Publicação
Detection of fraud patterns in electronic commerce environments
| Resumo: | Electronic transactions (e-commerce) have revolutionized the way consumers shop, making small and local retailers, which were being affected by the worldwide crisis, accessible to the entire world. As e-commerce market expands, commercial transactions supported by credit cards - Card or Customer Not Present (CNP) also increases. This growing relationship, quite natural and expected, has clear advantages, facilitating e-commerce transactions and attracting new possibilities for trading. However, at the same time a big and serious problem emerge: the occurrence of fraudulent situations in payments. Fraud imposes severe financial losses, which deeply impacts e-commerce companies and their revenue. In order to minimize losses, they spend a lot of efforts (and money) trying to establish the most satisfactory solutions to detect and counteract in a timely manner the occurrence of a fraud scenario. In the ecommerce domain, fraud analysts are typically interested in subject oriented customer data, frequently extracted from each order process that occurred in an e-commerce site. Besides transactional data, all their behavior data e.g. clickstream data are traced and recorded, enriching the means of detection with profiling data and providing a way to trace customers behavior along time. In this work, a signature-based method was used to establish the characteristics of user behavior and detect potential fraud cases. Signatures have already been used successfully for anomalous detection in many areas like credit card usage, network intrusion, and in particular in telecommunications fraud. A signature is defined by a set of attributes that receive a diverse range of variables - e.g. the average number of orders, time spent per order, number of payment attempts, number of days since last visit, and many others - related to the behavior of a user, referring to an e-commerce application scenario. Based on the analysis of user behavior deviation, detected by comparing the user recent activity with the user behavior data, which is expressed through the user signature, it's possible to detect potential fraud situations (deviate behaviors) in useful time, giving a more robust and accurate support decision system to the fraud analysts on their daily job. |
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| Autores principais: | Mota, Gabriel Ivan da Silva Rosa Neco da |
| Assunto: | Usage profiling over e-commerce systems Fraud detection and prevention Clickstream processing Signatures based methods Fraud detection applications Perfis de utilização em sistemas e-commerce Detecção e prevenção de fraude Processamento clickstream Métodos baseados em assinaturas Aplicações para a detecção de fraude Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
| Ano: | 2014 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Electronic transactions (e-commerce) have revolutionized the way consumers shop, making small and local retailers, which were being affected by the worldwide crisis, accessible to the entire world. As e-commerce market expands, commercial transactions supported by credit cards - Card or Customer Not Present (CNP) also increases. This growing relationship, quite natural and expected, has clear advantages, facilitating e-commerce transactions and attracting new possibilities for trading. However, at the same time a big and serious problem emerge: the occurrence of fraudulent situations in payments. Fraud imposes severe financial losses, which deeply impacts e-commerce companies and their revenue. In order to minimize losses, they spend a lot of efforts (and money) trying to establish the most satisfactory solutions to detect and counteract in a timely manner the occurrence of a fraud scenario. In the ecommerce domain, fraud analysts are typically interested in subject oriented customer data, frequently extracted from each order process that occurred in an e-commerce site. Besides transactional data, all their behavior data e.g. clickstream data are traced and recorded, enriching the means of detection with profiling data and providing a way to trace customers behavior along time. In this work, a signature-based method was used to establish the characteristics of user behavior and detect potential fraud cases. Signatures have already been used successfully for anomalous detection in many areas like credit card usage, network intrusion, and in particular in telecommunications fraud. A signature is defined by a set of attributes that receive a diverse range of variables - e.g. the average number of orders, time spent per order, number of payment attempts, number of days since last visit, and many others - related to the behavior of a user, referring to an e-commerce application scenario. Based on the analysis of user behavior deviation, detected by comparing the user recent activity with the user behavior data, which is expressed through the user signature, it's possible to detect potential fraud situations (deviate behaviors) in useful time, giving a more robust and accurate support decision system to the fraud analysts on their daily job. |
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