The Experts below are selected from a list of 3579 Experts worldwide ranked by ideXlab platform
Bjorn Ottersten - One of the best experts on this subject based on the ideXlab platform.
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feature engineering strategies for Credit Card Fraud detection
Expert Systems With Applications, 2016Co-Authors: Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Bjorn OtterstenAbstract:Credit Card Fraud detection evaluation measure.Each example is assumed to have different financial cost.Transaction aggregation strategy for predicting Fraud.Periodic features using the von Mises distribution.Code is open source and available at albahnsen.com/CostSensitiveClassification. Every year billions of Euros are lost worldwide due to Credit Card Fraud. Thus, forcing financial institutions to continuously improve their Fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, and do not take into account the actual financial costs associated with the Fraud detection process. Moreover, when constructing a Credit Card Fraud detection model, it is very important how to extract the right features from the transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we expand the transaction aggregation strategy, and propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Then, using a real Credit Card Fraud dataset provided by a large European Card processing company, we compare state-of-the-art Credit Card Fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%.
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detecting Credit Card Fraud using periodic features
International Conference on Machine Learning and Applications, 2015Co-Authors: Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Bjorn OtterstenAbstract:When constructing a Credit Card Fraud detection model, it is very important to extract the right features from transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Using a real Credit Card Fraud dataset provided by a large European Card processing company, we compare state-of-the-art Credit Card Fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%. The aforementioned Card processing company is currently incorporating the methodology proposed in this paper into their Fraud detection system.
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ICMLA - Detecting Credit Card Fraud Using Periodic Features
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015Co-Authors: Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Bjorn OtterstenAbstract:When constructing a Credit Card Fraud detection model, it is very important to extract the right features from transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Using a real Credit Card Fraud dataset provided by a large European Card processing company, we compare state-of-the-art Credit Card Fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%. The aforementioned Card processing company is currently incorporating the methodology proposed in this paper into their Fraud detection system.
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Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk
Machine Learning and Applications (ICMLA), 2013 12th International Conference on, 2013Co-Authors: Alejandro Correa Bahnsen, Ana Stojanovic, Djamila Aouada, Bjorn OtterstenAbstract:Credit Card Fraud is a growing problem that affects Card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art Credit Card Fraud detection algorithms miss to include the real costs of Credit Card Fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to Fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European Card processing company.
Alejandro Correa Bahnsen - One of the best experts on this subject based on the ideXlab platform.
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feature engineering strategies for Credit Card Fraud detection
Expert Systems With Applications, 2016Co-Authors: Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Bjorn OtterstenAbstract:Credit Card Fraud detection evaluation measure.Each example is assumed to have different financial cost.Transaction aggregation strategy for predicting Fraud.Periodic features using the von Mises distribution.Code is open source and available at albahnsen.com/CostSensitiveClassification. Every year billions of Euros are lost worldwide due to Credit Card Fraud. Thus, forcing financial institutions to continuously improve their Fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, and do not take into account the actual financial costs associated with the Fraud detection process. Moreover, when constructing a Credit Card Fraud detection model, it is very important how to extract the right features from the transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we expand the transaction aggregation strategy, and propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Then, using a real Credit Card Fraud dataset provided by a large European Card processing company, we compare state-of-the-art Credit Card Fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%.
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detecting Credit Card Fraud using periodic features
International Conference on Machine Learning and Applications, 2015Co-Authors: Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Bjorn OtterstenAbstract:When constructing a Credit Card Fraud detection model, it is very important to extract the right features from transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Using a real Credit Card Fraud dataset provided by a large European Card processing company, we compare state-of-the-art Credit Card Fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%. The aforementioned Card processing company is currently incorporating the methodology proposed in this paper into their Fraud detection system.
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ICMLA - Detecting Credit Card Fraud Using Periodic Features
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015Co-Authors: Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Bjorn OtterstenAbstract:When constructing a Credit Card Fraud detection model, it is very important to extract the right features from transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Using a real Credit Card Fraud dataset provided by a large European Card processing company, we compare state-of-the-art Credit Card Fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%. The aforementioned Card processing company is currently incorporating the methodology proposed in this paper into their Fraud detection system.
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Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk
Machine Learning and Applications (ICMLA), 2013 12th International Conference on, 2013Co-Authors: Alejandro Correa Bahnsen, Ana Stojanovic, Djamila Aouada, Bjorn OtterstenAbstract:Credit Card Fraud is a growing problem that affects Card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art Credit Card Fraud detection algorithms miss to include the real costs of Credit Card Fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to Fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European Card processing company.
Inna Skarbovsky - One of the best experts on this subject based on the ideXlab platform.
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a prototype for Credit Card Fraud management industry paper
Distributed Event-Based Systems, 2017Co-Authors: Alexander Artikis, Ivo Correia, Fabiana Fournier, Inna Skarbovsky, Nikos Katzouris, Chris Baber, Natan Morar, Georgios PaliourasAbstract:To prevent problems and capitalise on opportunities before they even occur, the research project SPEEDD proposed a methodology, and developed a prototype for proactive event-driven decisionmaking. We present the application of this methodology to Credit Card Fraud management. The machine learning component of the SPEEDD prototype supports the online construction of Fraud patterns, allowing it to efficiently adapt to the continuously changing Fraud types. Moreover, the user interface of the prototype enables Fraud analysts to make the most out of the results of automation (complex event processing) and thus reach informed decisions. Unlike most academic research on Credit Card Fraud management, the assessment of the prototype (components) is based on representative transaction datasets, allowing for a realistic evaluation.
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DEBS - A Prototype for Credit Card Fraud Management: Industry Paper
Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, 2017Co-Authors: Alexander Artikis, Ivo Correia, Fabiana Fournier, Inna Skarbovsky, Nikos Katzouris, Chris Baber, Natan Morar, Georgios PaliourasAbstract:To prevent problems and capitalise on opportunities before they even occur, the research project SPEEDD proposed a methodology, and developed a prototype for proactive event-driven decisionmaking. We present the application of this methodology to Credit Card Fraud management. The machine learning component of the SPEEDD prototype supports the online construction of Fraud patterns, allowing it to efficiently adapt to the continuously changing Fraud types. Moreover, the user interface of the prototype enables Fraud analysts to make the most out of the results of automation (complex event processing) and thus reach informed decisions. Unlike most academic research on Credit Card Fraud management, the assessment of the prototype (components) is based on representative transaction datasets, allowing for a realistic evaluation.
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DEBS - The uncertain case of Credit Card Fraud detection
Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, 2015Co-Authors: Ivo Correia, Fabiana Fournier, Inna SkarbovskyAbstract:Uncertainty is inherent in many real-time event-driven applications. Credit Card Fraud detection is a typical uncertain domain, where potential Fraud incidents must be detected in real time and tagged before the transaction has been accepted or denied. We present extensions to the IBM Proactive Technology Online (PROTON) open source tool to cope with uncertainty. The inclusion of uncertainty aspects impacts all levels of the architecture and logic of an event processing engine. The extensions implemented in PROTON include the addition of new built-in attributes and functions, support for new types of operands, and support for event processing patterns to cope with all these. The new capabilities were implemented as building blocks and basic primitives in the complex event processing programmatic language. This enables implementation of event-driven applications possessing uncertainty aspects from different domains in a generic manner. A first application was devised in the domain of Credit Card Fraud detection. Our preliminary results are encouraging, showing potential benefits that stem from incorporating uncertainty aspects to the domain of Credit Card Fraud detection.
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Industry Paper: The Uncertain Case of Credit Card Fraud Detection
2015Co-Authors: Ivo Correia, Fabiana Fournier, Inna SkarbovskyAbstract:Uncertainty is inherent in many real-time event-driven applications. Credit Card Fraud detection is a typical uncertain domain, where potential Fraud incidents must be detected in real time and tagged before the transaction has been accepted or denied. We present extensions to the IBM Proactive Technology Online (PROTON) open source tool to cope with uncertainty. The inclusion of uncertainty aspects impacts all levels of the architecture and logic of an event processing engine. The extensions implemented in PROTON include the addition of new built-in attributes and functions, support for new types of operands, and support for event processing patterns to cope with all these. The new capabilities were implemented as building blocks and basic primitives in the complex event processing programmatic language. This enables implementation of eventdriven applications possessing uncertainty aspects from different domains in a generic manner. A first application was devised in the domain of Credit Card Fraud detection. Our preliminary results are encouraging, showing potential benefits that stem from incorporating uncertainty aspects to the domain of Credit Card Fraud detection.
J. Christopher Westland - One of the best experts on this subject based on the ideXlab platform.
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Employing transaction aggregation strategy to detect Credit Card Fraud
Expert Systems with Applications, 2012Co-Authors: Sanjeev Jha, Montserrat Guillén, J. Christopher WestlandAbstract:Credit Card Fraud costs consumers and the financial industry billions of dollars annually. However, there is a dearth of published literature on Credit Card Fraud detection. In this study we employed transaction aggregation strategy to detect Credit Card Fraud. We aggregated transactions to capture consumer buying behavior prior to each transaction and used these aggregations for model estimation to identify Fraudulent transactions. We use real-life data of Credit Card transactions from an international Credit Card operation for transaction aggregation and model estimation.
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Data mining for Credit Card Fraud: A comparative study
Decision Support Systems, 2011Co-Authors: Siddhartha Bhattacharyya, Sanjeev Jha, Kurian Tharakunnel, J. Christopher WestlandAbstract:Credit Card Fraud is a serious and growing problem. While predictive models for Credit Card Fraud detection are in active use in practice, reported studies on the use of data mining approaches for Credit Card Fraud detection are relatively few, possibly due to the lack of available data for research. This paper evaluates two advanced data mining approaches, support vector machines and random forests, together with the well-known logistic regression, as part of an attempt to better detect (and thus control and prosecute) Credit Card Fraud. The study is based on real-life data of transactions from an international Credit Card operation.
Na Wang - One of the best experts on this subject based on the ideXlab platform.
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Research on Credit Card Fraud Detection Model Based on Distance Sum
Artificial Intelligence, 2009. JCAI '09. International Joint Conference on, 2009Co-Authors: Wen-Fang Yu, Na WangAbstract:Along with increasing Credit Cards and growing trade volume in China, Credit Card Fraud rises sharply. How to enhance the detection and prevention of Credit Card Fraud becomes the focus of risk control of banks. This paper proposes a Credit Card Fraud detection model using outlier detection based on distance sum according to the infrequency and unconventionality of Fraud in Credit Card transaction data, applying outlier mining into Credit Card Fraud detection. Experiments show that this model is feasible and accurate in detecting Credit Card Fraud.
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JCAI - Research on Credit Card Fraud Detection Model Based on Distance Sum
2009 International Joint Conference on Artificial Intelligence, 2009Co-Authors: Na WangAbstract:Along with increasing Credit Cards and growing trade volume in China, Credit Card Fraud rises sharply. How to enhance the detection and prevention of Credit Card Fraud becomes the focus of risk control of banks. This paper proposes a Credit Card Fraud detection model using outlier detection based on distance sum according to the infrequency and unconventionality of Fraud in Credit Card transaction data, applying outlier mining into Credit Card Fraud detection. Experiments show that this model is feasible and accurate in detecting Credit Card Fraud.