Download or read book Review on Credit Card Fraud Detection Using Data Mining Classification Techniques Machine Learning Algorithms written by Rahul Goyal and published by . This book was released on 2020 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining (DM) involves a core algorithm that enables data deeper than basic insights and knowledge. In fact, data mining is more part of knowledge discovery process. Credit card (CC) providers provide multiple cards to their customers. All credit card users must be genuine and sincere. Giving a card to any kind of mistake can lead to a financial crisis. Due to the rapid growth in cashless transactions, it is unlikely, Fake transactions can also be increased. A fraudulent transaction can be identified by studying credit cards of various behaviors as a previous transaction history data set. If there is any deviation from the available cost pattern, it is a bogus transaction. DM & machine learning techniques (MLT) are widely applied in credit card fraud detection (CCFD). In this survey paper we show an indication of various widely available DM & MLT for detecting credit card fraud.
Download or read book Data mining techniques in financial fraud detection written by Rohan Ahmed and published by GRIN Verlag. This book was released on 2018-05-24 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2016 in the subject Computer Science - General, grade: 1.7, Heilbronn University, language: English, abstract: In this seminar thesis you will get a view about the Data Mining techniques in financial fraud detection. Financial Fraud is taking a big issue in economical problem, which is still growing. So there is a big interest to detect fraud, but by large amounts of data, this is difficult. Therefore, many data mining techniques are repeatedly used to detect frauds in fraudulent activities. Majority of fraud area are Insurance, Banking, Health and Financial Statement Fraud. The most widely used data mining techniques are Support Vector Machines (SVM), Decision Trees (DT), Logistic Regression (LR), Naives Bayes, Bayesian Belief Network, Classification and Regression Tree (CART) etc. These techniques existed for many years and are used repeatedly to develop a fraud detection system or for analyze frauds.
Download or read book Data Mining and Analysis written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2014-05-12 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
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 Red Wired written by Shermon So and published by Marshall Cavendish International Asia Pte Ltd. This book was released on 2010-01-28 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: China now contains over 250 million Internet users, the largest in the world, and growing. Fortunes have been made, but more importantly, society and business are being transformed along the unique lines of Chinese Internet development. This will substantially affect the business and political character of the fastest growing economic power in the world. Red Wired takes a fascinating inside look at how China has adopted the Internet at rapid pace. Through unique access to the key players in China’s Internet revolution, the authors offer a new perspective on the growth of this superpower and the role that technology has played. Moreover, they offer business lessons from Internet companies which succeeded in this most complex and unique of markets.
Download or read book Fraud Detection in White Collar Crime written by Rohan Ahmed and published by GRIN Verlag. This book was released on 2018-06-28 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bachelor Thesis from the year 2017 in the subject Computer Science - Commercial Information Technology, grade: 1.3, Heilbronn University, language: English, abstract: White-collar crime is and has always been an urgent issue for the society. In recent years, white-collar crime has increased dramatically by technological advances. The studies show that companies are affected annually by corruption, balance-sheet manipulation, embezzlement, criminal insolvency and other economic crimes. The companies are usually unable to identify the damage caused by fraudulent activities. To prevent fraud, companies have the opportunity to use intelligent IT approaches. The data analyst or the investigator can use the data which is stored digitally in today’s world to detect fraud. In the age of Big Data, digital information is increasing enormously. Storage is cheap today and no longer a limited medium. The estimates assume that today up to 80 percent of all operational information is stored in the form of unstructured text documents. This bachelor thesis examines Data Mining and Text Mining as intelligent IT approaches for fraud detection in white-collar crime. Text Mining is related to Data Mining. For a differentiation, the source of the information and the structure is important. Text Mining is mainly concerned with weak- or unstructured data, while Data Mining often relies on structured sources. At the beginning of this bachelor thesis, an insight is first given on white-collar crime. For this purpose, the three essential tasks of a fraud management are discussed. Based on the fraud triangle of Cressey it is showed which conditions need to come together so that an offender commits a fraudulent act. Following, some well-known types of white-collar crime are considered in more detail. Text Mining approach was used to demonstrate how to extract potentially useful knowledge from unstructured text. For this purpose, two self-generated e-mails were converted into struc-tured format. Moreover, a case study will be conducted on fraud detection in credit card da-taset. The dataset contains legitimate and fraudulent transactions. Based on a literature research, Data Mining techniques are selected and then applied on the dataset by using various sampling techniques and hyperparameter optimization with the goal to identify correctly pre-dicted fraudulent transactions. The CRISP-DM reference model was used as a methodical procedure.
Download or read book Machine Learning Approach for Credit Card Fraud Detection KNN Na ve Bayes written by Darshan Kaur and published by . This book was released on 2020 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: The extraction of the useful information from the raw data is done a technique known as data mining. The prediction of new things from the current data has been done using the prediction analysis which is the application of data mining. Classifications techniques are most commonly used which are implemented for the prediction analysis. Hence, prediction of the credit card fraud detection is the main objective of this work. Author proposed various credit card fraud detection mechanisms and techniques to prevent and detect fraud timely. The fundamental of the proposed technique in the base paper is based on the conventional neural networks. This system drives the new values and learns from the previous experiences. For the detection of the credit card fraud, hybrid of KNN and naïve bayes classifier is proposed in this research work using which input data is classified into normal and fraud transactions. Test and training sets are the two sub-parts of the input data. In terms of precision and recall, the normal and fraud transactions have been predicted on the basis of test and training sets.
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 Innovations in Neural Information Paradigms and Applications written by Monica Bianchini and published by Springer Science & Business Media. This book was released on 2009-10-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tremendous advances in all disciplines including engineering, science, health care, business, avionics, management, and so on, can also be attributed to the development of artificial intelligence paradigms. In fact, researchers are always interested in desi- ing machines which can mimic the human behaviour in a limited way. Therefore, the study of neural information processing paradigms have generated great interest among researchers, in that machine learning, borrowing features from human intelligence and applying them as algorithms in a computer friendly way, involves not only Mathem- ics and Computer Science but also Biology, Psychology, Cognition and Philosophy (among many other disciplines). Generally speaking, computers are fundamentally well-suited for performing au- matic computations, based on fixed, programmed rules, i.e. in facing efficiently and reliably monotonous tasks, often extremely time-consuming from a human point of view. Nevertheless, unlike humans, computers have troubles in understanding specific situations, and adapting to new working environments. Artificial intelligence and, in particular, machine learning techniques aim at improving computers behaviour in tackling such complex tasks. On the other hand, humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial intelligence can help us understanding this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.
Download or read book Pattern Recognition and Machine Intelligence written by Rajat K. De and published by Springer Science & Business Media. This book was released on 2007-11-29 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Conference on Pattern Recognition and Machine Intelligence, PReMI 2007, held in Kolkata, India in December 2007. The 82 revised papers presented were carefully reviewed and selected from 241 submissions. The papers are organized in topical sections on pattern recognition, image analysis, soft computing and applications, data mining and knowledge discovery, bioinformatics, signal and speech processing, document analysis and text mining, biometrics, and video analysis.
Download or read book Global Electronic Commerce written by J. Christopher Westland and published by MIT Press. This book was released on 1999 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an understanding of the technologies of electronic commerce. The text does not concentrate solely on the Internet but suggests that the Internet is only a bridge technology. Each chapter contains an overview of a theory or practice followed by one or more business case studies.
Download or read book Proceedings of International Conference on Recent Trends in Computing written by Rajendra Prasad Mahapatra and published by Springer Nature. This book was released on 2023-03-20 with total page 837 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of high-quality peer-reviewed research papers presented at International Conference on Recent Trends in Computing (ICRTC 2022) held at SRM Institute of Science and Technology, Ghaziabad, Delhi, India, during 3 – 4 June 2022. The book discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques. The book presents original works from researchers from academic and industry in the field of networking, security, big data and the Internet of things.
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 New Paradigm in Decision Science and Management written by Srikanta Patnaik and published by Springer Nature. This book was released on 2019-09-20 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses an emerging area in computer science, IT and management, i.e., decision sciences and management. It includes studies that employ various computing techniques like machine learning to generate insights from huge amounts of available data; and which explore decision-making for cross-platforms that contain heterogeneous data associated with complex assets; leadership; and team coordination. It also reveals the advantages of using decision sciences with management-oriented problems. The book includes a selection of the best papers presented at the International Conference on Decision Science and Management 2018 (ICDSM 2018), held at the Interscience Institute of Management and Technology (IIMT), Bhubaneswar, India.
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 2018-05-01 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 Soft Computing for Intelligent Systems written by Nikhil Marriwala and published by Springer Nature. This book was released on 2021-06-22 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents high-quality research papers presented at the International Conference on Soft Computing for Intelligent Systems (SCIS 2020), held during 18–20 December 2020 at University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India. The book encompasses all branches of artificial intelligence, computational sciences and machine learning which is based on computation at some level such as AI-based Internet of things, sensor networks, robotics, intelligent diabetic retinopathy, intelligent cancer genes analysis using computer vision, evolutionary algorithms, fuzzy systems, medical automatic identification intelligence system and applications in agriculture, health care, smart grid and instrumentation systems. The book is helpful for educators, researchers and developers working in the area of recent advances and upcoming technologies utilizing computational sciences in signal processing, imaging, computing, instrumentation, artificial intelligence and their applications.
Download or read book Advances in Production Management Systems Artificial Intelligence for Sustainable and Resilient Production Systems written by Alexandre Dolgui and published by Springer Nature. This book was released on 2021-08-31 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.* The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections: Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19 Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains *The conference was held online.
