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| fraud detection data science: 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 data science: Fraud and Fraud Detection, + Website Sunder Gee, 2014-12-03 Detect fraud faster—no matter how well hidden—with IDEA automation Fraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare's IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book. Business systems' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to: Understand the different areas of fraud and their specific detection methods Identify anomalies and risk areas using computerized techniques Develop a step-by-step plan for detecting fraud through data analytics Utilize IDEA software to automate detection and identification procedures The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification. |
| fraud detection data science: Fraud Data Analytics Methodology Leonard W. Vona, 2017-01-04 Uncover hidden fraud and red flags using efficient data analytics Fraud Data Analytics Methodology addresses the need for clear, reliable fraud detection with a solid framework for a robust data analytic plan. By combining fraud risk assessment and fraud data analytics, you'll be able to better identify and respond to the risk of fraud in your audits. Proven techniques help you identify signs of fraud hidden deep within company databases, and strategic guidance demonstrates how to build data interrogation search routines into your fraud risk assessment to locate red flags and fraudulent transactions. These methodologies require no advanced software skills, and are easily implemented and integrated into any existing audit program. Professional standards now require all audits to include data analytics, and this informative guide shows you how to leverage this critical tool for recognizing fraud in today's core business systems. Fraud cannot be detected through audit unless the sample contains a fraudulent transaction. This book explores methodologies that allow you to locate transactions that should undergo audit testing. Locate hidden signs of fraud Build a holistic fraud data analytic plan Identify red flags that lead to fraudulent transactions Build efficient data interrogation into your audit plan Incorporating data analytics into your audit program is not about reinventing the wheel. A good auditor must make use of every tool available, and recent advances in analytics have made it accessible to everyone, at any level of IT proficiency. When the old methods are no longer sufficient, new tools are often the boost that brings exceptional results. Fraud Data Analytics Methodology gets you up to speed, with a brand new tool box for fraud detection. |
| fraud detection data science: Fraud Analytics Delena D. Spann, 2014-07-22 Proven guidance for expertly using analytics in fraud examinations, financial analysis, auditing and fraud prevention Fraud Analytics thoroughly reveals the elements of analysis that are used in today's fraud examinations, fraud investigations, and financial crime investigations. This valuable resource reviews the types of analysis that should be considered prior to beginning an investigation and explains how to optimally use data mining techniques to detect fraud. Packed with examples and sample cases illustrating pertinent concepts in practice, this book also explores the two major data analytics providers: ACL and IDEA. Looks at elements of analysis used in today's fraud examinations Reveals how to use data mining (fraud analytic) techniques to detect fraud Examines ACL and IDEA as indispensable tools for fraud detection Includes an abundance of sample cases and examples Written by Delena D Spann, Board of Regent (Emeritus) for the Association of Certified Fraud Examiners (ACFE), who currently serves as Advisory Board Member of the Association of Certified Fraud Examiners, Board Member of the Education Task Force of the Association of Certified Anti-Money Laundering Specialists ASIS International (Economic Crime Council) and Advisory Board Member of the Robert Morris University (School of Business), Fraud Analytics equips you with authoritative fraud analysis techniques you can put to use right away. |
| fraud detection data science: Fraud and Fraud Detection Sunder Gee, 2014-11-05 Detect fraud faster—no matter how well hidden—with IDEA automation Fraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare's IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book. Business systems' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to: Understand the different areas of fraud and their specific detection methods Identify anomalies and risk areas using computerized techniques Develop a step-by-step plan for detecting fraud through data analytics Utilize IDEA software to automate detection and identification procedures The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification. |
| fraud detection data science: Data Mining for Intelligence, Fraud & Criminal Detection Christopher Westphal, 2008-12-22 In 2004, the Government Accountability Office provided a report detailing approximately 200 government-based data-mining projects. While there is comfort in knowing that there are many effective systems, that comfort isn‘t worth much unless we can determine that these systems are being effectively and responsibly employed.Written by one of the most |
| fraud detection data science: Unstructured Data Analytics Jean Paul Isson, 2018-03-13 Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis. |
| fraud detection data science: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| fraud detection data science: Fraud Analytics with SAS , 2019-06-21 SAS software provides many different techniques to monitor in real time and investigate your data, and several groundbreaking papers have been written to demonstrate how to use these techniques. Topics covered illustrate the power of SAS solutions that are available as tools for fraud analytics, highlighting a variety of domains, including money laundering, financial crime, and terrorism. Also available free as a PDF from: sas.com/books. |
| fraud detection data science: Bank Fraud Revathi Subramanian, 2014-04-14 Learn how advances in technology can help curb bank fraud Fraud prevention specialists are grappling with ever-mounting quantities of data, but in today's volatile commercial environment, paying attention to that data is more important than ever. Bank Fraud provides a frank discussion of the attitudes, strategies, and—most importantly—the technology that specialists will need to combat fraud. Fraudulent activity may have increased over the years, but so has the field of data science and the results that can be achieved by applying the right principles, a necessary tool today for financial institutions to protect themselves and their clientele. This resource helps professionals in the financial services industry make the most of data intelligence and uncovers the applicable methods to strengthening defenses against fraudulent behavior. This in-depth treatment of the topic begins with a brief history of fraud detection in banking and definitions of key terms, then discusses the benefits of technology, data sharing, and analysis, along with other in-depth information, including: The challenges of fraud detection in a financial services environment The use of statistics, including effective ways to measure losses per account and ROI by product/initiative The Ten Commandments for tackling fraud and ways to build an effective model for fraud management Bank Fraud offers a compelling narrative that ultimately urges security and fraud prevention professionals to make the most of the data they have so painstakingly gathered. Such professionals shouldn't let their most important intellectual asset—data—go to waste. This book shows you just how to leverage data and the most up-to-date tools, technologies, and methods to thwart fraud at every turn. |
| fraud detection data science: 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 data science: Cognitive Computing for Big Data Systems Over IoT Arun Kumar Sangaiah, Arunkumar Thangavelu, Venkatesan Meenakshi Sundaram, 2017-12-30 This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches. |
| fraud detection data science: Machine Learning Applications for Accounting Disclosure and Fraud Detection Papadakis, Stylianos, Garefalakis, Alexandros, Lemonakis, Christos, Chimonaki, Christiana, Zopounidis, Constantin, 2020-10-02 The prediction of the valuation of the “quality” of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the “actual” financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify “quality” characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students. |
| fraud detection data science: Graph Algorithms for Data Science Tomaž Bratanic, 2024-03-12 Practical methods for analyzing your data with graphs, revealing hidden connections and new insights. Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn: Labeled-property graph modeling Constructing a graph from structured data such as CSV or SQL NLP techniques to construct a graph from unstructured data Cypher query language syntax to manipulate data and extract insights Social network analysis algorithms like PageRank and community detection How to translate graph structure to a ML model input with node embedding models Using graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. Foreword by Michael Hunger. About the technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's inside Creating knowledge graphs Node classification and link prediction workflows NLP techniques for graph construction About the reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the author Tomaž Bratanic works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Table of Contents PART 1 INTRODUCTION TO GRAPHS 1 Graphs and network science: An introduction 2 Representing network structure: Designing your first graph model PART 2 SOCIAL NETWORK ANALYSIS 3 Your first steps with Cypher query language 4 Exploratory graph analysis 5 Introduction to social network analysis 6 Projecting monopartite networks 7 Inferring co-occurrence networks based on bipartite networks 8 Constructing a nearest neighbor similarity network PART 3 GRAPH MACHINE LEARNING 9 Node embeddings and classification 10 Link prediction 11 Knowledge graph completion 12 Constructing a graph using natural language processing technique |
| fraud detection data science: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| fraud detection data science: Practical Machine Learning: A New Look at Anomaly Detection Ted Dunning, Ellen Friedman, 2014-07-21 Finding Data Anomalies You Didn't Know to Look For Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project. Use probabilistic models to predict what’s normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts |
| fraud detection data science: 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 data science: Forensic Analytics Mark J. Nigrini, 2020-04-20 Become the forensic analytics expert in your organization using effective and efficient data analysis tests to find anomalies, biases, and potential fraud—the updated new edition Forensic Analytics reviews the methods and techniques that forensic accountants can use to detect intentional and unintentional errors, fraud, and biases. This updated second edition shows accountants and auditors how analyzing their corporate or public sector data can highlight transactions, balances, or subsets of transactions or balances in need of attention. These tests are made up of a set of initial high-level overview tests followed by a series of more focused tests. These focused tests use a variety of quantitative methods including Benford’s Law, outlier detection, the detection of duplicates, a comparison to benchmarks, time-series methods, risk-scoring, and sometimes simply statistical logic. The tests in the new edition include the newly developed vector variation score that quantifies the change in an array of data from one period to the next. The goals of the tests are to either produce a small sample of suspicious transactions, a small set of transaction groups, or a risk score related to individual transactions or a group of items. The new edition includes over two hundred figures. Each chapter, where applicable, includes one or more cases showing how the tests under discussion could have detected the fraud or anomalies. The new edition also includes two chapters each describing multi-million-dollar fraud schemes and the insights that can be learned from those examples. These interesting real-world examples help to make the text accessible and understandable for accounting professionals and accounting students without rigorous backgrounds in mathematics and statistics. Emphasizing practical applications, the new edition shows how to use either Excel or Access to run these analytics tests. The book also has some coverage on using Minitab, IDEA, R, and Tableau to run forensic-focused tests. The use of SAS and Power BI rounds out the software coverage. The software screenshots use the latest versions of the software available at the time of writing. This authoritative book: Describes the use of statistically-based techniques including Benford’s Law, descriptive statistics, and the vector variation score to detect errors and anomalies Shows how to run most of the tests in Access and Excel, and other data analysis software packages for a small sample of the tests Applies the tests under review in each chapter to the same purchasing card data from a government entity Includes interesting cases studies throughout that are linked to the tests being reviewed. Includes two comprehensive case studies where data analytics could have detected the frauds before they reached multi-million-dollar levels Includes a continually-updated companion website with the data sets used in the chapters, the queries used in the chapters, extra coverage of some topics or cases, end of chapter questions, and end of chapter cases. Written by a prominent educator and researcher in forensic accounting and auditing, the new edition of Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations is an essential resource for forensic accountants, auditors, comptrollers, fraud investigators, and graduate students. |
| fraud detection data science: Intelligence and Security Informatics Michael Chau, G. Alan Wang, Hsinchun Chen, 2015-05-08 This book constitutes the refereed proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2015, held in Ho Chi Minh City, Vietnam, in May 2015 in conjunction with PAKDD 2015, the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining. The 8 revised full papers presented were carefully reviewed and selected from numerous submissions. Topics of the workshop are information sharing and big data analytics, infrastructure protection and emergency responses, cybercrime and terrorism informatics and analytics, as well as enterprise risk management, IS security and social media analytics. The papers present a significant view on regional data sets and case studies, including online social media and multimedia, fraud deception and text mining. |
| fraud detection data science: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| fraud detection data science: Machine Learning for Auditors Maris Sekar, 2022-02-27 Use artificial intelligence (AI) techniques to build tools for auditing your organization. This is a practical book with implementation recipes that demystify AI, ML, and data science and their roles as applied to auditing. You will learn about data analysis techniques that will help you gain insights into your data and become a better data storyteller. The guidance in this book around applying artificial intelligence in support of audit investigations helps you gain credibility and trust with your internal and external clients. A systematic process to verify your findings is also discussed to ensure the accuracy of your findings. Machine Learning for Auditors provides an emphasis on domain knowledge over complex data science know how that enables you to think like a data scientist. The book helps you achieve the objectives of safeguarding the confidentiality, integrity, and availability of your organizational assets. Data science does not need to be an intimidating concept for audit managers and directors. With the knowledge in this book, you can leverage simple concepts that are beyond mere buzz words to practice innovation in your team. You can build your credibility and trust with your internal and external clients by understanding the data that drives your organization. What You Will Learn Understand the role of auditors as trusted advisors Perform exploratory data analysis to gain a deeper understanding of your organization Build machine learning predictive models that detect fraudulent vendor payments and expenses Integrate data analytics with existing and new technologies Leverage storytelling to communicate and validate your findings effectively Apply practical implementation use cases within your organization Who This Book Is For AI Auditing is for internal auditors who are looking to use data analytics and data science to better understand their organizational data. It is for auditors interested in implementing predictive and prescriptive analytics in support of better decision making and risk-based testing of your organizational processes. |
| fraud detection data science: Congressional Record United States. Congress, 1968 |
| fraud detection data science: Data Science For Cyber-security Nicholas A Heard, Niall M Adams, Patrick Rubin-delanchy, Mellisa Turcotte, 2018-09-26 Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies. |
| fraud detection data science: Using Analytics to Detect Possible Fraud Pamela S. Mantone, 2013-07-16 Detailed tools and techniques for developing efficiency and effectiveness in forensic accounting Using Analytics to Detect Possible Fraud: Tools and Techniques is a practical overview of the first stage of forensic accounting, providing a common source of analytical techniques used for both efficiency and effectiveness in forensic accounting investigations. The book is written clearly so that those who do not have advanced mathematical skills will be able to understand the analytical tests and use the tests in a forensic accounting setting. It also includes case studies and visual techniques providing practical application of the analytical tests discussed. Shows how to develop both efficiency and effectiveness in forensic accounting Provides information in such a way that non-practitioners can easily understand Written in plain language: advanced mathematical skills are not required Features actual case studies using analytical tests Essential reading for every investor who wants to prevent financial fraud, Using Analytics to Detect Possible Fraud allows practitioners to focus on areas that require further investigative techniques and to unearth deceptive financial reporting before it's too late. |
| fraud detection data science: Analytics in a Big Data World Bart Baesens, 2014-04-15 The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities. |
| fraud detection data science: Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS Richard C. Zink, 2014-07-01 Improve efficiency while reducing costs in clinical trials with centralized monitoring techniques using JMP and SAS. International guidelines recommend that clinical trial data should be actively reviewed or monitored; the well-being of trial participants and the validity and integrity of the final analysis results are at stake. Traditional interpretation of this guidance for pharmaceutical trials has led to extensive on-site monitoring, including 100% source data verification. On-site review is time consuming, expensive (estimated at up to a third of the cost of a clinical trial), prone to error, and limited in its ability to provide insight for data trends across time, patients, and clinical sites. In contrast, risk-based monitoring (RBM) makes use of central computerized review of clinical trial data and site metrics to determine if and when clinical sites should receive more extensive quality review or intervention. Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS presents a practical implementation of methodologies within JMP Clinical for the centralized monitoring of clinical trials. Focused on intermediate users, this book describes analyses for RBM that incorporate and extend the recommendations of TransCelerate Biopharm Inc., methods to detect potential patient-or investigator misconduct, snapshot comparisons to more easily identify new or modified data, and other novel visual and analytical techniques to enhance safety and quality reviews. Further discussion highlights recent regulatory guidance documents on risk-based approaches, addresses the requirements for CDISC data, and describes methods to supplement analyses with data captured external to the study database. Given the interactive, dynamic, and graphical nature of JMP Clinical, any individual from the clinical trial team - including clinicians, statisticians, data managers, programmers, regulatory associates, and monitors - can make use of this book and the numerous examples contained within to streamline, accelerate, and enrich their reviews of clinical trial data. The analytical methods described in Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS enable the clinical trial team to take a proactive approach to data quality and safety to streamline clinical development activities and address shortcomings while the study is ongoing. This book is part of the SAS Press |
| fraud detection data science: Sharing Big Data Safely Ted Dunning, Ellen Friedman, 2015-09-15 Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away. Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to: Share original data in a controlled way so that different groups within your organization only see part of the whole. You’ll learn how to do this with the new open source SQL query engine Apache Drill. Provide synthetic data that emulates the behavior of sensitive data. This approach enables external advisors to work with you on projects involving data that you can't show them. If you’re intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. You’ll also get a collection of use cases. Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, you’ll discover new options to share data safely without sacrificing security. |
| fraud detection data science: Innovative Technology at the Interface of Finance and Operations Volodymyr Babich, John R. Birge, Gilles Hilary, 2022-01-01 This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing. The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study. |
| fraud detection data science: Advances in Intelligent Data Analysis XVIII Michael R. Berthold, Ad Feelders, Georg Krempl, 2020-04-02 This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. |
| fraud detection data science: Recent Advances in Big Data and Deep Learning Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita, 2019-04-02 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. |
| fraud detection data science: Network Science Albert-László Barabási, Márton PÃ3sfai, 2016-07-21 Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science. |
| fraud detection data science: Essentials of Online payment Security and Fraud Prevention David A. Montague, 2010-11-05 Essential guidance for preventing fraud in the card-not-present (CNP) space This book focuses on the prevention of fraud for the card-not-present transaction. The payment process, fraud schemes, and fraud techniques will all focus on these types of transactions ahead. Reveals the top 45 fraud prevention techniques Uniquely focuses on eCommerce fraud essentials Provides the basic concepts around CNP payments and the ways fraud is perpetrated If you do business online, you know fraud is a part of doing business. Essentials of On-line Payment Security and Fraud Prevention equips you to prevent fraud in the CNP space. |
| fraud detection data science: 2019 9th International Conference on Cloud Computing, Data Science and Engineering (Confluence) IEEE Staff, 2019-01-10 The scope of 9th International Conference Confluence 2019 covers the broad spectrum of Influential areas in the field of Information Technology and Computer Science The major topics include, but not limited to Ad hoc and Sensor Networks Artificial Intelligence Autonomic Computing Big Data Business CloudsCloud Computing Architectures Cloud Computing Consulting Methods Cloud Security, Privacy and Compliance Challenges Content Management Data Mining & Ontology Grid Computing, Image Processing, Intelligent Information System Interaction of Mobile Computing, mCommerce and Clouds Natural Language Processing, Network Architectures and Protocols Network Security & Cryptography Pattern Recognition Quantum Computing Remote Sensing & GIS Service Oriented Architecture and Cloud Computing Soft Computing Software Engineering Software Security & Risk Management Ubiquitous Computing Virtual and Overlay Networks Web Mining Wireless Communication and any other Relevant Topics Field |
| fraud detection data science: Handbook of Research on Managing Information Systems in Developing Economies Boateng, Richard, 2020-04-17 Technology provides accessibility otherwise unavailable to the people who can benefit from it the most. As new digital tools become less expensive and more widely available, research and real-world cases that examine the union between emergent countries and information systems are essential in determining the next steps for these nations. The Handbook of Research on Managing Information Systems in Developing Economies is a pivotal reference source that explores the effects of technological data handling within developing economies. Covering a broad range of topics such as emerging digital technologies, socio-economic development, and technology startups, this book is ideally designed for software programmers, policymakers, practitioners, educators, academicians, students, and researchers. |
| fraud detection data science: Beginning Anomaly Detection Using Python-Based Deep Learning Sridhar Alla, Suman Kalyan Adari, 2019-10-10 Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You Will LearnUnderstand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection |
| fraud detection data science: Learning from Imbalanced Data Sets Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera, 2018-10-22 This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions. |
| fraud detection data science: Intelligent Computing and Innovation on Data Science Sheng-Lung Peng, Sun-Yuan Hsieh, Suseendran Gopalakrishnan, Balaganesh Duraisamy, 2021-09-27 This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021), organized by Lincoln University, Malaysia from 19 – 20 February 2021. It covers wide range of recent technologies like artificial intelligence and machine learning, big data and data sciences, Internet of Things (IoT), and IoT-based digital ecosystem. The book brings together works from researchers, scientists, engineers, scholars and students in the areas of engineering and technology, and provides an opportunity for the dissemination of original research results, new ideas, research and development, practical experiments, which concentrate on both theory and practices, for the benefit of common man. |
| fraud detection data science: Developments Of Artificial Intelligence Technologies In Computation And Robotics - Proceedings Of The 14th International Flins Conference (Flins 2020) Zhong Li, Chunrong Yuan, Jie Lu, Etienne E Kerre, 2020-08-04 FLINS, an acronym introduced in 1994 and originally for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended into a well-established international research forum to advance the foundations and applications of computational intelligence for applied research in general and for complex engineering and decision support systems.The principal mission of FLINS is bridging the gap between machine intelligence and real complex systems via joint research between universities and international research institutions, encouraging interdisciplinary research and bringing multidiscipline researchers together.FLINS 2020 is the fourteenth in a series of conferences on computational intelligence systems. |
| fraud detection data science: Computational and Statistical Methods for Analysing Big Data with Applications Shen Liu, James Mcgree, Zongyuan Ge, Yang Xie, 2015-11-20 Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. - Advanced computational and statistical methodologies for analysing big data are developed - Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable - Case studies are discussed to demonstrate the implementation of the developed methods - Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation - Computing code/programs are provided where appropriate |
| fraud detection data science: Data Science Fundamentals and Practical Approaches Dr. Gypsy Nandi, Dr. Rupam Kumar Sharma, 2020-06-02 Learn how to process and analysis data using PythonÊ KEY FEATURESÊ - The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. - The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. - A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. DESCRIPTION This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems.Ê Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.Ê WHAT WILL YOU LEARNÊ Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. WHO THIS BOOK IS FOR The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. TABLE OF CONTENTS 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics |
Tutorial: Create, evaluate, and score a fraud detection model
Jan 17, 2025 · IS_CUSTOM_DATA = False # If True, the dataset has to be uploaded manually TARGET_COL = "Class" # Target column name IS_SAMPLE = False # If True, use only …
Mastering Fraud Detection in Data Science: Strategies ...
Jun 19, 2024 · These areas demonstrate the diverse and sophisticated application of data science in fraud detection, encompassing AI, machine learning, data analytics, predictive analytics, …
Data Science in Fraud Detection and Business Protection ...
Jan 27, 2024 · The Role of Data Science in Fraud Detection and Prevention - How Analytics Safeguards Businesses. Explore how data science enhances fraud detection and prevention, …
The Role of Data Science in Fraud Detection & Risk Management
The Role of Data Science in Fraud Detection and Risk Management Data Science plays a critical role in fraud detection and risk management across various industries, including finance, e …
Data analysis for fraud detection - Wikipedia
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find …
How data science can help in fraud detection : an overview
Jun 24, 2024 · Need for data science in fraud detection. Fraud analysis is needed to protect the growth and development of the country because it prevents financial loss for organisations and …
Fraud Detection | Towards Data Science
Jun 12, 2024 · Read articles about Fraud Detection in Towards Data Science - the world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial …
Usage of Data Science in Fraud Detection in 2024
Dec 15, 2023 · Data Science Contribution to Fraud Detection in Various Applications. Data science techniques can be applied across various industries and domains to detect and …
Tutorial: Create, evaluate, and score a fraud detection model
Jan 17, 2025 · IS_CUSTOM_DATA = False # If True, the dataset has to be uploaded manually TARGET_COL = "Class" # Target column name IS_SAMPLE = False # If True, use only …
Mastering Fraud Detection in Data Science: Strategies ...
Jun 19, 2024 · These areas demonstrate the diverse and sophisticated application of data science in fraud detection, encompassing AI, machine learning, data analytics, predictive analytics, …
Data Science in Fraud Detection and Business Protection ...
Jan 27, 2024 · The Role of Data Science in Fraud Detection and Prevention - How Analytics Safeguards Businesses. Explore how data science enhances fraud detection and prevention, …
The Role of Data Science in Fraud Detection & Risk Management
The Role of Data Science in Fraud Detection and Risk Management Data Science plays a critical role in fraud detection and risk management across various industries, including finance, e …
Data analysis for fraud detection - Wikipedia
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find …
How data science can help in fraud detection : an overview
Jun 24, 2024 · Need for data science in fraud detection. Fraud analysis is needed to protect the growth and development of the country because it prevents financial loss for organisations and …
Fraud Detection | Towards Data Science
Jun 12, 2024 · Read articles about Fraud Detection in Towards Data Science - the world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial …
Usage of Data Science in Fraud Detection in 2024
Dec 15, 2023 · Data Science Contribution to Fraud Detection in Various Applications. Data science techniques can be applied across various industries and domains to detect and …
