| 4631. |
- López-Fauqued, M., et al.
(författare)
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Safety profile of the adjuvanted recombinant zoster vaccine : Pooled analysis of two large randomised phase 3 trials
- 2019
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Ingår i: Vaccine. - : Elsevier Ltd. - 0264-410X .- 1873-2518. ; 37:18, s. 2482-2493
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Tidskriftsartikel (refereegranskat)abstract
- Background: The ZOE-50 (NCT01165177) and ZOE-70 (NCT01165229) phase 3 clinical trials showed that the adjuvanted recombinant zoster vaccine (RZV) was ≥90% efficacious in preventing herpes zoster in adults. Here we present a comprehensive overview of the safety data from these studies. Methods: Adults aged ≥50 (ZOE-50) and ≥70 (ZOE-70) years were randomly vaccinated with RZV or placebo. Safety analyses were performed on the pooled total vaccinated cohort, consisting of participants receiving at least one dose of RZV or placebo. Solicited and unsolicited adverse events (AEs) were collected for 7 and 30 days after each vaccination, respectively. Serious AEs (SAEs) were collected from the first vaccination until 12 months post-last dose. Fatal AEs, vaccination-related SAEs, and potential immune-mediated diseases (pIMDs) were collected during the entire study period. Results: Safety was evaluated in 14,645 RZV and 14,660 placebo recipients. More RZV than placebo recipients reported unsolicited AEs (50.5% versus 32.0%); the difference was driven by transient injection site and solicited systemic reactions that were generally seen in the first week post-vaccination. The occurrence of overall SAEs (RZV: 10.1%; Placebo: 10.4%), fatal AEs (RZV: 4.3%; Placebo: 4.6%), and pIMDs (RZV: 1.2%; Placebo: 1.4%) was balanced between groups. The occurrence of possible exacerbations of pIMDs was rare and similar between groups. Overall, except for the expected local and systemic symptoms, the safety results were comparable between the RZV and Placebo groups irrespective of participant age, gender, or race. Conclusions: No safety concerns arose, supporting the favorable benefit-risk profile of RZV. © 2019 GlaxoSmithKline Biologicals SA
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| 4632. |
- Lopez-Moreno, Jorge, et al.
(författare)
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Measuring the Perception of Light Inconsistencies
- 2010
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Konferensbidrag (refereegranskat)abstract
- In this paper we explore the ability of the human visual system to detect inconsistencies in the illumination of objects in images. We specifically focus on objects being lit from different angles as the rest of the image. We present the results of three different tests, two with synthetic objects and a third one with digitally manipulated real images. Our results seem to agree with previous publications exploring the topic, but we extend them by providing quantifiable data which in turn suggest approximate perceptual thresholds. Given that light detection in single images is an ill-posed problem, these thresholds can provide valid error limits to related algorithms in different contexts, such as compositing or augmented reality.
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| 4633. |
- Lopez-Rojas, Edgar Alonso, 1977-
(författare)
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Applying Simulation to the Problem of Detecting Financial Fraud
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This 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 introduce our approach towards the use of computer simulation for fraud detection and its applications in financial domains. Fraud is an important problem that impact the whole economy. Currently, there is a 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 Payment Simulator (PaySim) and Retail Store Simulator (RetSim), which allow us to generate synthetic transactional data that contains both: normal customer behaviour and fraudulent behaviour. These simulations are Multi Agent-Based Simulations (MABS) and were calibrated using real data from financial transactions. We developed agents that represent the clients and merchants 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 and merchants, 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 methods. 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 or breaking any non-disclose agreements. Using statistics and social network analysis (SNA) on real data we calibrated the relations between our agents and generate realistic synthetic data sets that were verified against the domain and validated statistically against the original source. We then used the simulation tools to model common fraud scenarios to ascertain exactly how effective are fraud techniques such as the simplest form of statistical threshold detection, which is perhaps the most common in use. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level. This means that there seems to be little economic room for improved fraud detection techniques. We also implemented other applications for the simulator tools such as the set up of a triage model and the measure of cost of fraud. This showed to be an important help for managers that aim to prioritise the fraud detection and want to know how much they should invest in fraud to keep the loses below a desired limit according to different experimented and expected scenarios of fraud.
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| 4634. |
- Lopez-Rojas, Edgar Alonso
(författare)
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Extending the RetSim Simulator for Estimating the Cost of fraud in the Retail Store Domain
- 2015
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Ingår i: Proceedings of the European Modeling and Simulation Symposium, 2015. - 9788897999485 - 9788897999577
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Konferensbidrag (refereegranskat)abstract
- RetSim 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.
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| 4635. |
- Lopez-Rojas, Edgar Alonso, et al.
(författare)
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Money Laundering Detection using Synthetic Data
- 2012
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Konferensbidrag (refereegranskat)abstract
- Criminals use money laundering to make the proceeds from their illegal activities look legitimate in the eyes of the rest of society. Current countermeasures taken by financial organizations are based on legal requirements and very basic statistical analysis. Machine Learning offers a number of ways to detect anomalous transactions. These methods can be based on supervised and unsupervised learning algorithms that improve the performance of detection of such criminal activity. In this study we present an analysis of the difficulties and considerations of applying machine learning techniques to this problem. We discuss the pros and cons of using synthetic data and problems and advantages inherent in the generation of such a data set. We do this using a case study and suggest an approach based on Multi-Agent Based Simulations (MABS).
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| 4636. |
- Lopez-Rojas, Edgar Alonso, et al.
(författare)
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Multi Agent Based Simulation (MABS) of Financial Transactions for Anti Money Laundering (AML)
- 2012
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Konferensbidrag (refereegranskat)abstract
- Mobile money is a service for performing financial transactions using a mobile phone. By law it has to have protection against money laundering and other types of fraud. Research into fraud detection methods is not as advanced as in other similar fields. However, getting access to real world data is difficult, due to the sensitive nature of financial transactions, and this makes research into detection methods difficult. Thus, we propose an approach based on a Multi-Agent Based Simulation (MABS) for the generation of synthetic transaction data. We present the generation of synthetic data logs of transactions and the use of such a data set for the study of different detection scenarios using machine learning.
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| 4637. |
- Lopez-Rojas, Edgar Alonso
(författare)
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On the Simulation of Financial Transactions for Fraud Detection Research
- 2014
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This 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.
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| 4638. |
- Lopez-Rojas, Edgar Alonso, 1977-, et al.
(författare)
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RETSIM : A shoe store agent-based simulation for fraud detection
- 2013
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Ingår i: 25th European Modeling and Simulation Symposium, EMSS 2013. - 9788897999225 ; , s. 25-34
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Konferensbidrag (refereegranskat)abstract
- RetSim is an agent-based simulator of a shoe store based on the transactional data of one of the largest retail shoe sellers in Sweden. The aim of RetSim is the generation of synthetic data that can be used for fraud detection research. Statistical and a Social Network Analysis (SNA) of relations between staff and customers was used to develop and calibrate the model. Our ultimate goal is for RetSim to be usable to model relevant scenarios to generate realistic data sets that can be used by academia, and others, to develop and reason about fraud detection methods without leaking any sensitive information about the underlying data. Synthetic data has the added benefit of being easier to acquire, faster and at less cost, for experimentation even for those that have access to their own data. We argue that RetSim generates data that usefully approximates the relevant aspects of the real data.
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| 4639. |
- Lopez-Rojas, Edgar, et al.
(författare)
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A review of computer simulation for fraud detection research in financial datasets
- 2016
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Ingår i: FTC 2016 - Proceedings of Future Technologies Conference. - : IEEE. - 9781509041718 ; , s. 932-935
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Konferensbidrag (refereegranskat)abstract
- The investigation of fraud in the financial domain has been restricted to those who have access to relevant data. However, customer financial records are protected by law and internal policies, therefore they are not available for most of the researchers in the area of fraud detection. This paper aims to present the work of those researchers who have had access to data and present an interesting approach to fraud detection research; which is the generation of a synthetic data set to work on fraud detection research. Some of the domains covered in this review include mobile money payments, e-payments, retail stores, online bank services and credit card payments. We also cover some of the most relevant surveys in the field and point out the impossibility to compare this work due to the lack of common public data set to test different results.
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| 4640. |
- Lopez-Rojas, Edgar Alonso, 1977-, et al.
(författare)
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Social Simulation of Commercial and Financial Behaviour for Fraud Detection Research
- 2014
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Ingår i: Advances in Computational Social Science and Social Simulation. - Barcelona.
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Konferensbidrag (refereegranskat)abstract
- We present a social simulation model that covers three main financialservices: Banks, Retail Stores, and Payments systems. Our aim is toaddress the problem of a lack of public data sets for fraud detectionresearch in each of these domains, and provide a variety of fraudscenarios such as money laundering, sales fraud (based on refunds anddiscounts), and credit card fraud. Currently, there is a general lackof public research concerning fraud detection in the financial domainsin general and these three in particular. One reason for this is thesecrecy and sensitivity of the customers data that is needed toperform research. We present PaySim, RetSim, and BankSim asthree case studies of social simulations for financial transactionsusing agent-based modelling. These simulators enable us to generatesynthetic transaction data of normal behaviour of customers, and alsoknown fraudulent behaviour. This synthetic data can be used to furtheradvance fraud detection research, without leaking sensitiveinformation about the underlying data. Using statistics and socialnetwork analysis (SNA) on real data we can calibrate the relationsbetween staff and customers, and generate realistic synthetic datasets. The generated data represents real world scenarios that arefound in the original data with the added benefit that this data canbe shared with other researchers for testing similar detection methodswithout concerns for privacy and other restrictions present when usingthe original data.
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