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分析银行卡欺诈检测
Plastic Card Fraud Detection using Peer Group Analysis |
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| 课程网址: |
http://videolectures.net/mmdss07_weston_pcf/
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| 主讲教师: |
Dave Weston
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| 开课单位: |
伦敦帝国学院
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| 开课时间: |
2007-12-03
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| 课程语种: |
英语
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| 中文简介: |
欺诈检测描述了尝试尽快识别欺诈活动的方法。从统计方法的角度来看，广泛存在两种欺诈检测方法[1]。这些与我们是否打算检测欺诈活动的已知例子或我们是否打算发现新形式的欺诈行为有关。在前一种情况下，在后一种情况下使用模式匹配技术，部署异常检测技术。对等组分析是一种无监督的监测行为随时间变化的方法[2]，它可用于异常检测[3]。在塑料卡欺诈检测的背景下，为每个帐户构建对等组，其中对等组是行为相似的其他帐户的集合。每个帐户的后续行为是根据其对等组来衡量的。如果某个帐户的行为与其同行群体存在强烈偏差，则该帐户会被标记为异常，并且其最近的交易会被标记为潜在的欺诈行为。这种方法不同于通常的异常检测方法，其中每个帐户的当前行为是根据其自己的过去行为来衡量的。我们将展示如何将对等组分析应用于由时间对齐的多元连续数据组成的时间序列。初始分析包括确定每个时间序列的对等组成员的方法。为此我们需要比较时间序列[4]，我们描述了一种对塑料卡交易数据有用的方法。一旦我们拥有对等组，则分析包括用于跟踪关于其对等组的时间序列的方法。如果时间序列与其对等组之间的分离超过某个外部设定的阈值，则称异常发生。塑料卡交易数据的账户历史既不是时间对齐的，也不是纯粹连续的数据。事务可以在任何时间发生，并且每个事务都与包含大量信息的记录相关联。这使得卡发行者能够区分可能发生的大量可能的交易类型。例如，账户持有人在ATM上检查他们的余额（没有转账的交易的例子）或者购买租赁汽车但在销售点不存在的账户持有人。我们描述了一种时间对齐不同账户交易历史并将一些相关信息转换为连续变量的方法。我们总结了使用同行群体分析对真实信用卡交易数据进行的实验。特别是，我们检查了错过欺诈交易对同伴群体表现的影响。我们描述了一种欺骗对等组欺诈交易的方法。我们使用专为塑料卡欺诈设计的新的绩效衡量方法来展示我们的结果[5]。并非所有帐户都可以被各自的同行小组很好地跟踪，以便有效地识别异常行为。我们描述了一种对等组质量的度量，我们使用它来识别更可能使用对等组成功分析的帐户。
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| 课程简介: |
Fraud detection describesmethods that attempt to identify fraudulent activity as quickly as possible. From a statistical methods perspective there are broadly two approaches to fraud detection [1]. These relate to whether we intend to detect known examples of fraudulent activity or whether we intend to detect novel forms of fraudulent behaviour. In the former case pattern matching techniques are used in the latter case anomaly detection techniques are deployed. Peer group analysis is an unsupervised method for monitoring behaviour over time [2] and it can be used for anomaly detection [3]. In the context of plastic card fraud detection, peer groups are built for each account, where a peer group is collection of other accounts that behave similarly. The subsequent behaviour of each account is measured in relation to its peer group. Should an account’s behaviour deviate strongly from its peer group then the account is flagged as anomalous and its recent transactions are flagged as potential frauds. This approach differs from the usual anomaly detection methods where each account’s current behaviour is measured in relation to its own past behaviour. We show how to apply peer group analysis to times series that consist of timealigned multivariate continuous data. The initial analysis comprises of a method to determine the peer group members for each time series. For this we need to compare time series [4], we describe one method that is useful for plastic card transaction data. Once we have the peer groups, the analysis then comprises of a method for tracking a time series with respect to its peer group. An anomaly is said to have occurred should the separation between the time series and its peer group exceed some externally set threshold. Account histories of plastic card transaction data are neither time aligned nor do they consist of purely continuous data. A transaction can occur at any time and each transaction has associated with it a record containing a large amount of information. This enables the card issuer to distinguish between the large number of possible transaction types that can occur. For example an account holder who checks their balance at an ATM (an example of a transaction where no money is transferred) or an account holder who purchases a rental car but was not present at the point of sale. We describe one way to time align different account transaction histories and to transform some pertinent information into continuous variables. We summarise experiments performed using peer group analysis on real credit card transaction data. In particular we examine the effect that missed fraudulent transactions have on the performance of the peer groups. We describe a method for robustifying against fraudulent transactions contaminating peer groups.We present our results using a new measure of performance that has been designed specifically for plastic card fraud [5]. Not all accounts can be tracked well enough by their respective peer groups to usefully identify anomalous behaviour.We describe a measure of peer group quality which we use to identify accounts that are more likely to be successfully analysed using peer groups.
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| 关 键 词: |
欺诈检测; 统计方法; 异常检测
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-05-31：吴雨秋(课程编辑志愿者)
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| 阅读次数: |
224
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