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

The Impact of Downsizing and Efficiency Measures on Anti-Fraud Resources

The main purpose of this study was to explore the impact of downsizing and efficiency measures on two key elements of operational performance - fraud detection and fraud reporting. Qualitative data were obtained from ethnographic observations of two major multinational insurance companies, which were already examined before the Global Financial Crisis, and subjected to an inter - and intra - business comparative analysis of anti - fraud resources. The paper points out a big discrepancy in opinions on the downsizing effects between junior staff and their supervisors. Whereas the latter present them as enabling the business to deal with suspicious claims more quickly, the former offer a contrastingly different view in which the constantly growing pressure often lea ds to suspicious claims getting approved. By validating the practical implications of a purposefully adapted version of resource - based theory, the paper illustrates the inviability of subjecting anti - fraud resources to the same levels of downsizing and efficiency as other business resources. Although the literature on the general negative impact of downsizing on the broadly - defined operational performance is growing, this is the first major study to examine its impact on insurance anti - fraud processes and illustrate their changes following the Global Financial Crisis

Detecting and Combating Fraudulent Health Insurance Claims Using ANN

This work was funded by the National Nature Science Foundation of China (71774069), 2014 “Six Talent Peaks” Project of Jiangsu Province (2014- JY-004) Abstract While governments and private sector stakeholders are taking steps to improve the access and quality of health care service to its citizenry, a lot of resources are lost every year due to fraudulent health insurance claims. The aim of this paper is to explore a more robust and accurate ways of predicting fraudulent health insurance claims by the use of artificial neural network (ANN). Using the fraud diamond theory (FDT)’s fraud elements as fraud indicators, a fraud prediction model was created to determine whether a claim presented by a subscriber (individual) is fraudulent or non-fraudulent by varying severally the number of epoch, hidden layer number and threshold of the artificial neural network on a 14 input data to obtain an optimal parameter for the model.The model was able to predict accurately 98.98% with an MSE of 0.0086, which outperformed other artificial neural network (ANN) methods used to predict fraudulent health care claims. The incorporation of the capacity indicator of the fraud diamond theory (FDT) makes this model a tool not only for prediction but also pre-empting the occurrence of fraud. This study is the first to adopt the fraud diamond theory’s fraud elements as fraud indicators together with artificial neural network (ANN) in predicting fraudulent health insurance claims. Keywords: health insurance claim, ANN, fraud prediction model, fraud diamond theor

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

Вероятностное моделирование операционных актуарных рисков

Страхові компанії функціонують в умовах наявності невизначеностей різної природи і типу, що призводить до виникнення фінансових ризиків. У зв’язку з цим виникає завдання своєчасного розпізнавання ризиків і створення механізмів управління ними. Це своєю чергою потребує створення математичних моделей для опису ризиків і методик їх застосування. Досліджено джерела виникнення шахрайства і виконано класифікацію ризиків цієї групи. Показано, що для математичного опису таких ризиків можна застосовувати моделі на основі апарату математичної статистики, моделі регресійного типу та на основі нечіткої логіки. Для оцінювання ризику страхового шахрайства в автострахуванні запропоновано модель у формі байєсівської мережі. На основі експертної і статистичної інформації страхової компанії виконано оцінювання структури мережі і запропоновано алгоритм формування висновку за побудованою моделлю з використанням навчальної вибірки. При цьому забезпечується виявлення прихованих взаємозв’язків між вибраними змінними. Побудована модель відображає причинно-наслідкові зв’язки між факторами ризику та втратами страхової компанії. Вона може бути застосована для аналізу стану внутрішнього середовища компанії; аналізу зовнішніх умов, у яких здійснює свою діяльність компанія; для визначення ймовірної причини втрат компанії, пов’язаних з операційними ризиками, а також для прийняття належних управлінських рішень.Insurance companies are functioning in conditions of uncertainties of various types and nature what results in respective financial risks. All these reasons lead to the problem of timely recognition and development of mechanisms for the risks management. To solve the problem appropriate mathematical models are developed to describe the risks, and methodologies proposed for their practical application. The sources of the insurance fraud are detected and respective risk classification is presented. It is shown that to describe mathematically the risks of this class it is appropriate to apply the models based on the mathematical statistics approach, regression type models, and fuzzy logic. For estimation of the risk of actuarial fraud in auto insurance a model is proposed in the form of Bayesian network. The model structure was estimated using expert and statistical information of insurance company with providing a possibility for detecting hidden interactions between selected variables. An algorithm was also developed for probabilistic inference on the network. The model constructed reflects the causal links between the risk factors and the insurance company losses. It can be applied for analysis of internal states of the company; analysis of external conditions characteristic for the company functioning; for determining probable reasons of company losses due to operational risks as well as for making appropriate managerial decisions.Страховые компании функционируют в условиях наличия неопределенностей различной природы и типов, что приводит к возникновению финансовых рисков. В связи с этим возникает задача своевременного распознавания рисков и создания механизмов управления ими. В свою очередь это требует создания математических моделей для описания рисков и методик их применения. Раскрыты источники возникновения мошенничества и приведена классификация рисков этой группы. Показано, что для математического описания таких рисков можно использовать модели на основе аппарата математической статистики, модели регрессионного типа и нечеткую логику. Для оценивания риска мошенничества в автостраховании предложена модель в форме байесовской сети. На основе экспертной и статистической информации страховой компании выполнено оценивание структуры сети и предложен алгоритм формирования вероятностного вывода по этой модели с использованием обучающей выборки. При этом обеспечивается обнаружение скрытых взаимосвязей между выбранными переменными. Построенная модель отражает причинно-следственные связи между факторами риска и потерями страховой компании. Она может быть использована для анализа состояния внутренней среды компании; анализа внешних условий, в которых проводит свою деятельность компания; для определения вероятной причины потерь, связанных с операционными рисками, а также для принятия надлежащих управленческих решений

Engineering the social: The role of shared artifacts

Abstract This paper presents a multidisciplinary approach to engineering socio-technical design. The paper addresses technological design for social interactions that are non-instrumental, and thereby sometimes contradictory or surprising and difficult to model. Through cooperative analysis of cultural probe data and development of agent-oriented software engineering (AOSE) models, ethnographers and software engineers participate in conversations around shared artifacts, which facilitate the transition from data collected in a social environment to a socially oriented requirements analysis for informing socio-technical design. To demonstrate how this transition was made, we present a case study of the process of designing technology to support familial relationships, such as playing, gifting, showing, telling and creating memories. The case study is based on data collected in a cultural probes study that explores the diverse, complex and unpredictable design environment of the home. A multidisciplinary team worked together through a process of conversations around shared artifacts to cooperatively analyze collected data and develop models. These conversations provided the opportunity to view the data from the perspective of alternative disciplines that resulted in the emergence of novel understandings and innovative practice. The artifacts in the process included returned probe items, scrapbooks, videos of interviews, photographs, family biographies and the AOSE requirements models. When shared between the two communities of practice, some of these artifacts played important roles in mediating discussions of mutual influence between ethnographers and software engineers. The shared artifacts acted as both triggers for conversations and information vessels-providing a variety of interpretable objects enabling both sides to articulate their understandings in different ways and to collaboratively negotiate understandings of the collected data. Analyzing the interdisciplinary exchange provided insight into the identification of bridging elements that allowed 'the social' to permeate the processes of analysis, requirements elicitation and design.