C].ACM international conference onKnowledge discovery and data mining，Washington，2010，723-731.
Y.Ding，X.Li.Time weight collaborative filtering[C].ACM internationalconference on Information and knowledge management，Bremen，2005，485-492.
Y.Koren.Collaborative filtering with temporal dynamics[J].Communications of the ACM，2010。
however, the prior art has not retrieved a document that specifically applies collaborative filtering techniques to financial big data analysis, which is the first contribution of the present invention.
Specifically, in the invention, the improved collaborative filtering algorithm comprises a time sequence updating step, a trust degree calculating step and a prediction score optimizing step;
the time sequence updating step is used for calculating the similarity between the existing data nodes in the first data chain or the second data chain and the first data stream or the second data stream based on the payment request time or the payment response time corresponding to the existing data nodes in the first data chain or the second data chain;
the trust degree calculation step calculates the trust degree between the sending network of the first data stream or the second data stream and the data connection network of the first data chain or the second data chain;
and the prediction score optimization step is used for calculating the prediction scores of the existing data nodes in the first data chain or the second data chain and the first data flow or the second data flow based on the similarity and the trust degree, and taking the data nodes with the prediction scores exceeding a preset value as the nodes recommended to be added.
It is worth pointing out that in the above model, the predetermined value is implemented to be set according to the attribute of the financial big data, with sufficient accuracy to distinguish between normal data and abnormal data.
And if none of the prediction scores exceeds the preset value in a prediction score optimization step, determining that no data node recommended to be added exists in the second data chain, and marking the second data set as an abnormal data set.
On the basis, matching the payer information and the payee information of all the second data sets marked as abnormal data sets and the corresponding first data sets to obtain a matching result, so that abnormal identification of the financial big data is realized.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A financial big data analysis method is realized based on an improved collaborative filtering algorithm, and comprises the following steps:
step S100: concurrently receiving a financial data stream over multiple data channels, the financial data stream being sent in payment events;
step S200: performing attribute analysis on the financial data stream, and dividing the financial data stream into at least a first data set and a second data set;
step S300: sending the first data set and the second data set to a first data chain and a second data chain respectively;
step S400: updating the first data chain and the second data chain based on an improved collaborative filtering algorithm;
step S500: performing collaborative matching analysis on the financial data stream by using the first data chain and the second data chain;
the method is characterized in that:
in step S100, the payment event of one unit includes information of a payer and information of a payee; the payer information comprises a payer ID number, a payment mode, a payment medium and payment request time, and the payee information comprises a payee ID number, a collection mode, a collection medium and collection response time;
the step S200 specifically includes:
the first data set comprises information of a payer, hardware information of a payment terminal, a payment mode and payment request time;
the second data set comprises payee information, hardware information of a payee terminal, a payment result and payment response time;
in step S300, the first data link is established based on a first data set, and the second data link is established based on a second data set.
2. A financial big data analysis method according to claim 1, characterized in that:
the first data link or the second data link comprises at least one data node, each data node corresponds to payer information or payee information, the payer information comprises a first identity ID used for uniquely identifying a payer, and the payee information comprises a second identity ID used for uniquely identifying a payee.
3. A financial big data analysis method according to claim 1, characterized in that:
in step S400, updating the first data chain based on an improved collaborative filtering algorithm specifically includes:
receiving a join request of the first data set by a current first data link, wherein the join request comprises payer information;
generating a first identity ID of the payer according to the payer information;
judging whether a data node corresponding to the first identity ID exists in the first data chain or not;
if so, adding the first data set to the data node;
and if not, giving at least one data node recommended to join from the first data chain based on an improved collaborative filtering algorithm.
4. A financial big data analysis method according to claim 1, characterized in that:
in step S400, updating the second data chain based on an improved collaborative filtering algorithm specifically includes:
receiving a join request of the second data set by a current second data chain, wherein the join request comprises payee information;
generating a second identity ID of the payee according to the payee information;
judging whether a data node corresponding to the second identity ID exists in the second data chain or not;
if so, adding the second data set to the data node;
and if not, determining whether the data node recommended to be added exists from the second data chain based on an improved collaborative filtering algorithm.
5. A financial big data analysis method according to any of the preceding claims, wherein:
the improved collaborative filtering algorithm comprises a time sequence updating step, a trust degree calculating step and a prediction score optimizing step;
the time sequence updating step is used for calculating the similarity between the existing data nodes in the first data chain or the second data chain and the first data stream or the second data stream based on the payment request time or the payment response time corresponding to the existing data nodes in the first data chain or the second data chain;
the trust degree calculation step calculates the trust degree between the sending network of the first data stream or the second data stream and the data connection network of the first data chain or the second data chain;
and the prediction score optimization step is used for calculating the prediction scores of the existing data nodes in the first data chain or the second data chain and the first data flow or the second data flow based on the similarity and the trust degree, and taking the data nodes with the prediction scores exceeding a preset value as the nodes recommended to be added.
6. The financial big data analysis method of claim 4, wherein:
if the data node corresponding to the second identity ID does not exist in the second data chain, determining whether a data node recommended to be added exists in the second data chain based on an improved collaborative filtering algorithm, specifically including:
and if the second data chain is determined to have no data node recommended to be added, marking the second data set as an abnormal data set.
7. The financial big data analysis method of claim 6, wherein:
the step S500 of performing a collaborative matching analysis on the financial data stream by using the first data chain and the second data chain specifically includes:
and matching the payer information and the payee information of all the second data sets marked as abnormal data sets and the corresponding first data sets to obtain a matching result.
8. A financial big data analysis method according to claim 1, characterized in that:
the payment medium includes: bank card, wearable payment equipment, portable mobile payment equipment.
9. A financial big data analysis method according to claim 1, characterized in that:
the payment mode or the collection mode comprises one or the combination of card swiping payment, flash payment, code scanning payment, biological payment, Bluetooth payment, card-free payment and NFC payment.
10. A computer-readable storage medium having stored thereon computer-executable program instructions that are executable by a processor and a memory for performing the method of any of claims 1-9.
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