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| Author | : Yogita Goyal |
| Publisher | : |
| Total Pages | : 44 |
| Release | : 2020-07-28 |
| Genre | : Computers |
| ISBN | : 9781952751424 |
| Author | : |
| Publisher | : |
| Total Pages | : 70 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Advancements in the modern age have brought many conveniences, one of those being credit cards. Providing an individual the ability to hold their entire purchasing power in the form of pocket-sized plastic cards have made credit cards the preferred method to complete financial transactions. However, these systems are not infallible and may provide criminals and other bad actors the opportunity to abuse them. Financial institutions and their customers lose billions of dollars every year to credit card fraud. To combat this issue, fraud detection systems are deployed to discover fraudulent activity after they have occurred. Such systems rely on advanced machine learning techniques and other supportive algorithms to detect and prevent fraud in the future. This work analyzes the various machine learning techniques for their ability to efficiently detect fraud and explores additional state-of-the-art techniques to assist with their performance. This work also proposes a generalized strategy to detect fraud regardless of a dataset's features or unique characteristics. The high performing models discovered through this generalized strategy lay the foundation to build additional models based on state-of-the-art methods. This work expands on the issues of fraud detection, such as missing data and unbalanced datasets, and highlights models that combat these issues. Furthermore, state-of-the-art techniques, such as adapting to concept drift, are employed to combat fraud adaptation.
| Author | : Marvin Namanda |
| Publisher | : GRIN Verlag |
| Total Pages | : 15 |
| Release | : 2016-05-20 |
| Genre | : Business & Economics |
| ISBN | : 3668222584 |
Research Paper (undergraduate) from the year 2016 in the subject Business economics - Information Management, grade: 1, Federation University Australia, course: ITECH1006, language: English, abstract: Fraud is a contemporary ethical issue whose complexity is growing by day. The aims of this study are to identify the types of credit card fraud and to stipulate the future issues with the sector. The minor aim is to compare and analyze recent publication findings in future issues with credit card fraud detection. The significance of this paper is to allow the appreciation of the future issues with respect to credit card fraud detection techniques.
| Author | : IEEE Staff |
| Publisher | : |
| Total Pages | : |
| Release | : 2019-05-20 |
| Genre | : |
| ISBN | : 9781538692967 |
Computer in Technical Systems, Intelligent Systems, Distributed Computing and Visualization Systems, Communication Systems, Information Systems Security, Digital Economy, Computers in Education, Microelectronics, Electronic Technology, Education
| Author | : Yvan Lucas |
| Publisher | : |
| Total Pages | : 125 |
| Release | : 2019 |
| Genre | : |
| ISBN | : |
The detection of credit card fraud has several features that make it a difficult task. First, attributes describing a transaction ignore sequential information. Secondly, purchasing behavior and fraud strategies can change over time, gradually making a decision function learned by an irrelevant classifier. We performed an exploratory analysis to quantify the day-by-day shift dataset and identified calendar periods that have different properties within the dataset. The main strategy for integrating sequential information is to create a set of attributes that are descriptive statistics obtained by aggregating cardholder transaction sequences. We used this method as a reference method for detecting credit card fraud. We have proposed a strategy for creating attributes based on Hidden Markov Models (HMMs) characterizing the transaction from different viewpoints in order to integrate a broad spectrum of sequential information within transactions. In fact, we model the authentic and fraudulent behaviors of merchants and cardholders according to two univariate characteristics: the date and the amount of transactions. Our multi-perspective approach based on HMM allows automated preprocessing of data to model temporal correlations. Experiments conducted on a large set of data from real-world credit card transactions (46 million transactions carried out by Belgian cardholders between March and May 2015) have shown that the proposed strategy for pre-processing data based on HMMs can detect more fraudulent transactions when combined with the Aggregate Data Pre-Processing strategy.
| Author | : Zhen Rong Yang |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 868 |
| Release | : 2004-08-13 |
| Genre | : Computers |
| ISBN | : 3540228810 |
This book constitutes the refereed proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004, held in Exeter, UK, in August 2004. The 124 revised full papers presented were carefully reviewed and selected from 272 submissions. The papers are organized in topical sections on bioinformatics, data mining and knowledge engineering, learning algorithms and systems, financial engineering, and agent technologies.
| Author | : Jen Grondahl Lee |
| Publisher | : Rowman & Littlefield |
| Total Pages | : 251 |
| Release | : 2017-03-17 |
| Genre | : Law |
| ISBN | : 144226800X |
Everyone is affected by credit card fraud, if they are aware of it or not. Every day there are a variety of ways that scams and fraudsters can get your card and personal information. Today so much business occurs over the Internet or via the phone where no card is present. What can start as a seemingly legitimate purchase can easily turn into fraudulent charges – or worse, sometimes a physical confrontation, when a criminal steals a credit card from a consumer who meets to pick up a product or receive a service. In Preventing Credit Card Fraud, Jen Grondahl Lee and Gini Graham Scott provide a helpful guide to protecting yourself against the threat of credit card fraud. While it may not be possible to protect yourself against all fraudsters, who have turned scamming Internet businesses into an art, these tips and techniques will help you avoid many frauds. As a growing concern in today’s world, there is a need to be better informed of what you can do to keep your personal information secure and avoid becoming a victim of credit card fraud. Preventing Credit Card Fraud is an important resource for both merchants and consumers engaged in online purchases and sales to defend themselves against fraud.
| Author | : David A. Montague |
| Publisher | : Trafford Publishing |
| Total Pages | : 218 |
| Release | : 2004 |
| Genre | : Business & Economics |
| ISBN | : 1412014603 |
Fraud is nothing new to the merchant. Since the beginning of time, man has always looked for the opportunity to defraud others - to gain goods or services without making payment. For the credit card industry, fraud is a part of doing business, and is something that is always a challenge. The merchants that are the best at preventing fraud are the ones that can adapt to change quickly. This book is written to provide information about how to prevent credit card fraud in the card-not-present space (mail order, telephone order, e-commerce). This book is meant to be an introduction to combating fraud, providing the basic concepts around credit card payment, the ways fraud is perpetrated, along with write ups that define and provide best practices on the use of 32 fraud-prevention techniques. 32 Detailed Fraud Prevention Techniques How to catch the Chameleon on the web Top 10 rules to prevent credit card fraud Understand common fraud schemes The one Fraud Prevention Technique no merchant can afford not to do Details on over 40 Vendors that sell fraud prevention tools and services, along with how to build it in-house Learn the anatomy of a Fraud Prevention Strategy
| Author | : Bart Baesens |
| Publisher | : John Wiley & Sons |
| Total Pages | : 406 |
| Release | : 2015-08-17 |
| Genre | : Computers |
| ISBN | : 1119133122 |
Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.
| Author | : Luca Oneto |
| Publisher | : Springer |
| Total Pages | : 392 |
| Release | : 2019-04-02 |
| Genre | : Computers |
| ISBN | : 3030168417 |
This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.
