1,720,975 research outputs found
Using the RetSim simulator for fraud detection research
No full textManaging fraud is important for business, retail and financialalike. One method to manage fraud is by \emph{detection}, wheretransactions etc. are monitored and suspicious behaviour is flaggedfor further investigation. There is currently a lack of publicresearch in this area. The main reason is the sensitive nature of thedata. Publishing real financial transaction data would seriouslycompromise the privacy of both customers, and companies alike. Wepropose to address this problem by building RetSim, a multi-agentbased simulator (MABS) calibrated with real transaction data from oneof the largest shoe retailers in Scandinavia. RetSim allows us togenerate synthetic transactional data that can be publicly shared andstudied without leaking business sensitive information, and stillpreserve the important characteristics of the data. We then use RetSim to model two common retail fraud scenarios toascertain exactly how effective the simplest form of statisticalthreshold detection could be. The preliminary results of our testedfraud detection method show that the threshold detection is effectiveenough at keeping fraud losses at a set level, that there is littleeconomic room for improved techniques
On the Simulation of Financial Transactions for Fraud Detection Research [Elektronisk resurs]
No full textThis thesis introduces a financial simulation model covering two related financial domains: Mobile Payments and Retail Stores systems. The problem we address in these domains is different types of fraud. We limit ourselves to isolated cases of relatively straightforward fraud. However, in this thesis the ultimate aim is to cover more complex types of fraud, such as money laundering, that comprises multiple organisations and domains. Fraud is an important problem that impact the whole economy. Currently, there is a general lack of public research into the detection of fraud. One important reason is the lack of transaction data which is often sensitive. To address this problem we present a Mobile Money Simulator (PaySim) and Retail Store Simulator (RetSim), which allow us to generate synthetic transactional data. These simulations are based on real transaction data. These simulations are multi agent based simulations. Hence, we developed agents that represent the clients in PaySim and customers and salesmen in RetSim. The normal behaviour was based on behaviour observed in data from the field, and is codified in the agents as rules of transactions and interaction between clients, or customers and salesmen. Some of these agents were intentionally designed to act fraudulently, based on observed patterns of real fraud. We introduced known signatures of fraud in our model and simulations to test and evaluate our fraud detection results. The resulting behaviour of the agents generate a synthetic log of all transactions as a result of the simulation. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data. Using statistics and social network analysis (SNA) on real data we could calibrate the relations between staff and customers and generate realistic synthetic data sets that were validated statistically against the original. We then used RetSim to model two common retail fraud scenarios to ascertain exactly how effective the simplest form of statistical threshold detection commonly in use could be. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level, that there seems to be little economic room for improved fraud detection techniques.</p
Extending the RetSim Simulator for Estimating the Cost of fraud in the Retail Store Domain [Elektronisk resurs]
No full textRetSim is a multi-agent based simulator (MABS) calibrated with real transaction data from one of the largest shoe retailers in Scandinavia. RetSim allows us to generate synthetic transactional data that can be publicly shared and studied without leaking business sensitive information, and still preserve the important characteristics of the data.In this paper we extended the fraud model of RetSim to cover more cases of internal fraud perpetrated by the staff and allow inventory control to flag even more suspicious activity. We also generated sufficient number of runs using a range of fraud parameters to cover a vast number of fraud scenarios that can be studied. We then use RetSim to simulate some of the more common retail fraud scenarios to ascertain exactly the cost of fraud using different fraud parameters for each case.</p
On the Simulation of Financial Transactions for Fraud Detection Research
No full textThis thesis introduces a financial simulation model covering two related financial domains: Mobile Payments and Retail Stores systems. The problem we address in these domains is different types of fraud. We limit ourselves to isolated cases of relatively straightforward fraud. However, in this thesis the ultimate aim is to cover more complex types of fraud, such as money laundering, that comprises multiple organisations and domains. Fraud is an important problem that impact the whole economy. Currently, there is a general lack of public research into the detection of fraud. One important reason is the lack of transaction data which is often sensitive. To address this problem we present a Mobile Money Simulator (PaySim) and Retail Store Simulator (RetSim), which allow us to generate synthetic transactional data. These simulations are based on real transaction data. These simulations are multi agent based simulations. Hence, we developed agents that represent the clients in PaySim and customers and salesmen in RetSim. The normal behaviour was based on behaviour observed in data from the field, and is codified in the agents as rules of transactions and interaction between clients, or customers and salesmen. Some of these agents were intentionally designed to act fraudulently, based on observed patterns of real fraud. We introduced known signatures of fraud in our model and simulations to test and evaluate our fraud detection results. The resulting behaviour of the agents generate a synthetic log of all transactions as a result of the simulation. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data. Using statistics and social network analysis (SNA) on real data we could calibrate the relations between staff and customers and generate realistic synthetic data sets that were validated statistically against the original. We then used RetSim to model two common retail fraud scenarios to ascertain exactly how effective the simplest form of statistical threshold detection commonly in use could be. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level, that there seems to be little economic room for improved fraud detection techniques
Extending the RetSim Simulator for Estimating the Cost of fraud in the Retail Store Domain
No full textRetSim is a multi-agent based simulator (MABS) calibrated with real transaction data from one of the largest shoe retailers in Scandinavia. RetSim allows us to generate synthetic transactional data that can be publicly shared and studied without leaking business sensitive information, and still preserve the important characteristics of the data. In this paper we extended the fraud model of RetSim to cover more cases of internal fraud perpetrated by the staff and allow inventory control to flag even more suspicious activity. We also generated sufficient number of runs using a range of fraud parameters to cover a vast number of fraud scenarios that can be studied. We then use RetSim to simulate some of the more common retail fraud scenarios to ascertain exactly the cost of fraud using different fraud parameters for each case
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Social simulation of commercial and financial behaviour for fraud detection research
Get PDFWe present a social simulation model that covers three main financial services: Banks, Retail Stores, and Payments systems. Our aim is to address the problem of a lack of public data sets for fraud detection research in each of these domains, and provide a variety of fraud scenarios such as money laundering, sales fraud (based on refunds and discounts), and credit card fraud. Currently, there is a general lack of public research concerning fraud detection in the financial domains in general and these three in particular. One reason for this is the secrecy and sensitivity of the customers data that is needed to perform research. We present PaySim, RetSim, and BankSim as three case studies of social simulations for financial transactions using agent-based modelling. These simulators enable us to generate synthetic transaction data of normal behaviour of customers, and also known fraudulent behaviour. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data. Using statistics and social network analysis (SNA) on real data we can calibrate the relations between staff and customers, and generate realistic synthetic data sets. The generated data represents real world scenarios that are found in the original data with the added benefit that this data can be shared with other researchers for testing similar detection methods without concerns for privacy and other restrictions present when using the original data
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