特征树阀值检测算法应对电信欺诈

电信网络日益复杂,这增加了电信营运的难度,并且大额欺诈和恶意欠费的状况使电信运营收入存在较大的风险。本文在数据挖掘技术、基于聚类的层次分析算法等理论基础上,采用了欺诈特征树阀值检测算法来应对电信欺诈,防范电信运营收入的流失。该算法将用户的数据特征项构建成欺诈特征树,采用关系数据模式来组织用户的欺诈特征项,并设定结点阀值作为检测判断的依据,依照用户最后的欺诈度值判断用户是否欺诈。算法简单高效,系统占用较少的内存并获得了较高的准确率。     

电信欺诈综合分析与系统架构研究

多年来,无论在西方发达国家还是在发展中国家,电信欺诈都正变得越来越严重与普遍。越来越多的电信运营商与欺诈研究专家认识到这个问题的严峻性与长期性,并分别在理论与工程等方面研究欺诈侦测、分析与预防的策略与措施。然而,从总体上看,上述工作实际效果不尽人意。本文从系统科学角度,按照综合集成的方法论,探讨进行电信欺诈智能分析与控制的综合策略。文章首先从多维视角分析电信欺诈的复杂性与演化性,并进而提出一磁智能分析与监控的综合策略:包括混合智能分析与监控的一整套流程,从五个模型与四层分析进行这类系统的分析与设计等,并进而基于上述策略提出欺诈智能分析与监控的闭环商业智能系统的框架。最后以移动欺诈客户为例,讨论了欠费客户的分类分析,介绍了一个基于上述策略的智能分析与监控系统原型。     

集成学习在互联网借贷反欺诈中的应用研究

本文介绍了基于集成学习的互联网借贷反欺诈方法的研究。互联网借贷反欺诈是互联网金融领域中的一个重要研究方向,传统的互联网借贷反欺诈算法大多基于规则。本文主要使用了多种机器学习算法训练反欺诈模型,并结合模型原理与场景特点分析了各模型性能上的差异,给出一种适合借贷反欺诈问题的交叉特征加权的模型集成策略。     

CRM在电信行业中的应用与技术

本文首先分析了电信CRM的含义及其实施的必要性,然后结合当前电信行业的经营状况,讨论了分析型CRM与BOSS系统的整合问题,并进一步介绍了应用于该领域的数据仓库和数据挖掘技术。     

数据挖掘在防电信客户欺诈模型中的应用

首先分析了当前电信客户欺诈背景,提出把数据挖掘技术应用于电信客户欺诈系统中的构想。防范电信欺诈从操作角度来说就是对欺诈人群的行为进行控制,利用数据挖掘等先进技术对电信客户的行为进行分析。文章着重阐述了采用数据挖掘、朴素贝叶斯分类等技术建模以及验证过程。性能测试表明:将先进的数据挖掘贝叶斯分类技术应用于实际的电信客户欺诈系统中,具有一定的市场价值。该模型能挖掘出潜在的风险行为,识别出客户的欺诈行为,从而解决诸多规模小、分散性大的电信欺诈行为。     

数据仓库技术及其在电信计费领域应用的探讨

该文首先介绍了数据仓库的基本概念,然后阐述了数据仓库在电信计费领域的主要应用,最后对电信计费领域建立数据仓库的方法及步骤进行了探讨。     

工作流技术在电信数据仓库系统中的应用

随着电信市场的逐步放开,各运营商近乎默契的同时启动数据仓库系统的建设。在数据仓库系统建设中,出现了很多问题。本文以广东电信数据仓库的建设为背景,分析了建设中存在的问题,提出了基于工作流技术的解决方案,并以应用实例的设计进行了介绍和说明,探讨了工作流技术在数据仓库系统中的应用效果。     

基于APP恶意程序检测——反欺诈功能设计

针对现阶段网络诈骗事件频发的社会现状，本文研究并设计了一套移动互联网反欺诈系统。系统所应用的技术方面，选用C语言进行底层开发，使用MSQL配合PHP语言进行Web前端开发，来完成基于APP恶意程序检测系统——反欺诈子系统的设计与实现，并将该系统成功地应用在移动互联网上。本文研究与设计的反欺诈系统主要强调其覆盖性、准确性和实时性的防御体系。通过部署该套反欺诈系统后，能够有效地降低不法分子对手机用户实施的诈骗，并可以详细地记录恶意欺诈行为。采用移动互联网反欺诈系统，大幅度地降低不法分子诈骗成功率，保护个人财产、信息安全，为现阶段移动互联网诈骗问题提供了良好的解决方案。     

基于密集子图的银行电信诈骗检测方法

目前银行对电信诈骗的标记数据积累少，人工标记数据的代价大，导致电信诈骗检测的有监督学习方法可使用的标记数据不足。针对这个问题，提出一种基于密集子图的无监督学习方法用于电信诈骗的检测。首先，通过在账户-资源（IP地址和MAC地址统称为资源）网络搜索可疑度较高的子图来识别欺诈账户；然后，设计了一种符合电信诈骗特性的子图可疑度量；最后，提出一种磁盘驻留、线性内存消耗且有理论保障的可疑子图搜索算法。在两组模拟数据集上，所提方法的F1-score分别达到0.921和0.861，高于CrossSpot、fBox和EvilCohort算法，与M-Zoom算法的0.899和0.898相近，但是所提方法的平均运行时间和内存消耗峰值均小于M-Zoom算法；在真实数据集上，所提方法的F1-score达到0.550，高于fBox和EvilCohort算法，与M-Zoom算法的0.529相近。实验结果表明，所提方法能较好地应用于现阶段的银行反电信诈骗业务，且非常适合于实际应用中的大规模数据集。     

电信欺诈行为的聚类研究

针对海量电信数据的聚类问题,利用粗集中的知识简化方法,减少属性的数量,提取主要的特征属性,并结合性能优良的模糊Kohonen聚类网络,提出了一种新的电信欺诈行为的检测模型,采用Microsoft SQL2005和VC＋＋6.0技术,利用电信运营商提供的真实数据对该模型进行验证,实验结果表明,基于粗集神经网络方法提出的模型快速有效且具有较高的准确率。     

数据仓库和数据挖掘技术在ERP中的应用

数据仓库和数据挖掘是近几年迅速发展起来的技术,主要用于构建企业的决策支持系统。文章根据数据仓库和数据挖掘技术的这个特点,并针对传统ERP系统在决策方面的不足,提出把数据仓库和数据挖掘应用到ERP中去,以数据仓库作为企业底层的数据源,再配合以各种数据挖掘技术,以提高ERP中的决策支持功能。     

A survey of signature based methods for financial fraud detection

Fraud detection mechanisms support the successful identification of fraudulent system transactions performed through security flaws within deployed technology frameworks while maintaining optimal levels of service delivery and a minimal numbers of false alarms. Knowledge discovery techniques have been widely applied in fraud detection for data analysis and training of supervised learning algorithms to support the extraction of fraudulent account behaviour within static data sets. Escalating costs associated with fraud however have continued to drive the migration towards increasingly proactive methods of fraud detection, to support the real-time screening of transactional data and detection of ambiguous user behaviour prior to transaction completion. This shift in data processing from post to pre data storage significantly reduces the available time within which to evaluate newly arriving system requests and produce an accurate fraud decision, demanding increasingly robust and intelligent user profiling technologies to support advanced fraud detection. This paper provides a comprehensive survey of existing research into account signatures, an innovative account profiling technology which maintains a statistical representation of normal account usage for rapid recalculation in real-time. Fraud detection architectures, processing models and applications to date are critically examined and evaluated with respect to their proactive capabilities for detection of fraud within streaming financial data. Discussion is also presented on challenges which remain within the proactive profiling of account behaviour and future research directions within the signature domain.     

From conceptual models to schemata: An object-process-based data warehouse construction method

Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system's conceptual model, and familiarity with data warehouse technologies. The suitability of current data warehouse modeling methods for large-scale systems is questionable, as they require multiple manual actions to discover measures and relevant dimensional entities and they tend to disregard the system's dynamic aspects. We present an Object-process-based Data Warehouse Construction (ODWC) method that overcomes these limitations of existing methods by utilizing the operational system conceptual model to construct a corresponding data warehouse schema. We specify the ODWC method, apply it on a case study, evaluate it, and compare it to existing methods.     

Learned lessons in credit card fraud detection from a practitioner perspective

Billions of dollars of loss are caused every year due to fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to non-stationary distribution of the data, highly imbalanced classes distributions and continuous streams of transactions.At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about which is the best strategy to deal with them.In this paper we provide some answers from the practitioner’s perspective by focusing on three crucial issues: unbalancedness, non-stationarity and assessment. The analysis is made possible by a real credit card dataset provided by our industrial partner.     

Designing an expert system for fraud detection in private telecommunications networks

Telecommunications fraud not only burdens telecom provider’s accountings but burdens individual users as well. The latter are particularly affected in the case of superimposed fraud where the fraudster uses a legitimate user’s account in parallel with the user. These cases are usually identified after user complaints for excess billing. However, inside the network of a large firm or organization, superimposed fraud may go undetected for some time. The present paper deals with the detection of fraudulent telecom activity inside large organizations’ premises. Focus is given on superimposed fraud detection. The problem is attacked via the construction of an expert system which incorporates both the network administrator’s expert knowledge and knowledge derived from the application of data mining techniques on real world data.     

以E-R模型为基础构造数据仓库的概念模型

建立数据模型是构造数据仓库的重要步骤之一,多维数据模型是数据仓库设计中广泛采用的概念模型。该文提出了利用操作型数据库系统中已存在的E-R模型,把E-R模型转换变形为属性树,从而建立数据仓库的多维数据模型的方法。使用这一方法可以对已存在的数据库系统的信息资源进行二次利用,有助于对现存信息系统的深入理解和认识,减少必不可少的信息系统调研所耗费的时间,加速构造数据仓库的进程。     

A survey on fraud and service misuse in voice over IP (VoIP) networks

The migration from circuit-switched networks to packet-switched networks necessitates the investigation of related issues such as service delivery, QoS, security, and service fraud and misuse. The latter can be seen as a combination of accounting and security aspects. In traditional telecommunication networks, fraud accounts for annual losses at an average of 3%–5% of the operators’ revenue and still increasing at a rate of more than 10% yearly. It is also expected that in VoIP networks, the situation will be worse due to the lack of strong built-in security mechanisms, and the use of open standards. This paper discusses the fraud problem in VoIP networks and evaluates the related available solutions.     

Identifying the signs of fraudulent accounts using data mining techniques

In today’s technological society there are various new means to commit fraud due to the advancement of media and communication networks. One typical fraud is the ATM phone scams. The commonality of ATM phone scams is basically to attract victims to use financial institutions or ATMs to transfer their money into fraudulent accounts. Regardless of the types of fraud used, fraudsters can only collect victims’ money through fraudulent accounts. Therefore, it is very important to identify the signs of such fraudulent accounts and to detect fraudulent accounts based on these signs, in order to reduce victims’ losses. This study applied Bayesian Classification and Association Rule to identify the signs of fraudulent accounts and the patterns of fraudulent transactions. Detection rules were developed based on the identified signs and applied to the design of a fraudulent account detection system. Empirical verification supported that this fraudulent account detection system can successfully identify fraudulent accounts in early stages and is able to provide reference for financial institutions.     

一种抗欺诈的C2C卖方信誉计算模型研究

针对C2C信誉模型中小额商品信誉炒作、信誉共谋、信誉诋毁等问题,引入交易价格、反馈可信度、共谋因子等参数,提出一种买方视角下抗欺诈的卖方成员信誉计算模型(C2CRep)。实验中通过收集网络交易数据,定义可疑欺诈的基本特征对数据进行抽取,并设定信誉计算误差(RCE)指标检验由欺诈行为带来的信誉值在社区信誉所占比例来检验模型的应用效果。结果表明,C2CRep在3类不同比例的欺诈行为中,RCE明显低于SPORAS与淘宝信誉模型,且RCE值在3类实验中都低于15%,抗欺诈性强。     

Classification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost

Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed distribution of class in the dataset used for fraud detection because of sparse fraud-related data. The objective of this paper is to examine whether classification cost is affected both by the cost-sensitive approach and by skewed distribution of class. To that end, we compare the classification cost incurred by a traditional cost-insensitive classification approach and two cost-sensitive classification approaches, Cost-Sensitive Classifier (CSC) and MetaCost. Experiments were conducted with a credit loan dataset from a major financial institution in Korea, while varying the distribution of class in the dataset and the number of input variables. The experiments showed that the lowest classification cost was incurred when the MetaCost approach was used and when non-fraud data and fraud data were balanced. In addition, the dataset that includes all delinquency variables was shown to be most effective on reducing the classification cost.