CN106384253A - Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof - Google Patents
Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof Download PDFInfo
- Publication number
- CN106384253A CN106384253A CN201610872621.8A CN201610872621A CN106384253A CN 106384253 A CN106384253 A CN 106384253A CN 201610872621 A CN201610872621 A CN 201610872621A CN 106384253 A CN106384253 A CN 106384253A
- Authority
- CN
- China
- Prior art keywords
- transaction
- card
- transactions
- consumption
- association rule
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明涉及一种银行卡交易中消费行为分析方法及装置，其中，分析方法包括：按照银行卡在中国境内刷卡消费的特约商户的类别代码为分类依据，统计一段交易时间的交易数据，获得交易汇总；其中，所述交易汇总包括：在交易时间段内每张卡交易的笔数、在交易时间段内刷卡消费所对应的卡数；对所述交易汇总进行计算，利用在交易时间段内刷卡消费所对应的卡数获得每张卡交易的笔数相应范围内每个类别代码的支持度；根据关联规则，利用所述每个类别代码的支持度确定每条关联规则的置信度；根据所述每条关联规则的置信度确定强关联规则集。
The present invention relates to a method and device for analyzing consumer behavior in bank card transactions, wherein the analysis method includes: according to the category codes of special merchants who swipe the bank card for consumption in China as the classification basis, count the transaction data for a certain period of time, and obtain the transaction Summary; wherein, the transaction summary includes: the number of transactions per card within the transaction time period, the number of cards corresponding to card consumption within the transaction time period; the transaction summary is calculated, and the transaction period is used The number of cards corresponding to the credit card consumption obtains the support degree of each category code within the corresponding range of the number of transactions of each card; according to the association rules, the confidence degree of each association rule is determined by the support degree of each category code; according to The confidence degree of each association rule determines a strong association rule set.
Description
技术领域technical field
本发明涉及信息挖掘技术领域，特别涉及一种银行卡交易中消费行为分析方法及装置。The invention relates to the technical field of information mining, in particular to a method and device for analyzing consumer behavior in bank card transactions.
背景技术Background technique
随着银行卡市场的不断发展和受理市场的不断拓展，对银行卡交易数据分析的深度和广度不够的弱点日益突出。更好地跟踪分析银行卡业务发展的趋势和市场的发展变化，对整个银行卡产业的分析决策中变得越来越关键。With the continuous development of the bank card market and the continuous expansion of the acceptance market, the weakness of insufficient depth and breadth of bank card transaction data analysis has become increasingly prominent. To better track and analyze the development trend of the bank card business and the development and changes of the market has become more and more critical in the analysis and decision-making of the entire bank card industry.
银联信息中心储存了大量的持卡人消费交易数据，传统的分析方法是对交易中各项特征逐一汇总分析，无法发现多个特征之间的关联关系。并且，传统的数据分析架构已经不能适应大数据环境下的要求，无法对海量数据进行实时处理和深度挖掘。The UnionPay information center stores a large amount of cardholder consumption transaction data. The traditional analysis method is to summarize and analyze each feature in the transaction one by one, and it is impossible to find the correlation between multiple features. Moreover, the traditional data analysis architecture can no longer adapt to the requirements of the big data environment, and cannot perform real-time processing and deep mining of massive data.
发明内容Contents of the invention
本发明实施例的主要目的在于提出一种银行卡交易中消费行为分析方法及装置，对银联信息中心储存的大量的持卡人消费交易数据进行挖掘，达到向消费者精准推送消费信息的目的。The main purpose of the embodiments of the present invention is to propose a method and device for analyzing consumption behavior in bank card transactions, which can mine a large amount of cardholder consumption transaction data stored in the UnionPay Information Center, and achieve the purpose of accurately pushing consumption information to consumers.
为实现上述目的，本发明提供了一种银行卡交易中消费行为分析方法，包括：In order to achieve the above object, the present invention provides a method for analyzing consumer behavior in bank card transactions, including:
按照银行卡在中国境内刷卡消费的特约商户的类别代码为分类依据，统计一段交易时间的交易数据，获得交易汇总；其中，所述交易汇总包括：在交易时间段内每张卡交易的笔数、在交易时间段内刷卡消费所对应的卡数；According to the category code of the special merchants who use the bank card for consumption in China as the classification basis, the transaction data of a certain period of transaction is counted to obtain the transaction summary; wherein, the transaction summary includes: the number of transactions per card within the transaction period , The number of cards corresponding to card consumption within the transaction time period;
对所述交易汇总进行计算，利用在交易时间段内刷卡消费所对应的卡数获得每张卡交易的笔数相应范围内每个类别代码的支持度；Calculate the transaction summary, and obtain the support degree of each category code within the corresponding range of the transaction number of each card by using the number of cards corresponding to the card consumption within the transaction time period;
根据关联规则，利用所述每个类别代码的支持度确定每条关联规则的置信度；According to the association rules, using the support degree of each category code to determine the confidence degree of each association rule;
根据所述每条关联规则的置信度确定强关联规则集。A strong association rule set is determined according to the confidence of each association rule.
可选的，在本发明一实施例中，所述计算的方法为频繁项集算法。Optionally, in an embodiment of the present invention, the calculation method is a frequent itemset algorithm.
可选的，在本发明一实施例中，所述每张卡交易的笔数相应范围包括80-100笔的卡交易、大于500笔的卡交易。Optionally, in an embodiment of the present invention, the corresponding range of the number of transactions per card includes 80-100 card transactions and more than 500 card transactions.
可选的，在本发明一实施例中，所述强关联规则集为置信度大于等于设定阈值的关联规则的集合。Optionally, in an embodiment of the present invention, the strong association rule set is a set of association rules whose confidence is greater than or equal to a set threshold.
为实现上述目的，本发明还提供了一种银行卡交易中消费行为分析装置，包括：In order to achieve the above object, the present invention also provides a consumer behavior analysis device in bank card transactions, including:
数据预处理单元，用于按照银行卡在中国境内刷卡消费的特约商户的类别代码为分类依据，统计一段交易时间的交易数据，获得交易汇总；其中，所述交易汇总包括：在交易时间段内每张卡交易的笔数、在交易时间段内刷卡消费所对应的卡数；The data preprocessing unit is used to classify according to the category codes of special merchants who swipe the bank card for consumption in China, and collect transaction data for a certain period of time to obtain transaction summaries; wherein, the transaction summaries include: within the transaction time period The number of transactions per card, and the number of cards corresponding to card consumption within the transaction time period;
支持度获取单元，用于对所述交易汇总进行计算，利用在交易时间段内刷卡消费所对应的卡数获得每张卡交易的笔数相应范围内每个类别代码的支持度；The support degree acquisition unit is used to calculate the transaction summary, and obtain the support degree of each category code within the corresponding range of the number of transactions of each card by using the number of cards corresponding to the consumption by swiping the card within the transaction time period;
置信度获取单元，用于根据关联规则，利用所述每个类别代码的支持度确定每条关联规则的置信度；A confidence acquisition unit, configured to determine the confidence of each association rule by using the support of each category code according to the association rules;
分析单元，用于根据所述每条关联规则的置信度确定强关联规则集。An analyzing unit, configured to determine a strong association rule set according to the confidence degree of each association rule.
可选的，在本发明一实施例中，所述支持度获取单元对所述交易汇总采用频繁项集算法进行计算。Optionally, in an embodiment of the present invention, the support acquisition unit calculates the transaction summary using a frequent itemset algorithm.
可选的，在本发明一实施例中，所述支持度获取单元采用的每张卡交易的笔数相应范围包括80-100笔的卡交易、大于500笔的卡交易。Optionally, in an embodiment of the present invention, the corresponding range of the number of transactions per card used by the support acquisition unit includes 80-100 card transactions and more than 500 card transactions.
可选的，在本发明一实施例中，所述分析单元获得的强关联规则集为置信度大于等于设定阈值的关联规则的集合。Optionally, in an embodiment of the present invention, the set of strong association rules obtained by the analyzing unit is a set of association rules whose confidence is greater than or equal to a set threshold.
上述技术方案具有如下有益效果：The above technical scheme has the following beneficial effects:
本技术方案引入大规模并行处理技术和分布式架构，满足了交易大数据环境下的要求，可以对海量数据进行全量处理和关联规则挖掘。This technical solution introduces large-scale parallel processing technology and distributed architecture, which meets the requirements of the transaction big data environment, and can perform full processing and association rule mining on massive data.
附图说明Description of drawings
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.
图1为本发明实施例提出的一种银行卡交易中消费行为分析方法流程图；Fig. 1 is a flow chart of a method for analyzing consumer behavior in bank card transactions proposed by an embodiment of the present invention;
图2为本发明实施例提出的一种银行卡交易中消费行为分析装置框图。Fig. 2 is a block diagram of a device for analyzing consumer behavior in bank card transactions proposed by an embodiment of the present invention.
具体实施方式detailed description
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
本技术方案的工作原理为：本发明利用大数据技术，通过对海量的银行卡交易数据，进行数据预处理、数据挖掘及业务解释和评估，从中挖掘银行卡交易中多项特征间的稳定关联规则，获取持卡人消费行为的喜好，预测出持卡人将来的消费行为，并提出有针对性的营销活动。The working principle of this technical solution is as follows: the present invention utilizes big data technology to perform data preprocessing, data mining, business interpretation and evaluation on massive bank card transaction data, and to mine stable correlations among multiple features in bank card transactions Rules, obtain the preferences of cardholders' consumption behavior, predict the future consumption behavior of cardholders, and propose targeted marketing activities.
基于上述工作原理，本发明实施例提出一种银行卡交易中消费行为分析方法，如图1所示。包括：Based on the above working principle, an embodiment of the present invention proposes a method for analyzing consumer behavior in bank card transactions, as shown in FIG. 1 . include:
步骤101)：按照银行卡在中国境内刷卡消费的特约商户的类别代码为分类依据，统计一段交易时间的交易数据，获得交易汇总；其中，所述交易汇总包括：在交易时间段内每张卡交易的笔数、在交易时间段内刷卡消费所对应的卡数；Step 101): According to the category code of the special merchant who swipes the bank card for consumption in China as the classification basis, the transaction data of a certain period of transaction time is counted to obtain the transaction summary; wherein, the transaction summary includes: each card within the transaction period The number of transactions and the number of cards corresponding to card consumption within the transaction time period;
步骤102)：对所述交易汇总进行计算，利用在交易时间段内刷卡消费所对应的卡数获得每张卡交易的笔数相应范围内每个类别代码的支持度；Step 102): Calculate the transaction summary, use the number of cards corresponding to the card consumption within the transaction time period to obtain the support degree of each category code within the corresponding range of the transaction number of each card;
步骤103)：根据关联规则，利用所述每个类别代码的支持度确定每条关联规则的置信度；Step 103): According to the association rules, use the support of each category code to determine the confidence of each association rule;
步骤104)：根据所述每条关联规则的置信度确定强关联规则集。Step 104): Determine a strong association rule set according to the confidence degree of each association rule.
在本实施例中，对于步骤101来说，提前对一段时间的交易数据，按照银行卡在中国境内刷卡消费的特约商户的类别代码(MCC)为分类依据，统计获得交易汇总。交易汇总包括：每张卡在交易时间段内交易的笔数以及在交易时间段内刷卡消费的卡数。例如：80-100笔的卡和大于500笔的卡的两组交易数据。交易数据为：在这段交易时间内在不同MCC的商户进行刷卡消费的数据信息。In this embodiment, for step 101, the transaction data for a certain period of time in advance is classified according to the category code (MCC) of the special merchants who use the bank card for consumption in China, and the transaction summary is obtained statistically. The transaction summary includes: the number of transactions for each card within the transaction period and the number of card consumption within the transaction period. For example: two sets of transaction data for cards with 80-100 transactions and cards with more than 500 transactions. The transaction data is: the data information of credit card consumption at different MCC merchants within this period of transaction time.
在步骤102中，采用频繁项集算法对交易汇总进行计算，获得每张卡交易的笔数相应范围内每个MCC的支持度。在本实施例中，支持度为：在相同的交易时间段内，某一MCC的商户进行刷卡消费的交易卡数除以在所有的MCC的商户进行刷卡消费的总交易卡数。In step 102, the transaction summary is calculated using the frequent itemset algorithm, and the support degree of each MCC within the corresponding range of the transaction number of each card is obtained. In this embodiment, the degree of support is: within the same transaction time period, the number of transaction cards used by merchants of a certain MCC divided by the total number of transaction cards used for consumption by merchants of an MCC.
根据交易汇总，可能需要调整频繁项集分析中最小支持度的阈值，例如：对交易笔数大于500的卡，取支持度大于5％的MCC，对交易笔数在80-100笔的卡，取支持度大于30％的MCC。According to the transaction summary, it may be necessary to adjust the minimum support threshold in frequent itemset analysis, for example: for cards with more than 500 transactions, MCC with support greater than 5%, for cards with 80-100 transactions, Take the MCC whose support is greater than 30%.
在步骤103中，根据支持度，计算置信度，见下表1：In step 103, the confidence is calculated according to the support, see Table 1 below:
表1Table 1
第一行，MCC为58xx支持度为36.7的含义为：在一定的交易时间段内，在58xx类商户内刷卡消费的卡数占总交易卡数的36.7％。在58xx类商户内刷卡消费的同时在54xx类商户内刷卡消费的卡数占在58xx类商户内刷卡消费的卡数的95.3％。In the first line, the MCC is 58xx and the support degree is 36.7. It means: within a certain transaction time period, the number of card consumption in 58xx merchants accounts for 36.7% of the total number of transaction cards. The number of credit card consumption in 54xx merchants while swiping cards in 58xx merchants accounted for 95.3% of the number of credit card consumption in 58xx merchants.
第二行，在53xx类商户和58xx类商户同时刷卡消费的卡数占总交易卡数的21.9％，在53xx类商户和58xx类商户同时刷卡消费的基础上，又在54xx类商户内刷卡消费的卡数占53xx类商户和58xx类商户同时刷卡消费的卡数的98％。In the second line, the number of card consumption at 53xx merchants and 58xx merchants at the same time accounted for 21.9% of the total number of transaction cards. On the basis of simultaneous card consumption at 53xx merchants and 58xx merchants, card consumption at 54xx merchants The number of cards accounted for 98% of the number of cards used by 53xx merchants and 58xx merchants at the same time.
在步骤104中，根据所述每条关联规则的置信度确定强关联规则集。根据实际经验值，将置信度大于某一阈值的关联规则作为强关联规则。In step 104, a strong association rule set is determined according to the confidence of each association rule. According to the actual experience value, the association rules with confidence greater than a certain threshold are regarded as strong association rules.
在本实施例中，将置信度大于90％的关联规则作为强关联规则。在表1中，58xx→54xx以及53xx+58xx→54xx均为强关联规则。In this embodiment, an association rule with a confidence greater than 90% is regarded as a strong association rule. In Table 1, 58xx→54xx and 53xx+58xx→54xx are strong association rules.
也就是说，在58xx类商户刷卡消费之后，高达95.3％的概率进入54xx类商户刷卡消费，在完成58xx类商户刷卡消费之后，业务市场部门的服务器向消费者精准发送关于54xx类商户的消费信息，引导和刺激消费者刷卡消费。That is to say, after swipe card consumption at 58xx merchants, there is a 95.3% probability of entering 54xx merchants for swipe card consumption. After completing 58xx merchant swipe card consumption, the server of the business marketing department will accurately send consumption information about 54xx merchants to consumers , Guide and stimulate consumers to swipe their cards for consumption.
由上述实施例可知，本技术方案获取持卡人消费行为的喜好，预测出持卡人将来的消费行为，并提出有针对性的营销活动，从而达到降低经营成本和提高企业竞争力的双重目的。It can be seen from the above embodiments that this technical solution obtains the preferences of the cardholder's consumption behavior, predicts the future consumption behavior of the cardholder, and proposes targeted marketing activities, so as to achieve the dual purposes of reducing operating costs and improving corporate competitiveness .
如图2所示，为本发明实施例提出的一种银行卡交易中消费行为分析装置框图。包括：As shown in FIG. 2 , it is a block diagram of a device for analyzing consumer behavior in bank card transactions proposed by an embodiment of the present invention. include:
数据预处理单元201，用于按照银行卡在中国境内刷卡消费的特约商户的类别代码为分类依据，统计一段交易时间的交易数据，获得交易汇总；其中，所述交易汇总包括：在交易时间段内每张卡交易的笔数、在交易时间段内刷卡消费所对应的卡数；The data preprocessing unit 201 is used to classify according to the category codes of special merchants who use bank cards for consumption within the territory of China, and collect transaction data for a certain period of transaction time to obtain transaction summaries; wherein, the transaction summaries include: The number of transactions per card within the transaction period, and the number of cards corresponding to card consumption within the transaction time period;
支持度获取单元202，用于对所述交易汇总进行计算，利用在交易时间段内刷卡消费所对应的卡数获得每张卡交易的笔数相应范围内每个类别代码的支持度；The support degree acquisition unit 202 is used to calculate the transaction summary, and obtain the support degree of each category code within the corresponding range of the number of transactions of each card by using the number of cards corresponding to the consumption by swiping the card within the transaction time period;
置信度获取单元203，用于根据关联规则，利用所述每个类别代码的支持度确定每条关联规则的置信度；Confidence degree acquisition unit 203, for determining the confidence degree of each association rule by using the support degree of each category code according to the association rules;
分析单元204，用于根据所述每条关联规则的置信度确定强关联规则集。An analysis unit 204, configured to determine a strong association rule set according to the confidence of each association rule.
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一般计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)或随机存储记忆体(Random AccessMemory，RAM)等。Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware to complete, and the programs can be stored in general computer-readable storage media. During execution, it may include the processes of the embodiments of the above-mentioned methods. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random Access Memory, RAM) and the like.
本领域技术人员还可以了解到本发明实施例列出的各种功能是通过硬件还是软件来实现取决于特定的应用和整个系统的设计要求。本领域技术人员可以对于每种特定的应用，可以使用各种方法实现所述的功能，但这种实现不应被理解为超出本发明实施例保护的范围。Those skilled in the art can also understand that whether various functions listed in the embodiments of the present invention are implemented by hardware or software depends on specific applications and design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be understood as exceeding the protection scope of the embodiments of the present invention.
本发明中应用了具体实施例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。In the present invention, specific examples have been applied to explain the principles and implementation methods of the present invention, and the descriptions of the above examples are only used to help understand the method of the present invention and its core idea; meanwhile, for those of ordinary skill in the art, according to this The idea of the invention will have changes in the specific implementation and scope of application. To sum up, the contents of this specification should not be construed as limiting the present invention.
以上具体实施方式，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上仅为本发明的具体实施方式而已，并不用于限定本发明的保护范围，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above specific embodiments have further described the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention, and are not used to limit the scope of protection of the present invention. Within the spirit and principles of the present invention, any modifications, equivalent replacements, improvements, etc., shall be included in the protection scope of the present invention.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610872621.8A CN106384253A (en) | 2016-09-30 | 2016-09-30 | Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610872621.8A CN106384253A (en) | 2016-09-30 | 2016-09-30 | Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106384253A true CN106384253A (en) | 2017-02-08 |
Family
ID=57937027
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610872621.8A Pending CN106384253A (en) | 2016-09-30 | 2016-09-30 | Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106384253A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110348982A (en) * | 2019-05-21 | 2019-10-18 | 平安银行股份有限公司 | Data verification method up to standard and device, electronic equipment and non-transient storage media |
| CN111221885A (en) * | 2020-01-06 | 2020-06-02 | 中国银联股份有限公司 | Method and system for calculating data ranking |
| CN111951035A (en) * | 2019-05-17 | 2020-11-17 | 上海树融数据科技有限公司 | Consumption analysis method, system, device and consumption analysis platform |
| CN112667968A (en) * | 2020-12-22 | 2021-04-16 | 银联商务股份有限公司 | Data processing device, method, equipment and readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101493925A (en) * | 2009-03-09 | 2009-07-29 | 浙江工商大学 | Retail industry dime ticket generating method by employing increment type excavation |
| CN104504159A (en) * | 2015-01-19 | 2015-04-08 | 齐鲁工业大学 | Application of multi-supporting-degree positive and negative sequence modes in clients' purchasing behavior analysis |
| CN104732419A (en) * | 2015-01-19 | 2015-06-24 | 齐鲁工业大学 | Application of positive and negative sequence mode screening method in customer purchasing behavior analysis |
| CN105183785A (en) * | 2015-08-17 | 2015-12-23 | 上海斐讯数据通信技术有限公司 | Data mining method and system for protecting association rule of original transaction data set |
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | 中国银联股份有限公司 | Cardholder consumption figure generation method and device |
-
2016
- 2016-09-30 CN CN201610872621.8A patent/CN106384253A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101493925A (en) * | 2009-03-09 | 2009-07-29 | 浙江工商大学 | Retail industry dime ticket generating method by employing increment type excavation |
| CN104504159A (en) * | 2015-01-19 | 2015-04-08 | 齐鲁工业大学 | Application of multi-supporting-degree positive and negative sequence modes in clients' purchasing behavior analysis |
| CN104732419A (en) * | 2015-01-19 | 2015-06-24 | 齐鲁工业大学 | Application of positive and negative sequence mode screening method in customer purchasing behavior analysis |
| CN105183785A (en) * | 2015-08-17 | 2015-12-23 | 上海斐讯数据通信技术有限公司 | Data mining method and system for protecting association rule of original transaction data set |
| CN105931068A (en) * | 2015-12-30 | 2016-09-07 | 中国银联股份有限公司 | Cardholder consumption figure generation method and device |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111951035A (en) * | 2019-05-17 | 2020-11-17 | 上海树融数据科技有限公司 | Consumption analysis method, system, device and consumption analysis platform |
| CN111951035B (en) * | 2019-05-17 | 2024-06-11 | 嘉兴树融数据科技有限公司 | Consumption analysis method, system, device and platform |
| CN110348982A (en) * | 2019-05-21 | 2019-10-18 | 平安银行股份有限公司 | Data verification method up to standard and device, electronic equipment and non-transient storage media |
| CN111221885A (en) * | 2020-01-06 | 2020-06-02 | 中国银联股份有限公司 | Method and system for calculating data ranking |
| CN112667968A (en) * | 2020-12-22 | 2021-04-16 | 银联商务股份有限公司 | Data processing device, method, equipment and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11288657B1 (en) | Detecting device presence indication | |
| US11282083B2 (en) | Systems and methods for detecting transaction card fraud based on geographic patterns of purchases | |
| US9818117B2 (en) | Systems and methods for using social network data to determine payment fraud | |
| US20200226460A1 (en) | Neural embeddings of transaction data | |
| US20170178139A1 (en) | Analysis of Transaction Information Using Graphs | |
| CN110111113B (en) | Abnormal transaction node detection method and device | |
| EP2840541A2 (en) | System, method, and computer program for dynamically identifying a merchant associated with an authorization request for a payment card | |
| CN106384253A (en) | Consumption behavior analysis method in bankcard transaction and consumption behavior analysis device thereof | |
| US11715106B2 (en) | Systems and methods for real-time institution analysis based on message traffic | |
| CN112037038A (en) | Bank credit risk prediction method and device | |
| CN109493086B (en) | Method and device for determining illegal commercial tenant | |
| WO2017112118A1 (en) | Systems and methods for use in determining detailed locations for certain entities | |
| CN107767155B (en) | A method and system for evaluating user portrait data | |
| WO2021202151A1 (en) | Systems and methods for advanced velocity profile preparation and analysis | |
| US20160005072A1 (en) | Method and system for online commerce analysis | |
| US20250190993A1 (en) | Methods and systems for reducing false positives for financial transaction fraud monitoring using artificial intelligence | |
| US20160260113A1 (en) | Systems and methods for visualizing performance, performing advanced analytics, and invoking actions with respect to a financial institution | |
| Louzada et al. | A zero-inflated non default rate regression model for credit scoring data | |
| CA3146127A1 (en) | System for detecting associated records in a record log | |
| CN106779723A (en) | A kind of mobile terminal methods of risk assessment and device | |
| Gümüş et al. | Ultimate Point in the Service Provided by the Banks to their Customers: Customer Satisfaction in the Common use of ATMs | |
| CN107679862B (en) | Method and device for determining characteristic value of fraud transaction model | |
| CN109919626B (en) | High-risk bank card identification method and device | |
| WO2021202222A1 (en) | Systems and methods for message tracking using real-time normalized scoring | |
| CN106815290B (en) | Method and device for determining attribution of bank card based on graph mining |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170208 |