Download or read book Machine Learning Advances in Payment Card Fraud Detection written by Nick Ryman-Tubb and published by Academic Press. This book was released on 2019-06-15 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Advances in Payment Card Fraud Detection provides a thorough review of the state-of-the-art in fraud detection research that is ideal for graduate level readers and professionals. Through a comprehensive examination of fraud analytics that covers data collection, steps for cleaning and processing data, tools for analyzing data, and ways to draw insights, the book introduces state-of the-art payment fraud detection techniques. Other topics covered include machine learning techniques for the detection of fraud, including SOAR, and opportunities for future research, such as developing holistic approaches for countering fraud. Covers analytical approaches and machine learning for fraud detection Explores SOAR with full R-code and example obfuscated datasets in a freely-accessible companion website Introduces state-of the-art payment fraud detection techniques
Download or read book Credit Card Fraud Detection and Analysis Through Machine Learning written by Yogita Goyal and published by . This book was released on 2020-07-28 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book 2019 18th International Symposium INFOTEH JAHORINA INFOTEH written by IEEE Staff and published by . This book was released on 2019-03-20 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: INFOTEH gathers the experts, scientists, engineers, researchers and students that deal with information technologies and their application in control, communication, production and electronic systems, power engineering and in other border areas
Download or read book Anomaly Detection in Credit Card Transactions Using Machine Learning written by Meenu and published by . This book was released on 2020 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anomaly Detection is a method of identifying the suspicious occurrence of events and data items that could create problems for the concerned authorities. Data anomalies are usually associated with issues such as security issues, server crashes, bank fraud, building structural flaws, clinical defects, and many more. Credit card fraud has now become a massive and significant problem in today's climate of digital money. These transactions carried out with such elegance as to be similar to the legitimate one. So, this research paper aims to develop an automatic, highly efficient classifier for fraud detection that can identify fraudulent transactions on credit cards. Researchers have suggested many fraud detection methods and models, the use of different algorithms to identify fraud patterns. In this study, we review the Isolation forest, which is a machine learning technique to train the system with the help of H2O.ai. The Isolation Forest was not so much used and explored in the area of anomaly detection. The overall performance of the version evaluated primarily based on widely-accepted metrics: precision and recall. The test data used in our research come from Kaggle.
Download or read book Credit Card Fraud Detection Using Machine Learning with Integration of Contextual Knowledge written by Yvan Lucas and published by . This book was released on 2019 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning Approach to Detect Fraudulent Banking Transactions written by Riwaj Kharel and published by GRIN Verlag. This book was released on 2022-09-22 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2022 in the subject Computer Sciences - Artificial Intelligence, grade: 3, University of Applied Sciences Berlin, course: Project management and Data Science, language: English, abstract: The study investigates whether a machine learning algorithm can be used to detect fraud attempts and how a fraud management system based on machine learning might work. For fraud detection, most institutions rely on rule-based systems with manual evaluation. Until recently, these systems had been performing admirably. However, as fraudsters become more sophisticated, traditional systems' outcomes are becoming inconsistent. Fraud usually comprises many methods that are used repeatedly that's why looking for patterns is a common emphasis for fraud detection. Data analysts can, for example, avoid insurance fraud by developing algorithms that recognize trends and abnormalities. AI techniques used to detect fraud include Data mining classifies, groups, and segments data to search through millions of transactions to find patterns and detect fraud. The scientific paper discusses machine learning methods to detect fraud detection with a case study and analysis of Kaggle datasets.
Download or read book WITS 2020 written by Saad Bennani and published by Springer Nature. This book was released on 2021-07-21 with total page 1139 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents peer-reviewed articles from the 6th International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS 2020), held at Fez, Morocco. It presents original research results, new ideas and practical lessons learnt that touch on all aspects of wireless technologies, embedded and intelligent systems. WITS is an international conference that serves researchers, scholars, professionals, students and academicians looking to foster both working relationships and gain access to the latest research results. Topics covered include Telecoms & Wireless Networking Electronics & Multimedia Embedded & Intelligent Systems Renewable Energies.
Download or read book Detecting Credit Card Fraud written by and published by . This book was released on 2020 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Recent Advances in Big Data and Deep Learning written by Luca Oneto and published by Springer. This book was released on 2019-04-02 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Empowering Artificial Intelligence Through Machine Learning written by Nedunchezhian Raju and published by CRC Press. This book was released on 2022-06-16 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new volume, Empowering Artificial intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next-big-game changer in research and technology, The volume looks at how computing has enabled machines to learn, making machine and tools become smarter in many sectors, including science and engineering, healthcare, finance, education, gaming, security, and even agriculture, plus many more areas. Topics include techniques and methods in artificial intelligence for making machines intelligent, machine learning in healthcare, using machine learning for credit card fraud detection, using artificial intelligence in education using gaming and automatization with courses and outcomes mapping, and much more. The book will be valuable to professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology.
Download or read book 2020 4th International Conference on Intelligent Computing and Control Systems ICICCS written by IEEE Staff and published by . This book was released on 2020-05-13 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The advent of ever augmenting and ubiquitous computational and control resources enhanced the opportunities for developing various intelligent computational and control techniques to solve number of real time issues like uncertainties, vagueness and imprecision techniques This International Conference on Intelligent Computing and Control Systems (ICICCS 2020) organized on 13 15, May 2020 by Vaigai College Engineering (VCE), Madurai, India rapidly covers the research topics with myriad of applications for developing innovative next generation technologies Enormous number of intelligent computational and control algorithms with the increasing computational and control power of computers have significantly extended the focus of researchers and scientists on providing unprecedented innovations in intelligent computing and control systems
Download or read book The Enhancement of Credit Card Fraud Detection Systems Using Machine Learning Methodology written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Download or read book Evolution in Computational Intelligence written by Vikrant Bhateja and published by Springer Nature. This book was released on 2020-09-08 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), which aims to bring together researchers, scientists, engineers and practitioners to share new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. The book is divided into two volumes: Evolution in Computational Intelligence (Volume 1) and Intelligent Data Engineering and Analytics (Volume 2). Covering a broad range of topics in computational intelligence, the book features papers on theoretical as well as practical aspects of areas such as ANN and genetic algorithms, computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, and multi-agent systems. As such, it is a valuable reference resource for postgraduate students in various engineering disciplines.
Download or read book Advances in Artificial Intelligence and Data Engineering written by Niranjan N. Chiplunkar and published by Springer Nature. This book was released on 2020-08-13 with total page 1456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering.
Download or read book The Enhancement of Credit Card Fraud Detection Systems Using Machine Learning Methodology written by Soheila Ehramikar and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In Canada, credit card fraud occurrences rose sharply in 1998 causing $147 million in losses. To address this problem, financial institutions (FIs) are employing preventive measures and fraud detection systems one of which is called FDS. Although FDS has shown good results in reducing fraud, the majority of cases being flagged by this system are 'False Positives ' resulting in substantial investigation costs and cardholder inconvenience. The possibilities of enhancing the current operation by introducing a post processing system constitute the objective of this research. The data used for the analysis was provided by one of the major Canadian banks. Based on variations and combinations of features and training class distributions, different sets of experiments were performed to explore the influence of these parameters on the performance of the prototype developed. The results indicate that the employed approach has a very good potential to improve on the existing system. However, further research is required including the development of prototype systems which should be enhanced by more extensive and informative data.
Download or read book Credit Card Fraud Detection Using Logistic Regression and Machine Learning Algorithms written by Haoyi Cheng and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is focused on detecting the probability of credit card fraud occurrence according to seven relative independent variables by using logistic regression, support vector machine, decision tree, and k-NN models. The dataset provided by Dhanush Narayanan R from Kaggle contains one million of data [1]. The final goal is to compare these four models and find the most accurate model.
