CN111582878A - A transaction risk prediction method, device and system - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及计算机数据处理技术领域,更具体的说,涉及一种交易风险预测方法、装置及系统。The present invention relates to the technical field of computer data processing, and more particularly, to a transaction risk prediction method, device and system.
背景技术Background technique
银行柜面业务通常有一系列的操作规范要求,一个业务场景由一系列的交易节点和交易数据构成。以客户办理外币兑换业务为例,柜员需要在银行系统中顺序操作以下交易节点:自动读取身份证信息、发起身份信息联网核查交易、创建临时客户、外币兑换、授权审核以及取出外钞。在该流程中,例如发起身份信息联网核查交易和授权审核两个交易节点漏做,或是柜员违规操作其他交易节点,比如操作其他转账交易节点转移客户资金等,都会导致整个交易存在较大的风险。The bank counter business usually has a series of operational specification requirements, and a business scenario consists of a series of transaction nodes and transaction data. Taking the customer's foreign currency exchange business as an example, the teller needs to sequentially operate the following transaction nodes in the banking system: automatic reading of ID card information, initiation of online verification transaction of identity information, creation of temporary customers, foreign currency exchange, authorization review, and withdrawal of foreign banknotes. In this process, for example, the two transaction nodes that initiate identity information online verification transaction and authorization review are missed, or the teller illegally operates other transaction nodes, such as operating other transfer transaction nodes to transfer customer funds, etc., which will lead to the entire transaction. risk.
银行的业务数据分散在不同的后台业务系统中,当前银行柜面业务的事中风险控制,通常是由单一后台业务系统对交易数据进行规则判断,以发现交易风险。例如,在外币兑换时,银行系统识别出客户无该银行的客户号时会进行交易拦截。虽然这种柜台交易层级的监控在一定程度上可以拦截部分违规交易或错误操作发生,但是这种监控方式仅可以实现对交易数据的风险预测,无法实现对交易操作中交易节点的风险预测。The business data of a bank is scattered in different back-end business systems. Currently, in-process risk control of bank over-the-counter business is usually performed by a single back-end business system to make rule judgments on transaction data to discover transaction risks. For example, during foreign currency exchange, the banking system will block the transaction when it recognizes that the customer does not have the bank's customer number. Although this kind of over-the-counter transaction-level monitoring can intercept some illegal transactions or erroneous operations to a certain extent, this monitoring method can only achieve risk prediction of transaction data, and cannot achieve risk prediction of transaction nodes in transaction operations.
发明内容SUMMARY OF THE INVENTION
有鉴于此,本发明公开一种交易风险预测方法、装置及系统,以实现对交易操作中交易节点的风险预测,从而通过提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。In view of this, the present invention discloses a transaction risk prediction method, device and system, so as to realize the risk prediction of transaction nodes in transaction operations, so as to remind business personnel to verify and correct risky transaction behaviors, thereby reducing the risk of risky transaction behaviors. transaction risk.
一种交易风险预测方法,包括:A trading risk prediction method, including:
获取交易行为数据;Obtain transaction behavior data;
对所述交易行为数据进行交易节点特征提取,得到第一特征数据集,所述第一特征数据集包括:各个交易节点的节点名称以及节点执行顺序;Extracting transaction node features from the transaction behavior data to obtain a first feature data set, where the first feature data set includes: node names of each transaction node and node execution order;
利用构建的风险评估模型对所述第一特征数据集进行风险预测,得到风险预测结果。Use the constructed risk assessment model to perform risk prediction on the first feature data set to obtain a risk prediction result.
可选的,所述风险评估模型为:基于不同业务场景下的历史交易节点数据中已打标的交易节点数据训练获得的模型,所述已打标的交易节点数据为历史交易节点数据中已有风险标签的交易节点数据,所述风险标签包括:风险事件和非风险事件。Optionally, the risk assessment model is: a model obtained by training based on the marked transaction node data in the historical transaction node data under different business scenarios, and the marked transaction node data is the one in the historical transaction node data. Transaction node data with risk labels, the risk labels include: risk events and non-risk events.
可选的,所述风险评估模型的构建过程包括:Optionally, the construction process of the risk assessment model includes:
获取不同业务场景下历史交易节点数据;Obtain historical transaction node data in different business scenarios;
根据不同业务场景下,基于业务规则确定的业务正常操作顺序,对所述历史交易节点数据进行打标,得到所述已打标的交易节点数据;According to different business scenarios, based on the normal business operation sequence determined by business rules, mark the historical transaction node data to obtain the marked transaction node data;
对所述已打标的交易节点数据进行过交易节点特征提取,得到第二特征数据集;Performing transaction node feature extraction on the marked transaction node data to obtain a second feature data set;
对所述第二特征数据集进行训练,得到的风险评估模型。The risk assessment model is obtained by training the second feature data set.
可选的,还包括:Optionally, also include:
根据所述风险预测结果确定交易风险监控方式,所述风险预测结果包括:异常概率或者风险程度,所述交易风险监控方式包括:提示和/或拦截交易。A transaction risk monitoring method is determined according to the risk prediction result, where the risk prediction result includes: abnormal probability or risk degree, and the transaction risk monitoring method includes: prompting and/or intercepting transactions.
可选的,还包括:Optionally, also include:
基于所述第一特征数据集和所述风险预测结果,对所述风险评估模型进行优化。Based on the first feature data set and the risk prediction result, the risk assessment model is optimized.
一种交易风险预测装置,包括:A transaction risk prediction device, comprising:
获取单元,用于获取交易行为数据;The acquisition unit is used to acquire transaction behavior data;
提取单元,用于对所述交易行为数据进行交易节点特征提取,得到第一特征数据集,所述第一特征数据集包括:各个交易节点的节点名称以及节点执行顺序;an extraction unit, configured to perform transaction node feature extraction on the transaction behavior data to obtain a first feature data set, where the first feature data set includes: node names of each transaction node and node execution sequence;
预测单元,用于利用构建的风险评估模型对所述第一特征数据集进行风险预测,得到风险预测结果。A prediction unit, configured to perform risk prediction on the first feature data set by using the constructed risk assessment model to obtain a risk prediction result.
可选的,所述风险评估模型为:基于不同业务场景下的历史交易节点数据中已打标的交易节点数据训练获得的模型,所述已打标的交易节点数据为历史交易节点数据中已有风险标签的交易节点数据,所述风险标签包括:风险事件和非风险事件。Optionally, the risk assessment model is: a model obtained by training based on the marked transaction node data in the historical transaction node data under different business scenarios, and the marked transaction node data is the one in the historical transaction node data. Transaction node data with risk labels, the risk labels include: risk events and non-risk events.
可选的,还包括:模型构建单元,用于构建所述风险评估模型;Optionally, it also includes: a model building unit for building the risk assessment model;
模型构建单元具体用于:Model building units are specifically used to:
获取不同业务场景下历史交易节点数据;Obtain historical transaction node data in different business scenarios;
根据不同业务场景下,基于业务规则确定的业务正常操作顺序,对所述历史交易节点数据进行打标,得到所述已打标的交易节点数据;According to different business scenarios, based on the normal business operation sequence determined by business rules, mark the historical transaction node data to obtain the marked transaction node data;
对所述已打标的交易节点数据进行过交易节点特征提取,得到第二特征数据集;Performing transaction node feature extraction on the marked transaction node data to obtain a second feature data set;
对所述第二特征数据集进行训练,得到的风险评估模型。The risk assessment model is obtained by training the second feature data set.
可选的,还包括:Optionally, also include:
监控确定单元,用于根据所述风险预测结果确定交易风险监控方式,所述风险预测结果包括:异常概率或者风险程度,所述交易风险监控方式包括:提示和/或拦截交易。A monitoring and determining unit, configured to determine a transaction risk monitoring method according to the risk prediction result, where the risk prediction result includes: abnormal probability or risk degree, and the transaction risk monitoring method includes: prompting and/or intercepting transactions.
可选的,还包括:Optionally, also include:
优化单元,用于基于所述第一特征数据集和所述风险预测结果,对所述风险评估模型进行优化。An optimization unit, configured to optimize the risk assessment model based on the first feature data set and the risk prediction result.
一种交易风险预测系统,包括至少一个处理器以及存储计算机可执行指令的存储器,所述处理器执行所述指令实现上述所述方法的步骤。A transaction risk prediction system includes at least one processor and a memory storing computer-executable instructions, the processor executing the instructions to implement the steps of the above-mentioned method.
从上述的技术方案可知,本发明公开了一种交易风险预测方法、装置及系统,对获取的交易行为数据进行交易节点特征提取,得到第一特征数据集,该第一特征数据集中包括:各个交易节点的节点名称以及节点执行顺序,利用构建的风险评估模型对第一特征数据集进行风险预测,得到风险预测结果。因此,本发明实现了对交易操作中交易节点的风险预测,从而通过提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。As can be seen from the above technical solutions, the present invention discloses a transaction risk prediction method, device and system. The acquired transaction behavior data is subjected to transaction node feature extraction to obtain a first feature data set. The first feature data set includes: each The node name of the transaction node and the node execution order are used to predict the risk of the first feature data set by using the constructed risk assessment model to obtain the risk prediction result. Therefore, the present invention realizes the risk prediction of the transaction nodes in the transaction operation, thereby reducing the transaction risk brought by the risky transaction behavior by reminding the business personnel to verify and correct the risky transaction behavior.
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,还可以根据公开的附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying 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 It is an embodiment of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the disclosed drawings without creative efforts.
图1为本发明实施例公开的一种交易风险预测方法流程图;1 is a flowchart of a transaction risk prediction method disclosed in an embodiment of the present invention;
图2为本发明实施例公开的另一种交易风险预测方法流程图;2 is a flowchart of another transaction risk prediction method disclosed in an embodiment of the present invention;
图3为本发明实施例公开的一种交易风险预测装置的结构示意图;3 is a schematic structural diagram of a transaction risk prediction device disclosed in an embodiment of the present invention;
图4为本发明实施例公开的另一种交易风险预测装置的结构示意图。FIG. 4 is a schematic structural diagram of another transaction risk prediction apparatus disclosed in an embodiment of the present invention.
具体实施方式Detailed ways
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 technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
本发明实施例公开了一种本发明公开的交易风险预测方法、装置及系统,对获取的交易行为数据进行交易节点特征提取,得到第一特征数据集,该第一特征数据集中包括:各个交易节点的节点名称以及节点执行顺序,利用构建的风险评估模型对第一特征数据集进行风险预测,得到风险预测结果。因此,本发明实现了对交易操作中交易节点的风险预测,从而通过提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。The embodiment of the present invention discloses a transaction risk prediction method, device and system disclosed in the present invention. The acquired transaction behavior data is subjected to transaction node feature extraction to obtain a first feature data set, where the first feature data set includes: each transaction The node name of the node and the execution sequence of the node are used to predict the risk of the first feature data set by using the constructed risk assessment model to obtain the risk prediction result. Therefore, the present invention realizes the risk prediction of the transaction nodes in the transaction operation, thereby reducing the transaction risk brought by the risky transaction behavior by reminding the business personnel to verify and correct the risky transaction behavior.
参见图1,本发明一实施例公开的一种交易风险预测方法流程图,该方法包括步骤:Referring to FIG. 1 , a flowchart of a transaction risk prediction method disclosed in an embodiment of the present invention includes the steps:
步骤S101、获取交易行为数据;Step S101, acquiring transaction behavior data;
其中,交易行为指:银行为客户提供金融服务时,柜员通过柜员前端系统发起的一系列系统交易操作的集合,从第一个入口画面开始直到交易结束为止的整个场景下的操作过程。Among them, transaction behavior refers to the collection of a series of system transaction operations initiated by the teller through the front-end system of the teller when the bank provides financial services to customers, from the first entry screen to the end of the transaction.
交易行为数据指:执行交易行为过程中生成的数据。Transaction behavior data refers to the data generated during the execution of transaction behavior.
交易行为数据主要包括:交易节点和交易数据。The transaction behavior data mainly includes: transaction nodes and transaction data.
需要说明的是,本实施例中的交易行为数据可以是T日(交易日)实时交易行为数据,或是T+1日事后交易行为数据。It should be noted that, the transaction behavior data in this embodiment may be real-time transaction behavior data on T day (trading day), or post-event transaction behavior data on T+1 day.
步骤S102、对所述交易行为数据进行交易节点特征提取,得到第一特征数据集;Step S102, performing transaction node feature extraction on the transaction behavior data to obtain a first feature data set;
其中,第一特征集中包括:各个交易节点的节点名称以及节点执行顺序。Wherein, the first feature set includes: the node name of each transaction node and the node execution order.
在实际应用中,可以基于业务规则对当前交易行为数据进行交易节点特征提取,得到特征数据集。In practical applications, transaction node feature extraction can be performed on the current transaction behavior data based on business rules to obtain a feature data set.
以客户办理外币兑换业务为例,柜员需要在银行系统中顺序操作以下交易节点:自动读取身份证信息、发起身份信息联网核查交易、创建临时客户、外币兑换、授权审核以及取出外钞。Taking the customer's foreign currency exchange business as an example, the teller needs to sequentially operate the following transaction nodes in the banking system: automatic reading of ID card information, initiation of online verification transaction of identity information, creation of temporary customers, foreign currency exchange, authorization review, and withdrawal of foreign banknotes.
步骤S103、利用构建的风险评估模型对所述第一特征数据集进行风险预测,得到风险预测结果。Step S103 , using the constructed risk assessment model to perform risk prediction on the first feature data set to obtain a risk prediction result.
在实际应用中,将特征数据集中的特征数据输入至预先构建的风险评估模型中,得到风险预测结果。In practical applications, the characteristic data in the characteristic data set is input into the pre-built risk assessment model to obtain the risk prediction result.
风险评估模型为:基于不同业务场景下的历史交易节点数据中已打标的交易节点数据训练获得的模型。所述已打标的交易节点数据可以是历史交易节点数据中已有风险标签的交易节点数据,所述风险标签可以根据历史交易发生后用户报案、预先存储的历史交易预测结果等方式确定。所述风险标签包括:风险事件和非风险事件。The risk assessment model is: a model obtained by training based on the marked transaction node data in the historical transaction node data under different business scenarios. The marked transaction node data may be transaction node data with a risk tag in the historical transaction node data, and the risk tag may be determined according to a user report after the historical transaction occurs, a pre-stored historical transaction prediction result, or the like. The risk labels include: risk events and non-risk events.
需要说明的是,在构建风险评估模型时,预先以客户为中心,以场景为单位,整合柜员服务单个客户的所有交易流程,包括:系统间的交易信息、授权审核动作以及刷卡刷操作等等。It should be noted that when building the risk assessment model, the customer is the center in advance, and the scenario is the unit to integrate all the transaction processes of the teller serving a single customer, including: transaction information between systems, authorization review actions, and card swiping operations, etc. .
本实施例中,风险评估模型的构建过程包括:In this embodiment, the construction process of the risk assessment model includes:
获取不同业务场景下历史交易节点数据;Obtain historical transaction node data in different business scenarios;
根据不同业务场景下,基于业务规则确定的业务正常操作顺序,对所述历史交易节点数据进行打标,得到所述已打标的交易节点数据;According to different business scenarios, based on the normal business operation sequence determined by business rules, mark the historical transaction node data to obtain the marked transaction node data;
对所述已打标的交易节点数据进行过交易节点特征提取,得到第二特征数据集;Performing transaction node feature extraction on the marked transaction node data to obtain a second feature data set;
对所述第二特征数据集进行训练,得到的风险评估模型。The risk assessment model is obtained by training the second feature data set.
在实际应用中,可以采用任何一种基于大数据的机器学习模型进行模型训练,得到风险评估模型。对风险评估模型进行训练的训练样本为:特征数据集和风险标签,其中,特征数据集为:对历史交易行为数据进行交易节点特征提取得到的。In practical applications, any machine learning model based on big data can be used for model training to obtain a risk assessment model. The training samples for training the risk assessment model are: feature data set and risk label, wherein, the feature data set is obtained by extracting transaction node features from historical transaction behavior data.
训练遵循的原则为:风险评估模型的输出结果可以准确的描述交易行为数据是否存在风险。The training follows the principle that the output of the risk assessment model can accurately describe whether there is a risk in the transaction behavior data.
综上可知,本发明公开的交易风险预测方法,对获取的交易行为数据进行交易节点特征提取,得到第一特征数据集,该第一特征数据集中包括:各个交易节点的节点名称以及节点执行顺序,利用构建的风险评估模型对第一特征数据集进行风险预测,得到风险预测结果。因此,本发明实现了对交易操作中交易节点的风险预测,从而通过提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。To sum up, in the transaction risk prediction method disclosed in the present invention, the acquired transaction behavior data is subjected to transaction node feature extraction to obtain a first feature data set, where the first feature data set includes: the node names of each transaction node and the execution order of the nodes , using the constructed risk assessment model to perform risk prediction on the first feature data set to obtain the risk prediction result. Therefore, the present invention realizes the risk prediction of the transaction nodes in the transaction operation, thereby reducing the transaction risk brought by the risky transaction behavior by reminding the business personnel to verify and correct the risky transaction behavior.
由于风险交易行为数据存在不断更迭的可能,导致银行出现新的风险事件,而风险评估模型无法对新的风险交易行为进行预测,因此为完善和优化风险评估模型,本发明会不断补充新的风险交易行为数据进行训练,来更新风险评估模型,从而提高风险评估模型预测的准确率。Due to the possibility of continuous change of risk transaction behavior data, new risk events appear in the bank, and the risk assessment model cannot predict the new risk transaction behavior. Therefore, in order to improve and optimize the risk assessment model, the present invention will continuously supplement new risks. The trading behavior data is trained to update the risk assessment model, thereby improving the prediction accuracy of the risk assessment model.
为进一步优化上述实施例,在步骤S103之后,还可以包括:In order to further optimize the above embodiment, after step S103, it may further include:
基于所述第一特征数据集和所述风险预测结果,对所述风险评估模型进行优化。Based on the first feature data set and the risk prediction result, the risk assessment model is optimized.
为减少风险交易行为数据所带来的交易风险,在实际应用中,本发明还可以对风险行为进行事中拦截或事后反馈,以提醒业务人员对风险交易行为进行核实和更正。In order to reduce the transaction risk brought by the risky transaction behavior data, in practical applications, the present invention can also intercept or post-event feedback on the risky behavior, so as to remind business personnel to verify and correct the risky transaction behavior.
具体的,参见图2,本发明另一实施例公开的一种交易风险预测方法流程图,包括步骤:Specifically, referring to FIG. 2 , a flowchart of a transaction risk prediction method disclosed by another embodiment of the present invention includes steps:
步骤S101、获取交易行为数据;Step S101, acquiring transaction behavior data;
其中,交易行为指:银行为客户提供金融服务时,柜员通过柜员前端系统发起的一系列系统交易操作的集合,从第一个入口画面开始直到交易结束为止的整个场景下的操作过程。Among them, transaction behavior refers to the collection of a series of system transaction operations initiated by the teller through the front-end system of the teller when the bank provides financial services to customers, from the first entry screen to the end of the transaction.
交易行为数据指:执行交易行为过程中生成的数据。Transaction behavior data refers to the data generated during the execution of transaction behavior.
交易行为数据主要包括:交易节点和交易数据。The transaction behavior data mainly includes: transaction nodes and transaction data.
步骤S102、对所述交易行为数据进行交易节点特征提取,得到第一特征数据集;Step S102, performing transaction node feature extraction on the transaction behavior data to obtain a first feature data set;
其中,第一特征集中包括:各个交易节点的节点名称以及节点执行顺序。Wherein, the first feature set includes: the node name of each transaction node and the node execution order.
在实际应用中,可以基于业务规则对当前交易行为数据进行交易节点特征提取,得到特征数据集。In practical applications, transaction node feature extraction can be performed on the current transaction behavior data based on business rules to obtain a feature data set.
步骤S103、利用构建的风险评估模型对所述第一特征数据集进行风险预测,得到风险预测结果;Step S103, using the constructed risk assessment model to perform risk prediction on the first feature data set to obtain a risk prediction result;
在实际应用中,将特征数据集中的特征数据输入至预先构建的风险评估模型中,得到风险预测结果。In practical applications, the characteristic data in the characteristic data set is input into the pre-built risk assessment model to obtain the risk prediction result.
风险评估模型为:基于不同业务场景下的历史交易节点数据中已打标的交易节点数据训练获得的模型。所述已打标的交易节点数据可以是历史交易节点数据中已有风险标签的交易节点数据,所述风险标签可以根据历史交易发生后用户报案、预先存储的历史交易预测结果等方式确定。所述风险标签包括:风险事件和非风险事件。The risk assessment model is: a model obtained by training based on the marked transaction node data in the historical transaction node data under different business scenarios. The marked transaction node data may be transaction node data with a risk tag in the historical transaction node data, and the risk tag may be determined according to a user report after the historical transaction occurs, a pre-stored historical transaction prediction result, or the like. The risk labels include: risk events and non-risk events.
步骤S104、根据所述风险预测结果确定交易风险监控方式。Step S104: Determine a transaction risk monitoring method according to the risk prediction result.
其中,所述风险预测结果包括:异常概率或者风险程度,所述交易风险监控方式包括:提示和/或拦截交易。Wherein, the risk prediction result includes: abnormal probability or risk degree, and the transaction risk monitoring method includes: prompting and/or intercepting transactions.
综上可知,本发明公开的交易风险预测方法,对获取的交易行为数据进行交易节点特征提取,得到第一特征数据集,该第一特征数据集中包括:各个交易节点的节点名称以及节点执行顺序,利用构建的风险评估模型对第一特征数据集进行风险预测,得到风险预测结果,并根据风险预测结果确定交易封信监控方式。因此,本发明实现了对交易操作中交易节点的风险预测,并可以对异常概率或者风险程度高的交易行为进行提示和/或拦截交易,从而提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。To sum up, in the transaction risk prediction method disclosed in the present invention, the acquired transaction behavior data is subjected to transaction node feature extraction to obtain a first feature data set, where the first feature data set includes: the node names of each transaction node and the execution order of the nodes , using the constructed risk assessment model to predict the risk of the first feature data set, obtain the risk prediction result, and determine the transaction letter monitoring method according to the risk prediction result. Therefore, the present invention realizes the risk prediction of the transaction nodes in the transaction operation, and can prompt and/or intercept the transaction behaviors with abnormal probability or high degree of risk, thereby reminding the business personnel to verify and correct the risky transaction behaviors, reducing the risk of The trading risk brought about by risky trading behavior.
与上述方法实施例相对应,本发明还公开了一种交易风险预测装置。Corresponding to the above method embodiments, the present invention also discloses a transaction risk prediction device.
参见图3,本发明一实施例公开的一种交易风险预测装置的结构示意图,该装置包括:Referring to FIG. 3, a schematic structural diagram of a transaction risk prediction device disclosed in an embodiment of the present invention includes:
获取单元201,用于获取交易行为数据;an obtaining
其中,交易行为指:银行为客户提供金融服务时,柜员通过柜员前端系统发起的一系列系统交易操作的集合,从第一个入口画面开始直到交易结束为止的整个场景下的操作过程。Among them, transaction behavior refers to the collection of a series of system transaction operations initiated by the teller through the front-end system of the teller when the bank provides financial services to customers, from the first entry screen to the end of the transaction.
交易行为数据指:执行交易行为过程中生成的数据。Transaction behavior data refers to the data generated during the execution of transaction behavior.
交易行为数据主要包括:交易节点和交易数据。The transaction behavior data mainly includes: transaction nodes and transaction data.
需要说明的是,本实施例中的交易行为数据可以是T日(交易日)实时交易行为数据,或是T+1日事后交易行为数据。It should be noted that, the transaction behavior data in this embodiment may be real-time transaction behavior data on T day (trading day), or post-event transaction behavior data on T+1 day.
提取单元202,用于对所述交易行为数据进行交易节点特征提取,得到第一特征数据集,所述第一特征数据集包括:各个交易节点的节点名称以及节点执行顺序;The
在实际应用中,可以基于业务规则对当前交易行为数据进行交易节点特征提取,得到特征数据集。In practical applications, transaction node feature extraction can be performed on the current transaction behavior data based on business rules to obtain a feature data set.
以客户办理外币兑换业务为例,柜员需要在银行系统中顺序操作以下交易节点:自动读取身份证信息、发起身份信息联网核查交易、创建临时客户、外币兑换、授权审核以及取出外钞。Taking the customer's foreign currency exchange business as an example, the teller needs to sequentially operate the following transaction nodes in the banking system: automatic reading of ID card information, initiation of online verification transaction of identity information, creation of temporary customers, foreign currency exchange, authorization review, and withdrawal of foreign banknotes.
预测单元203,用于利用构建的风险评估模型对所述第一特征数据集进行风险预测,得到风险预测结果。The
在实际应用中,将特征数据集中的特征数据输入至预先构建的风险评估模型中,得到风险预测结果。In practical applications, the characteristic data in the characteristic data set is input into the pre-built risk assessment model to obtain the risk prediction result.
风险评估模型为:基于不同业务场景下的历史交易节点数据中已打标的交易节点数据训练获得的模型。所述已打标的交易节点数据可以是历史交易节点数据中已有风险标签的交易节点数据,所述风险标签可以根据历史交易发生后用户报案、预先存储的历史交易预测结果等方式确定。所述风险标签包括:风险事件和非风险事件。The risk assessment model is: a model obtained by training based on the marked transaction node data in the historical transaction node data under different business scenarios. The marked transaction node data may be transaction node data with a risk tag in the historical transaction node data, and the risk tag may be determined according to a user report after the historical transaction occurs, a pre-stored historical transaction prediction result, or the like. The risk labels include: risk events and non-risk events.
需要说明的是,在构建风险评估模型时,预先以客户为中心,以场景为单位,整合柜员服务单个客户的所有交易流程,包括:系统间的交易信息、授权审核动作以及刷卡刷操作等等。It should be noted that when building the risk assessment model, the customer is the center in advance, and the scenario is the unit to integrate all the transaction processes of the teller serving a single customer, including: transaction information between systems, authorization review actions, and card swiping operations, etc. .
综上可知,本发明公开的交易风险预测装置,对获取的交易行为数据进行交易节点特征提取,得到第一特征数据集,该第一特征数据集中包括:各个交易节点的节点名称以及节点执行顺序,利用构建的风险评估模型对第一特征数据集进行风险预测,得到风险预测结果。因此,本发明实现了对交易操作中交易节点的风险预测,从而通过提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。To sum up, the transaction risk prediction device disclosed in the present invention performs transaction node feature extraction on the acquired transaction behavior data to obtain a first feature data set, where the first feature data set includes: the node names of each transaction node and the execution sequence of the nodes , using the constructed risk assessment model to perform risk prediction on the first feature data set to obtain the risk prediction result. Therefore, the present invention realizes the risk prediction of the transaction nodes in the transaction operation, thereby reducing the transaction risk brought by the risky transaction behavior by reminding the business personnel to verify and correct the risky transaction behavior.
可以理解,在执行预测单元203之前,首先需要建立风险评估模型。It can be understood that, before executing the
因此,为进一步优化上述实施例,交易风险预测装置还可以包括:Therefore, in order to further optimize the above embodiment, the transaction risk prediction device may further include:
模型构建单元,用于构建所述风险评估模型。A model building unit for building the risk assessment model.
其中,模型构建单元具体用于:Among them, the model building unit is specifically used for:
获取不同业务场景下历史交易节点数据;Obtain historical transaction node data in different business scenarios;
根据不同业务场景下,基于业务规则确定的业务正常操作顺序,对所述历史交易节点数据进行打标,得到所述已打标的交易节点数据;According to different business scenarios, based on the normal business operation sequence determined by business rules, mark the historical transaction node data to obtain the marked transaction node data;
对所述已打标的交易节点数据进行过交易节点特征提取,得到第二特征数据集;Performing transaction node feature extraction on the marked transaction node data to obtain a second feature data set;
对所述第二特征数据集进行训练,得到的风险评估模型。The risk assessment model is obtained by training the second feature data set.
在实际应用中,可以采用任何一种基于大数据的机器学习模型进行模型训练,得到风险评估模型。对风险评估模型进行训练的训练样本为:特征数据集和风险标签,其中,特征数据集为:对历史交易行为数据进行交易节点特征提取得到的。In practical applications, any machine learning model based on big data can be used for model training to obtain a risk assessment model. The training samples for training the risk assessment model are: feature data set and risk label, wherein, the feature data set is obtained by extracting transaction node features from historical transaction behavior data.
训练遵循的原则为:风险评估模型的输出结果可以准确的描述交易行为数据是否存在风险。The training follows the principle that the output of the risk assessment model can accurately describe whether there is a risk in the transaction behavior data.
由于风险交易行为数据存在不断更迭的可能,导致银行出现新的风险事件,而风险评估模型无法对新的风险交易行为进行预测,因此为完善和优化风险评估模型,本发明会不断补充新的风险交易行为数据进行训练,来更新风险评估模型,从而提高风险评估模型预测的准确率。Due to the possibility of continuous change of risk transaction behavior data, new risk events appear in the bank, and the risk assessment model cannot predict the new risk transaction behavior. Therefore, in order to improve and optimize the risk assessment model, the present invention will continuously supplement new risks. The trading behavior data is trained to update the risk assessment model, thereby improving the prediction accuracy of the risk assessment model.
为进一步优化上述实施例,交易风险预测装置还可以包括:To further optimize the above embodiment, the transaction risk prediction device may further include:
优化单元,用于基于所述第一特征数据集和所述风险预测结果,对所述风险评估模型进行优化。An optimization unit, configured to optimize the risk assessment model based on the first feature data set and the risk prediction result.
为减少风险交易行为数据所带来的交易风险,在实际应用中,本发明还可以对风险行为进行事中拦截或事后反馈,以提醒业务人员对风险交易行为进行核实和更正。In order to reduce the transaction risk brought by the risky transaction behavior data, in practical applications, the present invention can also intercept or post-event feedback on the risky behavior, so as to remind business personnel to verify and correct the risky transaction behavior.
具体的,参见图4,本发明一实施例公开的一种交易风险预测装置的结构示意图,包括:Specifically, referring to FIG. 4 , a schematic structural diagram of a transaction risk prediction device disclosed in an embodiment of the present invention includes:
获取单元201,用于获取交易行为数据;an obtaining
其中,交易行为指:银行为客户提供金融服务时,柜员通过柜员前端系统发起的一系列系统交易操作的集合,从第一个入口画面开始直到交易结束为止的整个场景下的操作过程。Among them, transaction behavior refers to the collection of a series of system transaction operations initiated by the teller through the front-end system of the teller when the bank provides financial services to customers, from the first entry screen to the end of the transaction.
交易行为数据指:执行交易行为过程中生成的数据。Transaction behavior data refers to the data generated during the execution of transaction behavior.
交易行为数据主要包括:交易节点和交易数据。The transaction behavior data mainly includes: transaction nodes and transaction data.
提取单元202,用于对所述交易行为数据进行交易节点特征提取,得到第一特征数据集,所述第一特征数据集包括:各个交易节点的节点名称以及节点执行顺序;The
在实际应用中,可以基于业务规则对当前交易行为数据进行交易节点特征提取,得到特征数据集。In practical applications, transaction node feature extraction can be performed on the current transaction behavior data based on business rules to obtain a feature data set.
预测单元203,用于利用构建的风险评估模型对所述第一特征数据集进行风险预测,得到风险预测结果;A
在实际应用中,将特征数据集中的特征数据输入至预先构建的风险评估模型中,得到风险预测结果。In practical applications, the characteristic data in the characteristic data set is input into the pre-built risk assessment model to obtain the risk prediction result.
监控确定单元204,用于根据所述风险预测结果确定交易风险监控方式,所述风险预测结果包括:异常概率或者风险程度,所述交易风险监控方式包括:提示和/或拦截交易。The monitoring and determining
综上可知,本发明公开的交易风险预测装置,对获取的交易行为数据进行交易节点特征提取,得到第一特征数据集,该第一特征数据集中包括:各个交易节点的节点名称以及节点执行顺序,利用构建的风险评估模型对第一特征数据集进行风险预测,得到风险预测结果,并根据风险预测结果确定交易封信监控方式。因此,本发明实现了对交易操作中交易节点的风险预测,并可以对异常概率或者风险程度高的交易行为进行提示和/或拦截交易,从而提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。To sum up, the transaction risk prediction device disclosed in the present invention performs transaction node feature extraction on the acquired transaction behavior data to obtain a first feature data set, where the first feature data set includes: the node names of each transaction node and the execution sequence of the nodes , using the constructed risk assessment model to predict the risk of the first feature data set, obtain the risk prediction result, and determine the transaction letter monitoring method according to the risk prediction result. Therefore, the present invention realizes the risk prediction of the transaction nodes in the transaction operation, and can prompt and/or intercept the transaction behaviors with abnormal probability or high degree of risk, thereby reminding the business personnel to verify and correct the risky transaction behaviors, reducing the risk of The trading risk caused by risky trading behavior.
本发明还提供了一种交易风险预测系统,所述系统可以为单独的交易风险预测处理系统,也可以应用在多种交易分析处理系统中。所述的系统可以为单独的服务器,也可以包括使用本说明书的一个或多个所述方法或一个或多个实施例装置的服务器集群、系统(包括分布式系统)、软件(应用)、实际操作装置、逻辑门电路装置、量子计算机等并结合必要的实施硬件的终端装置。所述交易风险预测处理系统可以包括至少一个处理器以及存储计算机可执行指令的存储器,所述处理器执行所述指令时实现上述任意一个或多个实施例中所述方法的步骤。The present invention also provides a transaction risk prediction system, which can be an independent transaction risk prediction processing system, or can be applied to various transaction analysis and processing systems. The system described may be a single server, or may include server clusters, systems (including distributed systems), software (applications), actual devices using one or more of the methods described in this specification or devices in one or more embodiments. Terminal devices for operating devices, logic gate circuit devices, quantum computers, etc. combined with necessary implementation hardware. The transaction risk prediction processing system may include at least one processor and a memory storing computer-executable instructions, the processor implementing the steps of the method described in any one or more of the above embodiments when the processor executes the instructions.
综上可知,本发明公开的交易风险预测系统,对获取的交易行为数据进行交易节点特征提取,得到第一特征数据集,该第一特征数据集中包括:各个交易节点的节点名称以及节点执行顺序,利用构建的风险评估模型对第一特征数据集进行风险预测,得到风险预测结果。因此,本发明实现了对交易操作中交易节点的风险预测,从而通过提醒业务人员对风险交易行为进行核实和更正,减少风险交易行为所带来的交易风险。To sum up, the transaction risk prediction system disclosed in the present invention performs transaction node feature extraction on the acquired transaction behavior data to obtain a first feature data set, where the first feature data set includes: the node name of each transaction node and the node execution order , and use the constructed risk assessment model to perform risk prediction on the first feature data set to obtain the risk prediction result. Therefore, the present invention realizes the risk prediction of the transaction nodes in the transaction operation, thereby reducing the transaction risk brought by the risky transaction behavior by reminding the business personnel to verify and correct the risky transaction behavior.
最后,还需要说明的是,在本文中,诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。Finally, it should also be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply these entities or there is any such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.
本说明书中各个实施例采用递进的方式描述,每个实施例重点说明的都是与其他实施例的不同之处,各个实施例之间相同相似部分互相参见即可。The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other.
对所公开的实施例的上述说明,使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的,本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下,在其它实施例中实现。因此,本发明将不会被限制于本文所示的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。The above description of the disclosed embodiments enables any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111967801A (en) * | 2020-09-23 | 2020-11-20 | 中国建设银行股份有限公司 | Transaction risk prediction method and device |
CN113052683A (en) * | 2021-04-30 | 2021-06-29 | 中国银行股份有限公司 | Violation prediction method and device |
CN114003969A (en) * | 2020-12-21 | 2022-02-01 | 北京八分量信息科技有限公司 | Risk assessment method based on block chain technology |
CN114490800A (en) * | 2020-11-12 | 2022-05-13 | 浙江网商银行股份有限公司 | Risk identification method and device |
CN114820194A (en) * | 2022-05-31 | 2022-07-29 | 中国工商银行股份有限公司 | Transaction risk assessment method and device for financial product and storage medium |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040260602A1 (en) * | 2003-06-19 | 2004-12-23 | Hitachi, Ltd. | System for business service management and method for evaluating service quality of service provider |
CN109242499A (en) * | 2018-09-19 | 2019-01-18 | 中国银行股份有限公司 | A kind of processing method of transaction risk prediction, apparatus and system |
CN109543984A (en) * | 2018-11-15 | 2019-03-29 | 上海盛付通电子支付服务有限公司 | Risk control method, device, electronic equipment and medium |
CN110097451A (en) * | 2019-04-01 | 2019-08-06 | 中国银联股份有限公司 | A kind of monitoring method and device of banking |
CN110796356A (en) * | 2019-10-22 | 2020-02-14 | 陈华 | Bank counter business monitoring system |
-
2020
- 2020-05-11 CN CN202010392844.0A patent/CN111582878A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20040260602A1 (en) * | 2003-06-19 | 2004-12-23 | Hitachi, Ltd. | System for business service management and method for evaluating service quality of service provider |
CN109242499A (en) * | 2018-09-19 | 2019-01-18 | 中国银行股份有限公司 | A kind of processing method of transaction risk prediction, apparatus and system |
CN109543984A (en) * | 2018-11-15 | 2019-03-29 | 上海盛付通电子支付服务有限公司 | Risk control method, device, electronic equipment and medium |
CN110097451A (en) * | 2019-04-01 | 2019-08-06 | 中国银联股份有限公司 | A kind of monitoring method and device of banking |
CN110796356A (en) * | 2019-10-22 | 2020-02-14 | 陈华 | Bank counter business monitoring system |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111967801A (en) * | 2020-09-23 | 2020-11-20 | 中国建设银行股份有限公司 | Transaction risk prediction method and device |
CN111967801B (en) * | 2020-09-23 | 2024-12-31 | 中国建设银行股份有限公司 | Transaction risk prediction method and device |
CN114490800A (en) * | 2020-11-12 | 2022-05-13 | 浙江网商银行股份有限公司 | Risk identification method and device |
CN114003969A (en) * | 2020-12-21 | 2022-02-01 | 北京八分量信息科技有限公司 | Risk assessment method based on block chain technology |
CN114003969B (en) * | 2020-12-21 | 2024-10-22 | 北京八分量信息科技有限公司 | Risk assessment method based on blockchain technology |
CN113052683A (en) * | 2021-04-30 | 2021-06-29 | 中国银行股份有限公司 | Violation prediction method and device |
CN114820194A (en) * | 2022-05-31 | 2022-07-29 | 中国工商银行股份有限公司 | Transaction risk assessment method and device for financial product and storage medium |
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