Fraud Prevention and Detection

Fraud has been a million-dollar business which is rapidly increasing at global level. Most organisations are victims of fraud which is committed in unlimited multifarious forms. Major threats have been prompted by new information systems, reengineering and reorganisation which weaken the existing controls. The paper addresses the anti-fraud strategy and fraud prevention and detection techniques. Keywords: fraud prevention; fraud detection; anti-fraud strategy; perp-wal

A framework for internal fraud risk reduction at it integrating business processes : the IFR² framework

Fraud is a million dollar business and it is increasing every year. Both internal and external fraud present a substantial cost to our economy worldwide. A review of the academic literature learns that the academic community only addresses external fraud and how to detect this type of fraud. Little or no effort to our knowledge has been put in investigating how to prevent ánd to detect internal fraud, which we call ‘internal fraud risk reduction’. Taking together the urge for research in internal fraud and the lack of it in academic literature, research to reduce internal fraud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR² framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.El fraude es un negocio millonario y está aumentando cada año. Tanto el fraude interno como el externo presentan un coste considerable para nuestra economía en todo el mundo. Este artículo sobre la literatura académica enseña que la comunidad académica solo se dirige al fraude externo, y cómo se detecta este tipo de fraude. Que sepamos, se ha hecho poco o ningún esfuerzo en investigar cómo evitar y detectar el fraude interno, al que llamamos ‘reducción del riesgo de fraude interno’. Teniendo en cuenta la urgencia de investigar el fraude interno, y la ausencia de ello en la literatura académica, la investigación para reducir este tipo de fraude es esencial. Este tema puede ser aún investigado con mayor profundidad solo después de tener un marco, en el que implementar investigación empírica. En este artículo, presentamos el marco IFR, deducido tanto de la literatura académica como de las prácticas empresariales actuales, donde el foco del marco sugiere usar un enfoque de extracción de datos

A Comprehensive Survey of Data Mining-based Fraud Detection Research

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.Comment: 14 page

Prescription Fraud detection via data mining : a methodology proposal

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- -Bilkent University, 2009.Includes bibliographical references leaves 61-69Fraud is the illegitimate act of violating regulations in order to gain personal profit. These kinds of violations are seen in many important areas including, healthcare, computer networks, credit card transactions and communications. Every year health care fraud causes considerable amount of losses to Social Security Agencies and Insurance Companies in many countries including Turkey and USA. This kind of crime is often seem victimless by the committers, nonetheless the fraudulent chain between pharmaceutical companies, health care providers, patients and pharmacies not only damage the health care system with the financial burden but also greatly hinders the health care system to provide legitimate patients with quality health care. One of the biggest issues related with health care fraud is the prescription fraud. This thesis aims to identify a data mining methodology in order to detect fraudulent prescriptions in a large prescription database, which is a task traditionally conducted by human experts. For this purpose, we have developed a customized data-mining model for the prescription fraud detection. We employ data mining methodologies for assigning a risk score to prescriptions regarding Prescribed Medicament- Diagnosis consistency, Prescribed Medicaments’ consistency within a prescription, Prescribed Medicament- Age and Sex consistency and Diagnosis- Cost consistency. Our proposed model has been tested on real world data. The results we obtained from our experimentations reveal that the proposed model works considerably well for the prescription fraud detection problem with a 77.4% true positive rate. We conclude that incorporating such a system in Social Security Agencies would radically decrease human-expert auditing costs and efficiency.Aral, Karca DuruM.S

Feature Space Modeling for Accurate and Efficient Learning From Non-Stationary Data

A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and heterogeneity, can pose another challenge for effective computational learning. The problem is to enable accurate and efficient learning from non-stationary data in a continuous fashion over time while facing and managing the critical challenges of time, memory, concept change, and complexity simultaneously. Feature space modeling is one of the most effective solutions to address this problem. For non-stationary data, selecting relevant features is even more critical than stationary data due to the reduction of feature dimension which can ensure the best use a computational resource to produce higher accuracy and efficiency by data mining algorithms. In this dissertation, we investigated a variety of feature space modeling techniques to improve the overall performance of data mining algorithms. In particular, we built Relief based feature sub selection method in combination with data complexity iv analysis to improve the classification performance using ovarian cancer image data collected in a non-stationary batch mode. We also collected time series health sensor data in a streaming environment and deployed feature space transformation using Singular Value Decomposition (SVD). This led to reduced dimensionality of feature space resulting in better accuracy and efficiency produced by Density Ration Estimation Method in identifying potential change points in data over time. We have also built an unsupervised feature space modeling using matrix factorization and Lasso Regression which was successfully deployed in conjugate with Relative Density Ratio Estimation to address the botnet attacks in a non-stationary environment. Relief based feature model improved 16% accuracy of Fuzzy Forest classifier. For change detection framework, we observed 9% improvement in accuracy for PCA feature transformation. Due to the unsupervised feature selection model, for 2% and 5% malicious traffic ratio, the proposed botnet detection framework exhibited average 20% better accuracy than One Class Support Vector Machine (OSVM) and average 25% better accuracy than Autoencoder. All these results successfully demonstrate the effectives of these feature space models. The fundamental theme that repeats itself in this dissertation is about modeling efficient feature space to improve both accuracy and efficiency of selected data mining models. Every contribution in this dissertation has been subsequently and successfully employed to capitalize on those advantages to solve real-world problems. Our work bridges the concepts from multiple disciplines ineffective and surprising ways, leading to new insights, new frameworks, and ultimately to a cross-production of diverse fields like mathematics, statistics, and data mining

Exploring Online Fraudsters’ Decision-Making Processes

A growing body of evidence suggests the situational context influences the social engineer (SE) characteristics and tactics offenders (i.e., fraudsters) deploy during the development of an online fraud event. Several attempts have been made to examine online the macro-social development of an online fraud event. Nevertheless, macro-level social examinations have been largely unsuccessful in combating online fraud because offenders and victims, including offender victims, are not computers; therefore, offenders’ interactions, motives, and tactics are very difficult to surmise. To address online fraud, three independent studies were conducted to explore what is known about online fraudsters and investigate what is not accounted. Specifically, a scoping review of offenders SE characteristics and tactics is conducted. In addition, two empirical investigations examining linguistic cues used by offender and offender victims are conducted. for that present day literature or governmental reports do not address. Together, these studies examine the influence of the situational context on offenders’ decision-making process, like their SE characteristics and tactics. The results and limitations associated with each study, along with recommendations for further research are discussed

Bankacılık işlemlerinde konum destekli sahtekarlık önleme sistemi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Sahtekârlık (fraud) işlemlerinin tespiti ulusal ve uluslararası ekonomiler için oldukça önemli bir görev haline gelmiştir. Bankalar ve diğer finansal kuruluşların gerçekleştirdikleri işlemlerin güvenilirliğini sağlaması başta ülke ekonomisi olmak üzere, finansal kuruluşun da itibar ve kârlılığını etkileyen temel faktörlerden birisidir. Sahtekârlık işlemlerinin tespit edilebilmesi ve önlenmesi amacıyla kamu ve özel finans kuruluşlarında bu kontrolleri yapmaktan sorumlu birimler oluşturulmuştur. Ancak sahtekârlık işlemlerini gerçekleştirmeye çalışan kişilerin, yakalanmamak amacıyla sürekli yöntem değiştirmeleri, bu tip işlemlerin tespit edilmesini zorlaştırmaktadır. Bu işlemlerin tespiti, işlem hacimlerinin yoğunluğu da dikkate alındığında teknoloji desteğini zorunlu kılmaktadır. Sahtekârlık işlemlerinin tespiti için geliştirilmiş uygulamalar içerisinde özellikle kural tabanlı sistemlerin yaygınlığı dikkate değerdir. Bu sistemler basit ve bileşik kurallar kullanan, doğrulanmış sahtekârlık veritabanları ve diğer önemli veri setlerinde karşılaştırma yapan ileri teknoloji veri eşleme sistemleri olabileceği gibi şüpheli davranışları tespit edebilen ve bu bilgiyi doğru kanala yönlendiren veritabanları gibi basit sistemler de olabilmektedir. Bununla birlikte, sahtekârlık işlemlerinin tespitinde işlem konumlarının (lokasyonlarının) dikkate alınması üzerine geliştirilmiş bir modele rastlanmamıştır. Bu tez çalışmasında hedeflenen bankacılık ürün ve hizmetlerine yönelik sahtekârlık işlemlerinin tespiti ve önlenmesi için finansal işlemlerin konum bilgisinin kullanılması ile daha iyi sonuçlar elde edilip edilemeyeceğinin incelenmesidir. Çalışma kapsamında coğrafi bilgi sistemlerinin yardımıyla ve veri madenciliği modelleri kullanılarak, konum ve zaman bilgisinin dahil edildiği senaryolar keşfedilmiştir. Anahtar kelimeler: Sahtekârlık (fraud) işlemleri, veri madenciliği, coğrafi bilgi sistemleri, lokasyon zekâsıFraud detection procedures for national and international economies have become quite important tasks. Ensuring the security of transactions carried out by banks and other financial institutions is one of the major factors affecting the reputation and profitability of such organizations. Public and private financial institutions establish organizational bodies responsible for carrying out controls for detecting and preventing fraudulent transactions. However, since people who perform fradulent transactions change their methods constantly in order not to get caught up, it gets more difficult to identify and detect this type of transactions. Detecting this type of transactions makes the support of technology compulsory, considering high volume and intensity of transactions. Among the applications that has been developed for the detection of fraudulent transactions, the prevalence of the rule-based systems are particularly noteworthy. As these systems may use of simple and compound rules, advanced data mapping technologies that make comparison in validated fraud databases, and other important databases mapping systems, they may be simple database systems that can detect suspicious behavior and directs this information to the right. However, we have not come across any model that takes into account of transaction location. The aim of this thesis study is to study the worth of location information of financial transactions for detecting the fraudulent transactions. The scope of work is to discover scenarios to detect fraudulent transactions by the support of geographic information systems with location, and time information and the help of models built by using data mining. Keywords: Fraudulent Transactions, Data Mining, Geographical Information Systems, Location Intelligenc