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Dissertations / Theses on the topic 'FRAUD AND DATA BREACHES'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Smith, Tanshanika Turner. "Examining Data Privacy Breaches in Healthcare." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2623.

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Healthcare data can contain sensitive, personal, and confidential information that should remain secure. Despite the efforts to protect patient data, security breaches occur and may result in fraud, identity theft, and other damages. Grounded in the theoretical backdrop of integrated system theory, the purpose of this study was to determine the association between data privacy breaches, data storage locations, business associates, covered entities, and number of individuals affected. Study data consisted of secondary breach information retrieved from the Department of Health and Human Services

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2

Ofori-Duodu, Michael Samuel. "Exploring Data Security Management Strategies for Preventing Data Breaches." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7947.

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Insider threat continues to pose a risk to organizations, and in some cases, the country at large. Data breach events continue to show the insider threat risk has not subsided. This qualitative case study sought to explore the data security management strategies used by database and system administrators to prevent data breaches by malicious insiders. The study population consisted of database administrators and system administrators from a government contracting agency in the northeastern region of the United States. The general systems theory, developed by Von Bertalanffy, was used as the co

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Saber, Jennifer. "Determining Small Business Cybersecurity Strategies to Prevent Data Breaches." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/4991.

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Cybercrime is one of the quickest growing areas of criminality. Criminals abuse the speed, accessibility, and privacy of the Internet to commit diverse crimes involving data and identity theft that cause severe damage to victims worldwide. Many small businesses do not have the financial and technological means to protect their systems from cyberattack, making them vulnerable to data breaches. This exploratory multiple case study, grounded in systems thinking theory and routine activities theory, encompassed an investigation of cybersecurity strategies used by 5 small business leaders in Middle

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Phillips, Thurman B., and Raymond J. Lanclos. "Data analytics in procurement fraud prevention." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/42708.

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Approved for public release; distribution is unlimited<br>The purpose of this research project is to explore the viability of detecting anomalies through using data analytics software as a tool in procurement fraud prevention and to analyze its potential policy implications on federal procurement stakeholders. According to a survey conducted in 2012 by the Association of Certified Fraud Examiners, organizations lose an estimated 5% of their revenues to fraud each year. In order to relate this estimate to the Department of Defense (DOD), this estimated percentage was applied to the requested DO

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Kongnso, Fedinand Jaiventume. "Best Practices to Minimize Data Security Breaches for Increased Business Performance." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1825.

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In the United States, businesses have reported over 2,800 data compromises of an estimated 543 million records, with security breaches costing firms approximately $7.2 million annually. Scholars and industry practitioners have indicated a significant impact of security breaches on consumers and organizations. However, there are limited data on the best practices for minimizing the impact of security breaches on organizational performance. The purpose of this qualitative multicase study was to explore best practices technology leaders use to minimize data security breaches for increased busines

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Perols, Johan L. "Detecting Financial Statement Fraud: Three Essays on Fraud Predictors, Multi-Classifier Combination and Fraud Detection Using Data Mining." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002486.

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7

Osei-Amanfi, Michael. "A Case Study Exploration of Strategies to Avoid Cloud Computing Data Breaches." Thesis, Grand Canyon University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13423232.

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<p> The purpose of this qualitative single case study was to explore the available strategies IT leaders at ABC, a Managed IT Services company in the SME sector in Columbus, Ohio, may use to avoid data breaches in the cloud environment. The security framework established by the Cloud Security Alliance (CSA) provided the conceptual framework for this study. A purposeful sampling strategy was used to select 10 IT leaders to participate in the study. Data were gathered through open-ended, semi-structured individual face-to-face interviews, asynchronous discussions through e-mails, and reviews of

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8

Moruzzi, Davide <1997&gt. "Cyber data intelligence tool: from raw data breaches to structured storing, performant searching and user-friendly visualisation." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19916.

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The thesis aims to summarise and motivate the cyber intelligence project carried out during the internship period in a local company. The scenario in which the developed software runs will be presented. The technologies and criticalities found will be highlighted and the final product that can be used by the company for its own purposes will be shown. It is possible to find everything you need by surfing the web, also a lot of stolen credentials. Everyday people upload on the web data which came from a breach due to lack of security in the network infrastructures, malware attacks or human err

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Ayereby, Manouan Pierre-Marius. "Overcoming Data Breaches and Human Factors in Minimizing Threats to Cyber-Security Ecosystems." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/6163.

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This mixed-methods study focused on the internal human factors responsible for data breaches that could cause adverse impacts on organizations. Based on the Swiss cheese theory, the study was designed to examine preventative measures that managers could implement to minimize potential data breaches resulting from internal employees' behaviors. The purpose of this study was to provide insight to managers about developing strategies that could prevent data breaches from cyber-threats by focusing on the specific internal human factors responsible for data breaches, the root causes, and the preven

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Griffin, Tony. "Strategies to Prevent Security Breaches Caused by Mobile Devices." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4628.

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Data breaches happen almost every day in the United States and, according to research, the majority of these breaches occur due to a lack of security with organizations' mobile devices. Although most of the security policies related to mobile devices currently in place may meet the guidelines required by law, they often fail to prevent a data breach caused by a mobile device. The main purpose of this qualitative single case study was to explore the strategies used by security managers to prevent data breaches caused by mobile devices. The study population consisted of security managers working

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Garringer, James. "The Role of Protocol Analysis in Cybersecurity| Closing the Gap on Undetected Data Breaches." Thesis, Utica College, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10974156.

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<p> Organizations of all sizes are targets for a cyberattack. Undetected data breaches result in the catastrophic loss of personally identifiable information (PII) causing considerable financial and reputation harm to organizations, while also imposing a risk of identity fraud to consumers. The purpose of this study was to consider the impact that undetected data breaches have on organizations with an additional focus on shortening the gap between the time of data breach and the time of detection through manual protocol analysis and intrusion detection system (IDS) solutions. This research rev

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12

Renström, Martin, and Timothy Holmsten. "Fraud Detection on Unlabeled Data with Unsupervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230592.

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A common problem in systems handling user interaction was the risk for fraudulent behaviour. As an example, in a system with credit card transactions it could have been a person using a another user's account for purchases, or in a system with advertisment it could be bots clicking on ads. These malicious attacks were often disguised as normal interactions and could be difficult to detect. It was especially challenging when working with datasets that did not contain so called labels, which showed if the data point was fraudulent or not. This meant that there were no data that had previously be

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Washington, Kevin D. "The impact of data breaches on market value of firms in the E-Commerce marketplace." Thesis, Capella University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10102712.

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<p> Using a sample of information and security data breaches the present research examines the public announcement impact between Brick and Mortar firms and E-Commerce firms. The dissertation initially analyzes the effects of publicly announced information and security breaches on abnormal stock returns, abnormal trading volume, and firm risk. The dissertation then analyzes differential impact between Brick and Mortar firms and E-Commerce firm’s market value following a data breach. Using a sample size of 38 information security breaches, E-Commerce firms resulted in more negative abnorm

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Enríquez, Luis. "Personal data breaches : towards a deep integration between information security risks and GDPR compliance risks." Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILD016.

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La sécurité de l'information est étroitement liée au droit de protection des données, car une mise en œuvre inefficace de la sécurité peut entraîner des violations de données à caractère personnel. Le RGPD repose sur la gestion de risques pour la protection des droits et libertés des personnes concernées, ce qui signifie que la gestion de risques est le mécanisme de protection des droits fondamentaux. Cependant, l'état de l'art en matière de gestion des risques liés à la sécurité de l'information et de gestion des risques juridiques sont encore immatures. Malheureusement, l'état actuel de l'ar

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Van, Heerden Johan H. "Detecting fraud in cellular telephone networks." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50314.

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Thesis (MSc)--University of Stellenbosch, 2005.<br>ENGLISH ABSTRACT: Cellular network operators globally loose between 3% and 5% of their annual revenue to telecommunications fraud. Hence it is of great importance that fraud management systems are implemented to detect, alarm, and shut down fraud within minutes, minimising revenue loss. Modern proprietary fraud management systems employ (i) classification methods, most often artificial neural networks learning from classified call data records to classify new call data records as fraudulent or legitimate, (ii) statistical methods building

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Gadirova, Nurlana. "The Impacts of Cyberattacks on Private Firms' Cash Holdings." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41924.

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This research investigates 202 data breach events occurring between 2015 and 2019 and the related financial effects on the USA's impacted private firms. From examining previous research, it is obvious that no known studies evaluate the financial impacts of cybercrimes on private firms. Prior studies mostly focus on public firms and stock market reactions even though there is the increasing number of cyberattacks on private firms too. This study seeks to fill the gap by providing the empirical evidence of the impacts on those firms' cash holding after experiencing a cybersecurity attack. Overal

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Metzler, Guillaume. "Learning from imbalanced data : an application to bank fraud detection." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES033.

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La détection de fraudes et d'anomalies, ou plus généralement l’apprentissage dans un contexte déséquilibré, est une tâche très souvent rencontrée dans de nombreuses applications industrielles.Détecter ces anomalies revêt un enjeu majeur dans notre société actuelle de par ses conséquences économiques.La société BLITZ Business Services est confrontée à ce type de problématique dans le cadre de la lutte contre la fraude par chèques. Ces fraudes représentent 0.4% des transactions pour ses clients mais des millions d'euros de pertes par an.Les données de fraudes sont difficiles pour la plupart des

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Haq, Ikram. "Fraud detection for online banking for scalable and distributed data." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171977.

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Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric

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Oransky, Ivan. "Retractions, Post-Publication Peer Review and Fraud: Scientific Publishing's Wild West." University of Arizona Library (Tucson, AZ), 2016. http://hdl.handle.net/10150/621550.

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Presentation given on October 27, 2016 at Data Reproducibility: Integrity and Transparency program as part of Open Access Week 2016.<br>Ivan Oransky and Adam Marcus founded Retraction Watch in 2010. Unbeknownst to them, retractions had grown ten-fold in the previous decade. Oransky will discuss the reasons for that increase, whether fraud is on the rise, the growth of post-publication peer review, and other trends he and Marcus have seen as they've built a site that is now viewed by 150,000 people per month, and funded by philanthropies including the MacArthur and Arnold Foundations.

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Pelletier, Justin M. "Effects of Data Breaches on Sector-Wide Systematic Risk in Financial, Technology, Healthcare and Services Sectors." Thesis, Capella University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10615009.

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<p> This research informs an ongoing debate regarding a firm’s incentives to invest in information security. Previous research reported that data breaches have had a decreasing impact on a company’s stock price over time, leading researchers to conclude that market-based incentives are decreasingly effective. Some information security economists also suggested that further regulation is necessary because they found that capital market participants poorly accounted for the spillover effects of a breach—the effects of a breach that are external to the breached company. However,

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Monteiro, António S. "Multiple additive regression trees : a methodology for predictive data mining for fraud detection /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FMonteiro.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2002.<br>Thesis advisor(s): Lyn R. Whitaker, Samuel E. Buttrey. Includes bibliographical references (p. 89-91). Also available online.

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Amaya, de la Pena Ignacio. "Fraud detection in online payments using Spark ML." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219916.

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Frauds in online payments cause billions of dollars in losses every year. To reduce them, traditional fraud detection systems can be enhanced with the latest advances in machine learning, which usually require distributed computing frameworks to handle the big size of the available data. Previous academic work has failed to address fraud detection in real-world environments. To fill this gap, this thesis focuses on building a fraud detection classifier on Spark ML using real-world payment data. Class imbalance and non-stationarity reduced the performance of our models, so experiments to tackle

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Zachis, Savio de Melo. "Analysis of breaches and co-breaks in financial series: a non-parametric approach data using high frequency." Universidade Federal do CearÃ, 2009. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=3730.

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nÃo hÃ<br>This research use a non-parametric test developed by Lee & MYKLAND (2007) to extract jumps in IBOVESPA series and study its dynamics. Among the qualities of this test there are the ability to identify the exact time of occurrence of break / co-break, the sign and size of it. The jumps in the series of Dow Jones, Exchange rate, C-Bond spread and SELIC rate were also estimated and the relation with IBOVESPAÂs jump were verified. The results were analyzed by descriptive statistics, analysis of frequencies and Logit regression models. As a main result there was the predominance of co-bre

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Hawlová, Kateřina. "Návrh postupu odhalování podvodného chování." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-114039.

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The thesis focus on fraud management - the detection of fraudulent behavior. In the first part current market situation in finance, telecommunications and healthcare is mapped. Following section contains the review of methods used for data mining which is closely related to detection of fradulent or non-standard behaviour. Part of the thesis is focused on the fraudulent behaviour detection in the procurement area. The whole cycle of deployment and use of the tool for detection is introduced. It covers data collection, identification of suspected cases, outcomes analysis and recommendations how

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Kävrestad, Joakim. "DEFINING, CATEGORIZING AND DEFENDING AGAINST ONLINE FRAUD." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9732.

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Frauds conducted online are crimes that are and has been increasing in numbers over the last years. Government reports shows that this is the case in Sweden as well as internationally. As of today the Swedish legal systems lacks a definition and categorization approach for online frauds. This thesis aims at developing such definition and a classification scheme that can be used to classify online frauds. Further, this thesis suggests and evaluates a defense mechanism for use in arenas were online frauds are executed. The results bring a definition and classification scheme that has been develo

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Edge, Michael Edward. "A Policy Driven Approach to Proactive Fraud Management in Financial Data Streams." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525241.

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Fraud management is a vital business operation for financial institutions towards minimising the widespread effect of fraud upon customer service delivery, bottom line operating expenditure and the organisation's brand image reputation. Rapidly changing fraud patterns continue to demonstrate fraudster's ability to actively reengineer their methods in response to ad-hoc security protocol deployments, and highlights the distinct gap between the speed of transaction execution within streaming financial data and corresponding fraud technology frameworks. Despite extensive research into fraud detec

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Rouillard, Gregory W. "An improved unsupervised modeling methodology for detecting fraud in vendor payment transactions." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FRouillard.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2003.<br>Thesis advisor(s): Samuel E. Buttrey, Lyn R. Whitaker. Includes bibliographical references (p. 147-148). Also available online.

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Westerlund, Fredrik. "CREDIT CARD FRAUD DETECTION (Machine learning algorithms)." Thesis, Umeå universitet, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136031.

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Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminalcan steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This es

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Monteiro, Antonio Jorge Ferreira da Silva. "Multiple additive regression trees: a methodology for predictive data mining for fraud detection." Thesis, Monterey, California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/4812.

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Approved for public release, distribution is unlimited<br>The Defense Finance Accounting Service DFAS-Operation Mongoose (Internal Review - Seaside) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits. In this thesis a new data mining methodology, Multiple Additive Regression Trees (MART) is applied to the problem of detecting potential fraudulent and suspect transactions (those with conditions needing impro

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Nsibande, Senzosenkosi. "The impact of data breaches of varying severity on the customer loyalty of high net worth individuals in retail banking." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/79631.

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The aim of this study was to investigate the impact of data breaches of varying severity on customer loyalty. The study was motivated by the growth in the amount of data shared and stored by organisations to enhance service offerings (Big Data), and the increase in the frequency and scale of data breaches caused by this. Research shows that customer loyalty is critical to the long-term profitability of an organisation, making the understanding of data breaches on customer loyalty critical for any organisation’s prospects. Despite this significance, literature on how breach severity can influen

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Abidogun, Olusola Adeniyi. "Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks." Thesis, University of the Western Cape, 2005. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention,<br /> marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pat

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Ötting, Marius [Verfasser]. "Sports statistics in the data age: betting fraud detection and performance evaluation / Marius Ötting." Bielefeld : Universitätsbibliothek Bielefeld, 2020. http://d-nb.info/1218230657/34.

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Lopez-Rojas, Edgar Alonso. "Applying Simulation to the Problem of Detecting Financial Fraud." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932.

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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 importan

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Ludvigsen, Jesper. "Handling Imbalanced Data Classification With Variational Autoencoding And Random Under-Sampling Boosting." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412838.

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In this thesis, a comparison of three different pre-processing methods for imbalanced classification data, is conducted. Variational Autoencoder, Random Under-Sampling Boosting and a hybrid approach of the two, are applied to three imbalanced classification data sets with different class imbalances. A logistic regression (LR) model is fitted to each pre-processed data set and based on its classification performance, the pre-processing methods are evaluated. All three methods shows indications of different advantages when handling class imbalances. For each pre-processed data, the LR-model has

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Canillas, Rémi. "Privacy and Security in a B2B environment : Focus on Supplier Impersonation Fraud Detection using Data Analysis." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI118.

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La fraude au fournisseur (Supplier Impersonation Fraud, SIF) est un type de fraude se produisant dans un contexte Business-to-Business (B2B), où des entreprises et des commerces interagissent entre eux, plutôt qu'avec le consommateur. Une fraude au fournisseur est effectuée lorsqu'une entreprise (fournisseur) proposant des biens ou des services à une autre entreprise (client) a son identité usurpée par un fraudeur. Dans cette thèse, nous proposons, d'utiliser les techniques et outils récents en matière d'apprentissage machine (Machine Learning) afin de résoudre à ces différents points, en élab

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Elmir, Ahmad. "PaySim Financial Simulator : PaySim Financial Simulator." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14061.

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The lack of legitimate datasets on mobile money transactions toperform research on in the domain of fraud detection is a big prob-lem today in the scientic community. Part of the problem is theintrinsic private nature of mobile transactions, not much infor-mation can be exploited. This will leave the researchers with theburden of rst harnessing the dataset before performing the actualresearch on it. The dataset corresponds to the set of data in whichthe research is to be performed on. This thesis discusses a solutionto such a problem, namely the Paysim simulator. Paysim is a -nancial simulator

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Jenkins, Donald J. "Evaluation of fraud detection data mining used in the auditing process of the Defense Finance And Accounting Service." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FJenkins.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2002.<br>Thesis advisor(s): Samuel E. Buttrey, Lyn R. Whitaker. Includes bibliographical references (p. 103-105). Also available online.

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Minhas, Saliha Z. "A corpus driven computational intelligence framework for deception detection in financial text." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25345.

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Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud

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Knuth, Tobias. "Fraud prevention in the B2C e-Commerce mail order business : a framework for an economic perspective on data mining." Thesis, Edinburgh Napier University, 2018. http://researchrepository.napier.ac.uk/Output/1256175.

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A remarkable gap exists between the financial impact of fraud in the B2C e-commerce mail order business and the amount of research conducted in this area — whether it be qualitative or quantitative research about fraud prevention. Projecting published fraud rates of only approx. one percent to e-commerce sales data, the affected sales volume amounts to $651 million in the German market, and in the North American market, the volume amounts to $5.22 billion; empirical data, however, indicate even higher fraud rates. Low profit margins amplify the financial damage caused by fraudulent activities.

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Kemp, Steven. "Fraud against individuals in the Internet era: trends, victimisation, impact and reporting." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/671164.

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This thesis aims to examine fraud against individuals in the Internet era to shed light on: current trends and the dark figure of fraud; factores associated with victimisation; how fraud impacts individuals; and the factores that may influence and motivate fraud reporting. A variety of data sources are used and both descriptive statistics and a varierty of regression analyses are employed to acheive this aim. The results show that the dark figure of fraud is extensive and a number of factors are related to reporting and the motives for not reporting. Moreover, victimisation is extensive throug

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Paredes, Leandro Rocío Margaret. "An internal fraud model for operational losses : an application to evaluate data integration techniques in operational risk management in financial institutions." Doctoral thesis, Pontificia Universidad Católica del Perú, 2016. http://tesis.pucp.edu.pe/repositorio/handle/123456789/7998.

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xv, 153 h. : il. ; 30 cm<br>The handling of external operational loss data by individual banks is one of the longstanding problems in risk management theory and practice. The extant literature has not provided a method to identify the best way to combine internal and external operational loss data to calculate operational risk capital. Hence, to improve the knowledge and understanding of internal-external data combination in operational risk management, this study applied a simulation-based evaluation of well-known data combination techniques such as the scaling, the Bayesian, and the co

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Evett, Chantal. "What are the Potential Impacts of Big Data, Artificial Intelligence and Machine Learning on the Auditing Profession?" Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1642.

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To maintain public confidence in the financial system, it is essential that most financial fraud is prevented and that incidents of fraud are detected and punished. The responsibility of uncovering creatively implemented fraud is placed, in a large part, on auditors. Recent advancements in technology are helping auditors turn the tide against fraudsters. Big Data, made possible by the proliferation, widespread availability and amalgamation of diverse digital data sets, has become an important driver of technological change. Big Data analytics are already transforming the traditional audit. Sam

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Zhang, Angang. "Some Advances in Classifying and Modeling Complex Data." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/77958.

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In statistical methodology of analyzing data, two of the most commonly used techniques are classification and regression modeling. As scientific technology progresses rapidly, complex data often occurs and requires novel classification and regression modeling methodologies according to the data structure. In this dissertation, I mainly focus on developing a few approaches for analyzing the data with complex structures. Classification problems commonly occur in many areas such as biomedical, marketing, sociology and image recognition. Among various classification methods, linear classifiers ha

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Rahman, Mahmudur. "Data Verifications for Online Social Networks." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2299.

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Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from mobile devices. The value and impact of social media makes it however an attractive attack target. In this thesis, we focus on the following social media vulnerabilities. First, review centered social networks such as Yelp and Google Play have been shown to be the targets of significant search rank and malware proliferation attacks. Detecting fraudulent behaviors is thus paramount to prevent not only public opinion bias, but

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Islam, Md Zahidul. "A Cloud Based Platform for Big Data Science." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103700.

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With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managin

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Cavina, Eugenio. "GEAR: una piattaforma Big Data per l'elaborazione di stream di dati attraverso Machine Learning e Business Rules." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20416/.

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Nel panorama digitale degli ultimi anni, l’utilizzo dei pagamenti elettronici è in continua crescita. La conseguente delibera di regolamentazioni per la tutela dei consumatori implica anche l’utilizzo di sistemi di Online Fraud Detection (OFD) in grado di gestire grandi quantità di dati. La tesi parte dai sistemi OFD (tipicamente proprietari) per creare una soluzione più generale rispetto al dominio specifico e con tecnologie open source. I sistemi di OFD presentano infatti caratteristiche generalizzabili e riutilizzabili in domini applicativi diversi dai pagamenti elettronici e queste sono st

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Younis, Zaki Mohamed. "An ontological approach for monitoring and surveillance systems in unregulated markets." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/an-ontological-approach-for-monitoring-and-surveillance-systems-in-unregulated-markets(056f8010-08b2-4eb0-a75d-5301b899ec90).html.

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Ontologies are a key factor of Information management as they provide a common representation to any domain. Historically, finance domain has suffered from a lack of efficiency in managing vast amounts of financial data, a lack of communication and knowledge sharing between analysts. Particularly, with the growth of fraud in financial markets, cases are challenging, complex, and involve a huge volume of information. Gathering facts and evidence is often complex. Thus, the impetus for building a financial fraud ontology arises from the continuous improvement and development of financial market

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Ravaglia, Alex. "Riconoscimento di frodi attraverso la modellazione del comportamento degli utenti." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23107/.

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Lo scopo principale di questa tesi è quello di voler mostrare un approccio per il riconoscimento di frodi bancarie. Vengono descritte e fornite soluzioni per diversi problemi, tra i quali la manipolazione di dataset con distribuzioni sbilanciate dei dati tra le classi. Vengono addestrati e testati diversi algoritmi di classificazione dimostrando come Random Forest e XGBoost siano gli approcci migliori. Un aspetto principale che permette di migliorare notevolmente i risultati ottenuti è quello di basare la classificazione sul comportamento degli utenti. Viene mostrato come sia possibile tramite

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Vilar, Fabien. "Reasoning about big data flows : TOM4A recursive abstraction based problem solving method." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0667/document.

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Ce document concerne le développement d'un cadre mathématique spécifiant une technologie capable de prendre en charge quelques unes des problématiques relevant du domaine des grands flux de données. Nous proposons de combiner le point de vue ontologique de Newell et celui épistémologique de Floridi d'abstraction pour construire des outils de transformation de modéles au moyen d'un ensemble adéquats de foncteurs au sens de la théorie des catégories de Samuel Eilenberg et Saunders Mac Lane. La méthode de résolution de problème proposée est basée sur un raisonnement d'abstraction temps réel qui p

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Chau, Dueng Horng. "Data Mining Meets HCI: Making Sense of Large Graphs." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/94.

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We have entered the age of big data. Massive datasets are now common in science, government and enterprises. Yet, making sense of these data remains a fundamental challenge. Where do we start our analysis? Where to go next? How to visualize our findings? We answers these questions by bridging Data Mining and Human- Computer Interaction (HCI) to create tools for making sense of graphs with billions of nodes and edges, focusing on: (1) Attention Routing: we introduce this idea, based on anomaly detection, that automatically draws people’s attention to interesting areas of the graph to start thei

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