Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change.

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These “false alarms” delay the detection of fraudulent transactions. Analysis of 11 months of credit card transaction data from a major Canadian bank was conducted to determine savings improvements that can be achieved by identifying truly fraudulent transactions. A meta-classifier model was used in this research. This model consists of 3 base classifiers constructed using the k-nearest neighbour, decision tree, and naïve Bayesian algorithms. The naïve Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from this research show that when a meta-classifier was deployed in series with the Bank’s existing fraud detection algorithm a 24% to 34% performance improvement was achieved resulting in $1.8 to $2.6 million cost savings per year.

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These “false alarms” delay the detection of fraudulent transactions. Analysis of 11 months of credit card transaction data from a major Canadian bank was conducted to determine savings improvements that can be achieved by identifying truly fraudulent transactions. A meta-classifier model was used in this research. This model consists of 3 base classifiers constructed using the k-nearest neighbour, decision tree, and naïve Bayesian algorithms. The naïve Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from this research show that when a meta-classifier was deployed in series with the Bank’s existing fraud detection algorithm a 24% to 34% performance improvement was achieved resulting in $1.8 to $2.6 million cost savings per year.

Authenticity issues and fraudulent practices regarding animal products are affecting consumer confidence. Verifying the description, composition, processing or origin of foods can be challenging. To explore British and Kurdish consumers’ perceptions of kebab meat products, focus groups and questionnaire surveys were applied. About 40% of participants in the UK tend to purchase fewer processed meats after the European horsemeat scandal. Issues raised by participants indicated their concerns about the declaration of species, meat content, and other ingredients incorporated in kebab and other meat products. Lack of consumer trust has been linked to authenticity issues. Reactions towards the addition of fat-replacing inulin were positive by more than half of respondents. A further study aimed to investigate the effect of commercial inulin (CI) and Jerusalem artichoke (JA) tubers as fat replacers on the eating quality and overall acceptability of kebabs. Inulin flour prepared from JA by a simple protocol presented advantages with about 10% higher cooking yield and overall acceptability when compared with CI. Levels of inulin as low as 0.5% were detected in meat products using enzymatic assay, which could be relevant to detect additives and enforce labelling requirements. The authenticity (origin and species) was investigated in fish samples from commercial markets in Erbil, Kurdistan Region of Iraq (KRI). The declared fish species was checked using DNA barcoding with Cytochrome b region. A 10 % rate of mislabelling occurred only for wild common carp (Cyprinus carpio), with 9 out of 12 discovered to be the related species goldfish (Carassius auratus), which was deemed to be accidental rather than deliberate fraud. Such occurrences were from street markets and fishmongers, while none were from supermarkets. Wild and farmed common carp samples were not discriminated by DNA barcoding. Further fingerprinting using compositional profile and nearinfrared spectroscopy (NIRS) together with chemometric analysis aimed to predict composition and discriminate between wild and farmed common carp and species identity. NIRS-predictions of composition and some macrominerals of fish have a strong correlation with the references. NIRS with chemometric analysis is promising, but were not satisfactorily accurate for micro-minerals. Even with no clear solution from principal component analysis (PCA), NIRS-PCA may contribute to discriminating sample groups, but not for authentication when used alone. Having reliable techniques for authentication of food of animal origin may discourage deliberate replacement in retail, wholesale and international trade, and may contribute to reductions in food mislabelling, therefore protecting consumers from fraudulent practices.

Neste trabalho é analisada a aplicação da técnica de Sistemas Imunológicos Artificiais (SIA) a um problema do mundo real: como predizer fraudes e furtos de energia elétrica. Vários trabalhos tem mostrado que épossível detectar padrões de dados anormais a partir dos dados de consumidores de energia elétrica e descobrir problemas como fraude e furto. Sistemas Imunológicos Artificiais é um ramo recente da Inteligência Computacional e tem diversas possíveis aplicações, sendo uma delas o reconhecimento de padrões. Mais de um algoritmo pode ser empregado para criar um SIA; no escopo deste trabalho será empregado o algoritmo Clonalg. A eficácia deste algoritmo é medida e comparada com a de outros métodos de classificação. A amostra de dados usada para validar este trabalho foi fornecida por uma companhia de energia elétrica. Os dados fornecidos foram selecionados e transformados com o objetivo de eliminar redundância e normalizar valores. / In this paper, we analyze the application of an Artificial Immune System (AIS) to a real world problem: how to predict electricity fraud and theft. Various works have explained that it is possible to detect abnormal data patterns from electricity consumers and discover problems like fraud and theft. Artificial Immune Systems is a recent branch of Computational Intelligence and has several possible applications, one of which is pattern recognition. More than one algorithm can be employed to create an AIS; we selected the Clonalg algorithm for our analysis. The efficiency of this algorithm is measured and compared with that of other classifier methods. The data sample used to validate this work was provided by an electrical energy company. The provided data were selected and transformed with the aim of eliminating redundant data and to normalize values.

This thesis deals with a very important and serious topic, which is creative accounting and its relation to accounting frauds. Most of the society finds creative accounting undesirable because it was mentioned in relation to bankruptcy of big American a European companies after the revelation of their financial manipulations. The thesis analyses accounting frauds focusing on fraud detection, prevention, typical fraudster and criteria of the fraudelent actions according to the fraud triangle.

Neste trabalho é analisada a aplicação da técnica de Sistemas Imunológicos Artificiais (SIA) a um problema do mundo real: como predizer fraudes e furtos de energia elétrica. Vários trabalhos tem mostrado que épossível detectar padrões de dados anormais a partir dos dados de consumidores de energia elétrica e descobrir problemas como fraude e furto. Sistemas Imunológicos Artificiais é um ramo recente da Inteligência Computacional e tem diversas possíveis aplicações, sendo uma delas o reconhecimento de padrões. Mais de um algoritmo pode ser empregado para criar um SIA; no escopo deste trabalho será empregado o algoritmo Clonalg. A eficácia deste algoritmo é medida e comparada com a de outros métodos de classificação. A amostra de dados usada para validar este trabalho foi fornecida por uma companhia de energia elétrica. Os dados fornecidos foram selecionados e transformados com o objetivo de eliminar redundância e normalizar valores. / In this paper, we analyze the application of an Artificial Immune System (AIS) to a real world problem: how to predict electricity fraud and theft. Various works have explained that it is possible to detect abnormal data patterns from electricity consumers and discover problems like fraud and theft. Artificial Immune Systems is a recent branch of Computational Intelligence and has several possible applications, one of which is pattern recognition. More than one algorithm can be employed to create an AIS; we selected the Clonalg algorithm for our analysis. The efficiency of this algorithm is measured and compared with that of other classifier methods. The data sample used to validate this work was provided by an electrical energy company. The provided data were selected and transformed with the aim of eliminating redundant data and to normalize values.

Neste trabalho é analisada a aplicação da técnica de Sistemas Imunológicos Artificiais (SIA) a um problema do mundo real: como predizer fraudes e furtos de energia elétrica. Vários trabalhos tem mostrado que épossível detectar padrões de dados anormais a partir dos dados de consumidores de energia elétrica e descobrir problemas como fraude e furto. Sistemas Imunológicos Artificiais é um ramo recente da Inteligência Computacional e tem diversas possíveis aplicações, sendo uma delas o reconhecimento de padrões. Mais de um algoritmo pode ser empregado para criar um SIA; no escopo deste trabalho será empregado o algoritmo Clonalg. A eficácia deste algoritmo é medida e comparada com a de outros métodos de classificação. A amostra de dados usada para validar este trabalho foi fornecida por uma companhia de energia elétrica. Os dados fornecidos foram selecionados e transformados com o objetivo de eliminar redundância e normalizar valores. / In this paper, we analyze the application of an Artificial Immune System (AIS) to a real world problem: how to predict electricity fraud and theft. Various works have explained that it is possible to detect abnormal data patterns from electricity consumers and discover problems like fraud and theft. Artificial Immune Systems is a recent branch of Computational Intelligence and has several possible applications, one of which is pattern recognition. More than one algorithm can be employed to create an AIS; we selected the Clonalg algorithm for our analysis. The efficiency of this algorithm is measured and compared with that of other classifier methods. The data sample used to validate this work was provided by an electrical energy company. The provided data were selected and transformed with the aim of eliminating redundant data and to normalize values.

Made available in DSpace on 2016-03-15T19:37:51Z (GMT). No. of bitstreams: 1 Luis Alexandre Rodrigues.pdf: 1364668 bytes, checksum: ac6c4273730fb6f75f7a0ceead7e4c1f (MD5) Previous issue date: 2014-08-05 / Universidade Presbiteriana Mackenzie / This work presents a process to detect suspected cases of fraud at automobile claims dataset, which is evaluated the economic created by it. Because of a detection process presenting misclassific ation, it is necessary to evaluate the financial economy made by the process not only its accuracy in detecting suspected cases of fraud. This process uses a combination of classifiers, with C4.5 Decision Tree, Naive Bayes and Support Vector Machine, const ructed by samples of the data set with automobile claims. This way, the process defined by this work can obtain the balance between the accuracy of classification and the financial economy. / Este trabalho apresenta um processo para detectar casos suspeitos de fraude em conjunto de dados com sinistros de automóvel, em que é avaliada a economia financeira gerada por ele. Devido ao fato de um processo de detecção apresentar erros de classificação, é necessário avaliar a economia financeira apresentada pelo processo e não somente a sua precisão na detecção de casos suspeitos de fraude. Este processo utiliza a combinação de classificadores, sendo Árvore de Decisão C4.5, Naive Bayes e Support Vector Machine, construídos por amostras do conjunto de dados com sinistros de automóvel. Desta forma, o processo definido por este trabalho pode obter o equilíbrio entre a precisão da classificação e a economia financeira.

Segundo relatório da auditoria KPMG, 69% das empresas admitiram ser vítimas de algum tipo de fraude. Em 2010, no setor bancário foram perdidos aproximadamente R$ 1,5 bilhões devido às fraudes financeiras cometidas em clientes considerando apenas as fraudes documentais e as perdas com fraudes bancárias eletrônicas superaram os 900 milhões neste mesmo ano. Os tipos de fraudes cometidas foram diversos, dentre eles a fraude durante a abertura de contas, cheques clonados, falsificação de documentos, alterações de códigos de barras e clonagem de cartões. A fraude é um problema frequente nas organizações e bastante discutido no mercado, porém verificou-se a existência de uma lacuna teórica quando se trata de gestão da fraude externa. O objetivo do trabalho foi a estruturação de um quadro conceitual para a Gestão da Fraude Financeira e a sua comparação com a prática.Este é um estudo qualitativo exploratório e foi realizado por meio da análise baseada na Teoria Fundamentada definindo categorias a partir da literatura disponível e a sua comparação com entrevistas feitas em um banco de varejo brasileiro e uma associação de instituições financeiras, além dos artigos jornalísticos. Com relação à utilização dos resultados esta é uma pesquisa aplicada já que seu resultado pode, além de contribuir para a discussão teórica, ser aplicada em qualquer organização interessada em gerir a fraude financeira. Os resultados da elaboração do quadro conceitual mostram que a gestão da fraude financeira externa tem quatro fases: a Contínua, a Prevenção, Detecção e a Reação e as categorias definidas estão inseridas nelas. Quanto à comparação da teoria com a prática, nem todos os aspectos verificados na literatura puderam ser encontrados nos relatos das entrevistas e nos artigos jornalísticos analisados. / According to KPMG audit report, 69% of companies admitted being victims of some kind of fraud. In 2010, the banking sector have lost approximately R$ 1.5 billion due to financial fraud perpetrated on customers considering only documentary fraud and the electronic banking fraud losses exceeded R$ 900 million in the same year. The types of fraud were many, including fraud during account opening, cloned checks, forgery, alteration barcode and card cloning. Fraud is a common problem in organizations and widely discussed in the market, however it was found that there is a theoretical gap when it comes to managing external fraud. The objective of this research was to structure a conceptual framework for the Management of Fraud and its comparison with the practice. This is an exploratory qualitative study and was conducted through analysis based on Grounded Theory defining categories from the available literature and interviews with comparison to a bank and an association of financial institutions, in addition to news articles. Regarding the use of results is an applied research its result can also contribute to the theoretical discussion, and be applied to any organization interested in managing financial fraud. The results of the development of the conceptual framework shows that the management of external financial fraud has four phases: Continuous, Prevention, Detection and Reaction and the defined categories are located in them. Regarding the comparison of theory with practice, not all aspects verified in the literature could be found in the reports of interviews and newspaper articles analyzed.