Data Mining Techniques for Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models. Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matri

Data Mining Techniques in Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models

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

Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Utilización de metodologías de Inteligencia Artificial y sus aplicaciones en El Salvador

El presente artículo intenta dar una pequeña perspectiva de cómo el uso de las metodologías basadas en Inteligencia Artificial (IA) podrían contribuir en la solución de problemas reales del país: como la eficiencia y eficacia en consultas médicas del Instituto del Seguro Social Salvadoreño (ISSS), toma de decisiones políticas importantes, resolución de juicios legales, evasión de impuestos, aprobación de créditos, optimización de recursos, etc. El documento describe brevemente diferentes técnicas de Inteligencia Artificial (IA) tales como Sistemas Expertos (SE), Razonamiento Basados en Casos (RBC), Redes Neuronales Artificiales (RNA) y Algoritmos Genéticos (AG) entre otras, y menciona en forma sintetizada algunas áreas críticas en las que podrían aplicarse en el país con éxito. El objetivo principal de este artículo es dar a conocer otras alternativas hasta ahora desconocidas por las instituciones del Estado para la resolución de problemas nacionales importantes

Web usage mining for click fraud detection

Estágio realizado na AuditMark e orientado pelo Eng.º Pedro FortunaTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

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

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