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| fraud detection machine learning case study: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke, 2015-08-17 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. |
| fraud detection machine learning case study: The Fight Against Fraud , 2004 |
| fraud detection machine learning case study: Deep Learning Neural Networks: Design And Case Studies Daniel Graupe, 2016-07-07 Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance.This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research. |
| fraud detection machine learning case study: Machine Learning and Data Science Blueprints for Finance Hariom Tatsat, Sahil Puri, Brad Lookabaugh, 2020-10-01 Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations |
| fraud detection machine learning case study: Applied Analytics through Case Studies Using SAS and R Deepti Gupta, 2018-08-03 Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniques Create and apply analytical strategies Who This Book Is For Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling. |
| fraud detection machine learning case study: Investigative Data Mining for Security and Criminal Detection Jesus Mena, 2003 Publisher Description |
| fraud detection machine learning case study: Machine Learning and Data Science Blueprints for Finance Hariom Tatsat, Sahil Puri, Brad Lookabaugh, 2020-10-01 Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations |
| fraud detection machine learning case study: Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security Sudeep Tanwar, Slawomir T. Wierzchon, Pradeep Kumar Singh, Maria Ganzha, Gregory Epiphaniou, 2023-07-01 This book features selected research papers presented at the Fourth International Conference on Computing, Communications, and Cyber-Security (IC4S 2022), organized in Ghaziabad India, during October 21–22, 2022. The conference was hosted at KEC Ghaziabad in collaboration with WSG Poland, SFU Russia, & CSRL India. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues. |
| fraud detection machine learning case study: Machine Learning Forensics for Law Enforcement, Security, and Intelligence Jesus Mena, 2016-04-19 Increasingly, crimes and fraud are digital in nature, occurring at breakneck speed and encompassing large volumes of data. To combat this unlawful activity, knowledge about the use of machine learning technology and software is critical. Machine Learning Forensics for Law Enforcement, Security, and Intelligence integrates an assortment of deductive |
| fraud detection machine learning case study: Databricks Certified Associate Developer for Apache Spark Using Python Saba Shah, 2024-06-14 Learn the concepts and exercises needed to confidently prepare for the Databricks Associate Developer for Apache Spark 3.0 exam and validate your Spark skills with an industry-recognized credential Key Features Understand the fundamentals of Apache Spark to design robust and fast Spark applications Explore various data manipulation components for each phase of your data engineering project Prepare for the certification exam with sample questions and mock exams Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionSpark has become a de facto standard for big data processing. Migrating data processing to Spark saves resources, streamlines your business focus, and modernizes workloads, creating new business opportunities through Spark’s advanced capabilities. Written by a senior solutions architect at Databricks, with experience in leading data science and data engineering teams in Fortune 500s as well as startups, this book is your exhaustive guide to achieving the Databricks Certified Associate Developer for Apache Spark certification on your first attempt. You’ll explore the core components of Apache Spark, its architecture, and its optimization, while familiarizing yourself with the Spark DataFrame API and its components needed for data manipulation. You’ll also find out what Spark streaming is and why it’s important for modern data stacks, before learning about machine learning in Spark and its different use cases. What’s more, you’ll discover sample questions at the end of each section along with two mock exams to help you prepare for the certification exam. By the end of this book, you’ll know what to expect in the exam and gain enough understanding of Spark and its tools to pass the exam. You’ll also be able to apply this knowledge in a real-world setting and take your skillset to the next level.What you will learn Create and manipulate SQL queries in Apache Spark Build complex Spark functions using Spark's user-defined functions (UDFs) Architect big data apps with Spark fundamentals for optimal design Apply techniques to manipulate and optimize big data applications Develop real-time or near-real-time applications using Spark Streaming Work with Apache Spark for machine learning applications Who this book is for This book is for data professionals such as data engineers, data analysts, BI developers, and data scientists looking for a comprehensive resource to achieve Databricks Certified Associate Developer certification, as well as for individuals who want to venture into the world of big data and data engineering. Although working knowledge of Python is required, no prior knowledge of Spark is necessary. Additionally, experience with Pyspark will be beneficial. |
| fraud detection machine learning case study: Google Cloud AI and Machine Learning Certification , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| fraud detection machine learning case study: Machine Learning Essentials and Applications Mrs. N. Jayasri, Dr. Saiyed Faiayaz Waris, Mr. Drumil Joshi, Mrs. P. Revathy, 2024-07-27 Machine Learning Essentials and Applications a comprehensive of machine learning's core principles, methodologies, and real-world applications. This book is designed for both beginners and professionals, covering essential topics like supervised and unsupervised learning, neural networks, and deep learning. With clear explanations and practical examples, it connects theory to practice, showcasing machine learning’s impact across industries such as healthcare, finance, and technology. Ideal for readers seeking foundational knowledge and insights into the transformative potential of machine learning in various fields. |
| fraud detection machine learning case study: Machine Learning Algorithms and Techniques Krishna Bonagiri, 2024-06-21 Machine Learning Algorithms and Techniques the concepts, popular algorithms, and essential techniques of machine learning. A comprehensive covering supervised, unsupervised, and reinforcement learning methods while exploring key algorithms like decision trees, neural networks, clustering, and more. Practical applications and examples bring each algorithm to life, helping readers understand how these models are used to solve real-world problems. Designed for both beginners and experienced practitioners, this book is an ideal guide for mastering the fundamentals and applications of machine learning. |
| fraud detection machine learning case study: Image Processing and Capsule Networks Joy Iong-Zong Chen, João Manuel R. S. Tavares, Subarna Shakya, Abdullah M. Iliyasu, 2020-07-23 This book emphasizes the emerging building block of image processing domain, which is known as capsule networks for performing deep image recognition and processing for next-generation imaging science. Recent years have witnessed the continuous development of technologies and methodologies related to image processing, analysis and 3D modeling which have been implemented in the field of computer and image vision. The significant development of these technologies has led to an efficient solution called capsule networks [CapsNet] to solve the intricate challenges in recognizing complex image poses, visual tasks, and object deformation. Moreover, the breakneck growth of computation complexities and computing efficiency has initiated the significant developments of the effective and sophisticated capsule network algorithms and artificial intelligence [AI] tools into existence. The main contribution of this book is to explain and summarize the significant state-of-the-art research advances in the areas of capsule network [CapsNet] algorithms and architectures with real-time implications in the areas of image detection, remote sensing, biomedical image analysis, computer communications, machine vision, Internet of things, and data analytics techniques. |
| fraud detection machine learning case study: Machine Learning Algorithms and Techniques SURESH KOTTUR, 2024-08-01 Machine Learning Algorithms and Techniques the foundational algorithms and advanced techniques of machine learning, designed to empower readers in building intelligent, data-driven applications. Covering a wide array of algorithms—supervised, unsupervised, and reinforcement learning offers in-depth explanations, real-world examples, and practical applications. Whether you’re a beginner or an experienced practitioner, this guide provides a clear understanding of core concepts, optimization strategies, and performance evaluation methods, equipping you with essential skills for navigating the dynamic field of machine learning. |
| fraud detection machine learning case study: 50 Algorithms Every Programmer Should Know Imran Ahmad, 2023-09-29 Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Familiarize yourself with advanced deep learning architectures Explore newer topics, such as handling hidden bias in data and algorithm explainability Get to grips with different programming algorithms and choose the right data structures for their optimal implementation Book DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learn Design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples Create a recommendation engine that suggests relevant movies to subscribers Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs Who this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary. |
| fraud detection machine learning case study: The AI Revolution: How Artificial Intelligence Will Reshape Our Lives, Careers, and Future Rick Spair, Welcome to The AI Revolution: How Artificial Intelligence Will Reshape Our Lives, Careers, and Future, a comprehensive exploration of one of the most transformative technologies of our time. Artificial Intelligence (AI) is not just a buzzword or a distant futuristic concept; it is a reality that is rapidly reshaping every facet of our lives. From the way we communicate, work, and learn to how we address global challenges, AI is at the forefront of innovation and change. As you delve into this book, you will embark on a journey through the history, development, and profound impact of AI. We will explore the foundational concepts that underpin AI technologies, demystify the jargon that often surrounds this field, and provide a clear understanding of how AI works. More importantly, we will examine the real-world applications of AI across various sectors, highlighting the benefits and challenges that come with integrating AI into our daily lives. The narrative will take you through the corridors of healthcare, where AI is revolutionizing diagnostics and treatment; into the financial world, where it is enhancing fraud detection and customer service; and onto the roads, where autonomous vehicles are becoming a reality. You will see how AI is personalizing education, transforming entertainment, and optimizing retail experiences. Each chapter is designed to provide insights into how AI is currently being utilized and the future possibilities it holds. Beyond the technological advancements, this book delves into the ethical considerations and societal impacts of AI. We will discuss the moral dilemmas, privacy concerns, and the need for transparency and accountability in AI development. Understanding these aspects is crucial for fostering a responsible AI ecosystem that benefits all of humanity. In the chapters dedicated to the future of work, you will learn about the skills and competencies required in an AI-driven job market. We will explore the opportunities and challenges posed by job automation and the importance of continuous learning and adaptability. This book aims to equip you with the knowledge to navigate and thrive in a rapidly changing world. We will also address the vital role of individuals, businesses, and governments in shaping the future of AI. From fostering innovation and ensuring ethical practices to promoting inclusivity and equity, the collective efforts of all stakeholders are essential for creating a balanced and beneficial AI landscape. The AI Revolution: How Artificial Intelligence Will Reshape Our Lives, Careers, and Future is not just an academic discourse but a call to action. It encourages readers to engage with AI positively, responsibly, and proactively. As we stand on the brink of this technological revolution, it is imperative to understand its implications and harness its potential to create a better, more equitable world. Join us as we explore the fascinating world of AI, understand its transformative power, and envision a future where technology and humanity coexist harmoniously for the greater good. |
| fraud detection machine learning case study: Ensemble Machine Learning Cha Zhang, Yunqian Ma, 2012-02-17 It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike. |
| fraud detection machine learning case study: Artificial Intelligence and Machine Learning for Sustainable Development Pawan Whig, Pavika Sharma, Nagender Aneja, Ahmed A. Elngar, Nuno Silva, 2024-12-18 Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development. The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future. |
| fraud detection machine learning case study: Study Guide to Endpoint Security , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| fraud detection machine learning case study: Intelligent Data Engineering and Analytics Vikrant Bhateja, Xin-She Yang, Jerry Chun-Wei Lin, Ranjita Das, 2023-02-23 The book presents the proceedings of the 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022), held at NIT Mizoram, Aizawl, Mizoram, India during 18 – 19 June 2022. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. These proceedings are divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This volume is a valuable resource for postgraduate students in various engineering disciplines. |
| fraud detection machine learning case study: Google Cloud Professional Data Engineer , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| fraud detection machine learning case study: Machine Learning Applications Using Python Puneet Mathur, 2018-12-12 Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will LearnDiscover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals. |
| fraud detection machine learning case study: Mastering Machine Learning: Practical Applications Across Industries Vijay Gupta, 2024-05-08 Mastering Machine Learning: Practical Applications Across Industries offers a comprehensive exploration of the transformative potential of machine learning (ML) across diverse sectors. From healthcare to finance, manufacturing to entertainment, this ebook delves into the practical applications and real-world case studies that showcase the power of ML in driving innovation and efficiency. Through a blend of theoretical insights and hands-on guidance, readers will embark on a journey through the fundamentals of ML techniques, understanding key concepts, algorithms, and methodologies. The ebook illuminates the path from theory to practice, providing actionable strategies for implementing ML solutions in various organizational contexts. Each chapter is carefully crafted to highlight the unique challenges and opportunities present in different industries, offering in-depth analyses of successful ML implementations and the lessons learned along the way. From predicting patient outcomes in healthcare to optimizing financial portfolios in banking, readers will discover how ML is revolutionizing decision-making processes and reshaping business landscapes. Moreover, Mastering Machine Learning doesn't shy away from addressing the ethical considerations inherent in ML applications. Discussions on bias, fairness, privacy, and transparency provide readers with a nuanced understanding of the social and ethical implications of ML adoption, empowering them to navigate these complex issues responsibly. Whether you're a seasoned data scientist looking to expand your expertise or a business leader seeking to leverage ML for strategic advantage, this ebook serves as an indispensable guide. Packed with insights, case studies, and practical tips, Mastering Machine Learning equips readers with the knowledge and tools needed to harness the full potential of ML across industries and drive meaningful impact in an increasingly data-driven world. |
| fraud detection machine learning case study: Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security Hassan, Ahdi, Prasad, Vivek Kumar, Bhattacharya, Pronaya, Dutta, Pushan Kumar, Damaševi?ius, Robertas, 2024-02-14 The Healthcare sector is experiencing a mindset change with the advent of Healthcare 5.0, bringing forth improved patient care and system efficiency. However, this transformation poses significant challenges. The growing digitization of healthcare systems raises concerns about the security and privacy of patient data, making seamless data sharing and collaboration increasingly complex tasks. Additionally, as the volume of healthcare data expands exponentially, efficient handling and analysis become vital for optimizing healthcare delivery and patient outcomes. Addressing these multifaceted issues is crucial for healthcare professionals, IT experts, data scientists, and researchers seeking to fully harness the potential of Healthcare 5.0. Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security presents a comprehensive solution to the pressing challenges in the digitalized healthcare industry. This research book dives into the principles of Healthcare 5.0 and explores practical implementation through cloud computing, data analytics, and federated learning. Readers will gain profound insights into the role of cloud computing in managing vast amounts of healthcare data, such as electronic health records and real-time analytics. Cloud-based frameworks, architectures, and relevant use cases are explored to optimize healthcare delivery and improve patient outcomes. |
| fraud detection machine learning case study: Machine Learning and Knowledge Discovery in Databases Wray Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor, 2009-08-27 This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques. |
| fraud detection machine learning case study: Methods of Strategic Trade Analysis Christopher Nelson, 2022-12-04 This book addresses ways that governments, international organizations, and other stakeholders can utilize data to uncover illicit trade in materials and equipment that could be used to support chemical, biological, nuclear, and advanced conventional weapons systems. Key concepts of strategic trade are introduced, including examples of strategic goods and their potential uses in weapons of mass destruction (WMDs) and weapons systems, the interplay between the Harmonized System and strategic trade control regimes, and the data available for analysis in the field. Innovative, yet practical methodologies to analyze strategic trade cover the use of crime scripts, risk assessment indicators, mirror statistics, market share analysis, and transshipment and re-export analysis. There are also chapters on leading-edge techniques involving machine learning and network analysis that have shown promise in other areas of crime and illicit trade investigations. Each chapter provides step-by-step instructions on applying the technique, numerous case studies and examples, and discussions of the strengths and weaknesses of each approach. This volume is designed to provide all types of analysts with practical pathways for understanding, detecting, and disrupting illicit procurement of materials and equipment needed to produce WMDs and advanced weapons. |
| fraud detection machine learning case study: Machine Intelligence Techniques for Data Analysis and Signal Processing Dilip Singh Sisodia, Lalit Garg, Ram Bilas Pachori, M. Tanveer, 2023-05-30 This book comprises the proceedings of the 4th International Conference on Machine Intelligence and Signal Processing (MISP2022). The contents of this book focus on research advancements in machine intelligence, signal processing, and applications. The book covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. It also includes the progress in signal processing to process the normal and abnormal categories of real-world signals such as signals generated from IoT devices, smart systems, speech, and videos and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), electromyogram (EMG), etc. This book proves a valuable resource for those in academia and industry. |
| fraud detection machine learning case study: Progress in Advanced Computing and Intelligent Engineering Chhabi Rani Panigrahi, Bibudhendu Pati, Binod Kumar Pattanayak, Seeven Amic, Kuan-Ching Li, 2021-04-15 This book focuses on theory, practice and applications in the broad areas of advanced computing techniques and intelligent engineering. This book includes 74 scholarly articles which were accepted for presentation from 294 submissions in the 5th ICACIE during 25–27 June 2020 at Université des Mascareignes (UdM), Mauritius, in collaboration with Rama Devi Women’s University, Bhubaneswar, India, and S‘O’A Deemed to be University, Bhubaneswar, India. This book brings together academicians, industry persons, research scholars and students to share and disseminate their knowledge and scientific research work related to advanced computing and intelligent engineering. It helps to provide a platform to the young researchers to find the practical challenges encountered in these areas of research and the solutions adopted. The book helps to disseminate the knowledge about some innovative and active research directions in the field of advanced computing techniques and intelligent engineering, along with some current issues and applications of related topics. |
| fraud detection machine learning case study: AI-Driven Decentralized Finance and the Future of Finance Irfan, Mohammad, Elmogy, Mohammed, Gupta, Swati, Khalifa, Fahmi, Dias, Rui Teixeira, 2024-08-26 In the evolving landscape of finance, traditional institutions grapple with challenges ranging from outdated processes to limited accessibility, hindering the industry's ability to meet the diverse needs of a modern, digital-first society. Moreover, as the world embraces Decentralized Finance (DeFi) and Artificial Intelligence (AI) technologies, there becomes a need to bridge the gap between innovation and traditional financial systems. This disconnect not only impedes progress but also limits the potential for financial inclusion and sustainable growth. AI-Driven Decentralized Finance and the Future of Finance addresses the complexities and challenges currently facing the financial industry. By exploring the transformative potential of AI in decentralized finance, this book offers a roadmap for navigating the convergence of technology and finance. From optimizing smart contracts to enhancing security and personalizing financial experiences, the book provides practical insights and real-world examples that empower professionals to leverage AI-driven strategies effectively. |
| fraud detection machine learning case study: When Numbers Don’t Add Up Faisal Sheikh, 2020-12-02 The author contextualized the phenomenon of accounting fraud using a framework he developed called “Corporate Governance Cosmos.” The book contains an extensive literature review including an evaluation of the seminal theory in this area, namely, the Fraud Triangle. There is a comprehensive exploration of the motivations for accounting fraud and a growing realization that Dark Triad (psychopathy, narcissism, and machiavellianism) tendencies may explain why executives engage in accounting fraud. The author expands an established framework entitled Cooks Recipes Incentives Monitoring End results (C R I M E) by Rezaee (2005), to ‘’C R I M E L’’, where L is the “Learning” from 33 international case studies of accounting fraud. Accountants, auditors, antifraud practitioners, and graduate students will find the case studies of accounting fraud particularly useful as it makes the phenomenon tangible and more understandable. The penultimate chapter is a study of the likely impact of financial technology on accounting fraud. The author concludes by marshalling various insights including a brief discussion of ethics, forwarding his International Code of Ethics for Professional Accountants (IFAC) ‘‘Ethical Triangle’’, his vision for the future accountant, which he refers to as ‘’accounting engineers’’, and an ancient prescription for the curse of accounting fraud. |
| fraud detection machine learning case study: Managing Data Science Kirill Dubovikov, 2019-11-12 Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book. |
| fraud detection machine learning case study: Innovative Machine Learning Applications for Cryptography Ruth, J. Anitha, Vijayalakshmi, G.V. Mahesh, Visalakshi, P., Uma, R., Meenakshi, A., 2024-03-04 Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, machine learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches. |
| fraud detection machine learning case study: ROBOTS, LAUGHTER, AND UNEMPLOYMENT Young Akpasubi, 2023-06-28 This book provides an overview of the rapid advancements in AI technology, setting the stage for understanding its impact on the workforce and society. Examining the Impact of AI: It explores the benefits and concerns associated with AI adoption, discussing how different industries and job sectors are affected by automation and the potential for job displacement. Reskilling and Upskilling: The book emphasizes the importance of continuous learning and acquiring new skills to adapt to the changing demands of the AI-driven job market. It explores strategies for individuals and organizations to stay relevant and thrive in this new era. Ethical Considerations: It delves into the ethical implications of AI adoption, discussing topics such as fairness, transparency, privacy, and accountability. The book emphasizes the need for responsible AI development and highlights the importance of establishing ethical guidelines and regulations. Personal Stories of Job Displacement: Through engaging case studies, the book shares personal stories of individuals who have experienced job displacement due to AI automation. These stories provide insights into the challenges, struggles, and triumphs of individuals navigating the changing employment landscape. Redefining Work and Work-Life Balance: The book explores the evolving nature of work in the AI era, discussing topics such as flexible work arrangements, task automation, and the importance of maintaining a healthy work-life balance in a technology-driven world. Collaboration between Humans and AI: It emphasizes the collaborative approach between humans and AI, highlighting how AI technologies can augment human capabilities rather than replacing them. The book explores the potential for humans and AI to work together to achieve better outcomes. Future Implications: The book concludes by discussing the future of work in a world with AI, encouraging readers to consider the possibilities and challenges that lie ahead. It emphasizes the importance of responsible AI adoption, ongoing learning, and ethical considerations for creating a positive and inclusive future. These highlights offer a glimpse into the key themes and insights covered in this Guide to surviving the AI Revolution. It is a comprehensive exploration of the AI revolution, its impact on jobs, and the necessary adaptations individuals and organizations must make to thrive in this new era. |
| fraud detection machine learning case study: A Comprehensive Guide to Machine Learning Operations (MLOps) Rick Spair, Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, revolutionizing how businesses make decisions, automate processes, and provide innovative products and services. Yet, the successful implementation of AI and ML goes beyond developing sophisticated models. It requires the seamless integration of these models into operational workflows, ensuring their reliability, scalability, security, and ethical compliance. This integration is the heart of Machine Learning Operations or MLOps. This comprehensive guide is your passport to understanding the intricate world of MLOps. Whether you are an aspiring data scientist, a seasoned machine learning engineer, an operations professional, or a business leader, this guide is designed to equip you with the knowledge and insights needed to navigate the complexities of MLOps effectively. |
| fraud detection machine learning case study: ChatGPT and AI for Accountants Dr. Scott Dell, Dr. Mfon Akpan, 2024-06-28 Elevate your accounting skills by applying ChatGPT across audit, tax, consulting, and beyond Key Features Leverage the impact of AI on modern accounting, from audits to corporate governance Use ChatGPT to streamline your accounting tasks with practical hands-on techniques Understand the impact of AI in accounting through in-depth chapters covering various domains, including ethical considerations and data analytics Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the fast-paced AI world, accounting professionals are increasingly challenged by the complexities of AI. Many struggle to integrate these advanced tools into their workflows, leading to a sense of overwhelm. ChatGPT for Accounting bridges this gap by not only simplifying AI concepts but also offering practical insights for its application in various accounting domains. This book takes you from the foundational principles of Generative Artificial Intelligence (GAI) to its practical applications in audits, tax planning, practice management, fraud examination, financial analysis, and beyond. Each chapter equips you with essential skills, showing you how AI can revolutionize internal control systems, enhance recruitment processes, streamline marketing plans, optimize tax strategies, and boost efficiency in audits. You’ll then advance to exploring the role of AI in forensic accounting, financial analysis, managerial accounting, and corporate governance, while also addressing ethical and security implications. Concluding with a reflective outlook on the promises and challenges of AI, you’ll gain a holistic view of the future of accounting. By the end of this book, you’ll be equipped with the knowledge to harness the power of AI effectively and ethically, transforming your accounting practice and staying ahead in the ever-evolving landscape.What you will learn Understand the fundamentals of AI and its impact on the accounting sector Grasp how AI streamlines and enhances the auditing process for high accuracy Uncover the potential of AI in simplifying tax processes and ensuring compliance Get to grips with using AI to identify discrepancies and prevent financial fraud Master the art of AI-powered data analytics for informed decision-making Gain insights into seamlessly integrating AI tools within existing accounting systems Stay ahead in the evolving landscape of AI-led accounting tools and practices Who this book is for Whether you're a seasoned accounting professional, a C-suite executive, a business owner, an accounting educator, a student of accounting, or a technology enthusiast, this book provides the knowledge and insights you need to navigate the changing landscape in applying GAI technology to make a difference in all you do. An appreciation and understanding of the accounting process and concepts will be beneficial. |
| fraud detection machine learning case study: Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity Lobo, Victor, Correia, Anacleto, 2022-06-24 The growth of innovative cyber threats, many based on metamorphosing techniques, has led to security breaches and the exposure of critical information in sites that were thought to be impenetrable. The consequences of these hacking actions were, inevitably, privacy violation, data corruption, or information leaking. Machine learning and data mining techniques have significant applications in the domains of privacy protection and cybersecurity, including intrusion detection, authentication, and website defacement detection, that can help to combat these breaches. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity provides machine and deep learning methods for analysis and characterization of events regarding privacy and anomaly detection as well as for establishing predictive models for cyber attacks or privacy violations. It provides case studies of the use of these techniques and discusses the expected future developments on privacy and cybersecurity applications. Covering topics such as behavior-based authentication, machine learning attacks, and privacy preservation, this book is a crucial resource for IT specialists, computer engineers, industry professionals, privacy specialists, security professionals, consultants, researchers, academicians, and students and educators of higher education. |
| fraud detection machine learning case study: Database Management using AI: A Comprehensive Guide A Purushotham Reddy, 2024-10-20 Database Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age. |
| fraud detection machine learning case study: Statistical Machine Learning for Engineering with Applications Jürgen Franke, |
| fraud detection machine learning case study: Tax and Technology Annika Streicher, Svitlana Buriak, 2023-10-13 The challenges and opportunities of new technologies in the tax field Technological developments induced major reforms in the regulatory international and domestic tax landscapes as well as in the developments in the use of technology by tax administrations and taxpayers. New technology, especially the innovations in virtual asset-light cross-border business organizations, data analytics, service and process automation, on one hand, disrupted the well-established legal tax principles and rules and, on the other, stimulated informed data-driven and structured solutions in tax compliance. Technological advances affected nearly every area and each aspect of taxation: Direct tax regulations, indirect tax law, and tax procedures including tax compliance, and tax control functions. International organizations such as the Organization for Economic Co-operation and Development (OECD), the United Nations (UN), and the European Commission as a supranational organization fostered critical legislative reforms and proposals among which are the OECD Two-Pillar Solution to Address the Tax Challenges Arising from Digitalisation of the Economy, Article 12B of the UN Model Tax Convention to tax automated digital services, new rules for tracing transfers of crypto-assets in the EU, as well as the EU ́s VAT e-commerce package and VAT in the Digital Age package. While these proposals aim to address a wide range of the benefits and challenges of Economy 4.0, certain questions arise concerning the consistency of the legislative developments with their initial objectives, the appropriateness of the legal form for the economic substance of the regulated relations for the effectiveness of the regulations as well as their coherence. This volume contains a collection of scientific chapters on the general topic Tax and Technology that were successfully completed by the 2022/2023 LL.M. graduates of the Institute for Austrian and International Tax Law, WU. The volume is divided into three parts that contain the contributions dealing with the impact of the technology on international tax law, indirect tax law, and procedural law. Each chapter provides an in-depth analysis of a unique research question aiming to innovatively contribute to the current debate and develop a practical approach for implementing the findings. |
Fraud: Definition, Types, and Consequences of Fraudulent Behavior
Apr 30, 2025 · Fraud is an intentional act of deceit designed to reward the perpetrator or to deny the rights of a victim. Some of the most common types of fraud involve the insurance industry, …
Fraud - Wikipedia
In law, fraud is intentional deception to deprive a victim of a legal right or to gain from a victim unlawfully or unfairly.
FRAUD Definition & Meaning - Merriam-Webster
The meaning of FRAUD is deceit, trickery; specifically : intentional perversion of truth in order to induce another to part with something of value or to surrender a legal right.
Fraud 101: What Is Fraud? - Association of Certified Fraud …
“Fraud” is any activity that relies on deception in order to achieve a gain. Fraud becomes a crime when it is a “knowing misrepresentation of the truth or concealment of a material fact to induce …
Fraud - Definition, Meaning, Types, and Examples - Legal Dictionary
Dec 1, 2014 · Fraud takes place when a person deliberately practices deception in order to gain something unlawfully or unfairly. In most states, the act of fraud can be classified as either a …
fraud | Wex | US Law | LII / Legal Information Institute
Fraud is both a civil tort and criminal wrong. In civil litigation , allegations of fraud might be based on a misrepresentation of fact that was either intentional or negligent .
Fraud - Office for Victims of Crime
Discover publications, resources, and other information about victims of fraud.
Fraud - FindLaw
Nov 23, 2023 · Fraud can take many forms. One commits fraud through false statements, misrepresentation, or dishonest conduct intended to mislead or deceive. This article looks at …
What Is Fraud? Types And Definitions - Financial Crime Academy
Jun 10, 2025 · Fraud is defined as an intentionally deceptive action intended to provide the perpetrator with an unlawful gain or to deny a victim’s right. Tax fraud, credit card fraud, wire …
Fraud | Types of Fraud Crimes & Their Penalties
3 days ago · The broad legal definition of fraud is the intentional deception of another for personal gain. A person who defrauds another deprives the victim of his or her money or property for …
Fraud: Definition, Types, and Consequences of Fraudulent Behavior
Apr 30, 2025 · Fraud is an intentional act of deceit designed to reward the perpetrator or to deny the rights of a victim. Some of the most common types of fraud involve the insurance industry, …
Fraud - Wikipedia
In law, fraud is intentional deception to deprive a victim of a legal right or to gain from a victim unlawfully or unfairly.
FRAUD Definition & Meaning - Merriam-Webster
The meaning of FRAUD is deceit, trickery; specifically : intentional perversion of truth in order to induce another to part with something of value or to surrender a legal right.
Fraud 101: What Is Fraud? - Association of Certified Fraud …
“Fraud” is any activity that relies on deception in order to achieve a gain. Fraud becomes a crime when it is a “knowing misrepresentation of the truth or concealment of a material fact to induce …
Fraud - Definition, Meaning, Types, and Examples - Legal Dictionary
Dec 1, 2014 · Fraud takes place when a person deliberately practices deception in order to gain something unlawfully or unfairly. In most states, the act of fraud can be classified as either a …
fraud | Wex | US Law | LII / Legal Information Institute
Fraud is both a civil tort and criminal wrong. In civil litigation , allegations of fraud might be based on a misrepresentation of fact that was either intentional or negligent .
Fraud - Office for Victims of Crime
Discover publications, resources, and other information about victims of fraud.
Fraud - FindLaw
Nov 23, 2023 · Fraud can take many forms. One commits fraud through false statements, misrepresentation, or dishonest conduct intended to mislead or deceive. This article looks at …
What Is Fraud? Types And Definitions - Financial Crime Academy
Jun 10, 2025 · Fraud is defined as an intentionally deceptive action intended to provide the perpetrator with an unlawful gain or to deny a victim’s right. Tax fraud, credit card fraud, wire …
Fraud | Types of Fraud Crimes & Their Penalties
3 days ago · The broad legal definition of fraud is the intentional deception of another for personal gain. A person who defrauds another deprives the victim of his or her money or property for …
