Download or read book Data Driven Modelling and Predictive Analytics in Business and Finance written by Alex Khang and published by CRC Press. This book was released on 2024-07-24 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.

Download or read book Predictive Intelligence for Data Driven Managers written by Uwe Seebacher and published by Springer Nature. This book was released on 2021-03-26 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how companies can easily and pragmatically set up and realize the path to a data-driven enterprise, especially in the marketing practice, without external support and additional investments. Using a predictive intelligence (PI) ecosystem, the book first introduces and explains the most important concepts and terminology. The PI maturity model then describes the phases in which you can build a PI ecosystem in your company. The book also demonstrates a PI self-test which helps managers identify the initial steps. In addition, a blueprint for a PI tech stack is defined for the first time, showing how IT can best support the topic. Finally, the PI competency model summarizes all elements into an action model for the company. The entire book is underpinned with practical examples, and case studies show how predictive intelligence, in the spirit of data-driven management, can be used profitably in the short, medium, and long terms.

Download or read book Synergy of AI and Fintech in the Digital Gig Economy written by Alex Khang and published by CRC Press. This book was released on 2024-09-30 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: The convergence of Artificial Intelligence (AI) and Financial Technology (Fintech) has ushered in a new era of innovation in the finance ecosystem, particularly within the context of the digital gig economy. This emerging trend has created a unique set of challenges and opportunities, which AI and Fintech are poised to address. This book explores how the convergence of these cutting-edge technologies is reshaping the financial landscape, especially related to the way people work and earn in the gig economy, and examines the rise of the digital gig economy and its impact on the traditional workforce. Synergy of AI and Fintech in the Digital Gig Economy presents the key advancements in AI and Fintech, how they are disrupting traditional financial systems, and how AI-powered tools and platforms are streamlining financial processes, enhancing decision-making, and providing personalized services to individuals and businesses. The book explores how the synergy of AI and Fintech is advancing financial inclusion and looks at how these technologies are providing previously underserved populations with access to financial services and empowering them to participate in the global economy. Highlights include how AI and Fintech are revolutionizing risk assessment and management in the financial sector and discuss the use of advanced algorithms to detect fraud, assess creditworthiness, and mitigate financial risk more effectively. The book also addresses the regulatory challenges and ethical considerations arising from the integration of AI and Fintech and discusses the need for responsible AI and data privacy to ensure sustainable development. Insights, case studies, and practical examples provided in the book show how AI and Fintech are driving transformative changes and represent an area of significant interest and importance in the realm of finance and technology. Written for students, scholars, lecturers, researchers, scientists, experts, specialists, and engineers, this book represents an area of significant interest and importance in the realm of finance and technology. Real-world examples and contributions from industry experts give readers a comprehensive understanding of this hot trending topic.

Download or read book Revolutionizing the AI Digital Landscape written by Alex Khang and published by CRC Press. This book was released on 2024-06-07 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the growing influence of artificial intelligence in the marketing sphere, providing insights into how AI can be harnessed for developing more effective and efficient marketing strategies. In addition, the book will also offer a comprehensive overview of the various digital marketing tools available to entrepreneurs, discussing their features, benefits, and potential drawbacks. This will help entrepreneurs make well-informed decisions when selecting the tools most suited to their needs and objectives. It is designed to help entrepreneurs develop and implement successful strategies, leveraging the latest tools and technologies to achieve their business goals. As the digital landscape continues to evolve rapidly, this book aims to serve as a valuable resource for entrepreneurs looking to stay ahead of the curve and capitalize on new opportunities. The book's scope encompasses a wide range of topics, including customer experience, content marketing, AI strategy, and digital marketing tools.

Download or read book Three Essays on Business Analytics written by Yuxin Zhang (Ph. D.) and published by . This book was released on 2020 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: In my dissertation, I propose a general research framework of MAD---Monitoring, Analyzing, and Data Informed Decision-making---for financial decision-making. I present three essays which concentrate on two consequential aspects of decision-making for financial risk management. The first two essays focus on better monitoring and analyzing the risk, and the last one focuses on better data-informed decision-making based on the observation and analysis. In the first essay, I study the modeling of joint mortality for the practice of life insurance and annuity pricing. Specifically, I develop a new mathematical model to describe the joint mortality for coupled dependent lives. This model can be used to guide the risk management strategy and the pricing policy for insurance and annuity products. It is shown that it improves the current methods for modeling financial decision-making related to dependent life structures (such as joint life insurance, last survivor annuities, and defined benefit plans for married couples). In the second essay, I study the prediction of Bitcoin price movement and the relevant implications for business analytics. I exploit Bitcoin transaction networks and link network characteristics with the Bitcoin market exchange price. Based on this linkage and the data record, I construct predictive models for Bitcoin price movement. With the innovative use of Bitcoin transaction network data, the predictive models lead to more accurate results which outperform existing models. This methodological innovation also presents new managerial insights from network perspectives. In the third essay, I focus on data-driven decision-making in contexts of the allocation of disaster relief funds. Specifically, I tackle methodological challenges in disaster management when data are extremely sparse and insufficient in the beginning of the disaster evolution, and slowly become more available and reliable as time unfolds. Here I propose an iterative learning method within the general MAD framework to estimate disaster damage losses using very limited and slowly obtained data. Results show that this iterative learning method leads to highly accurate results with fast convergence of the estimation error to a very low level. The framework and results of this essay can be further used for disaster management and resource allocation in various scenarios

Download or read book AI Centric Modeling and Analytics written by Alex Khang and published by CRC Press. This book was released on 2023-12-06 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shares new methodologies, technologies, and practices for resolving issues associated with leveraging AI-centric modeling, data analytics, machine learning-aided models, Internet of Things-driven applications, and cybersecurity techniques in the era of Industrial Revolution 4.0. AI-Centric Modeling and Analytics: Concepts, Technologies, and Applications focuses on how to implement solutions using models and techniques to gain insights, predict outcomes, and make informed decisions. This book presents advanced AI-centric modeling and analysis techniques that facilitate data analytics and learning in various applications. It offers fundamental concepts of advanced techniques, technologies, and tools along with the concept of real-time analysis systems. It also includes AI-centric approaches for the overall innovation, development, and implementation of business development and management systems along with a discussion of AI-centric robotic process automation systems that are useful in many government and private industries. This reference book targets a mixed audience of engineers and business analysts, researchers, professionals, and students from various fields.

Download or read book Modeling Techniques in Predictive Analytics written by Thomas W. Miller and published by Pearson Education. This book was released on 2014 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you've identified it, and then how to successfully model that data. You'll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today's #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it's easy to find for those who want it (and easy to skip for those who don't).

Download or read book AI Oriented Competency Framework for Talent Management in the Digital Economy written by Alex Khang and published by CRC Press. This book was released on 2024-05-29 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the digital-driven economy era, an AI-oriented competency framework (AIoCF) is a collection to identify AI-oriented knowledge, attributes, efforts, skills, and experiences (AKASE) that directly and positively affect the success of employees and the organization. The application of skills-based competency analytics and AI-equipped systems is gradually becoming accepted by business and production organizations as an effective tool for automating several managerial activities consistently and efficiently in developing and moving the capacity of a company up to a world-class level. AI-Oriented Competency Framework for Talent Management in the Digital Economy: Models, Technologies, Applications, and Implementation discusses all the points of an AIoCF, which includes predictive analytics, advisory services, predictive maintenance, and automated processes, which help to make the operations of project management, personnel management, or administration more efficient, profitable, and safe. The book includes the functionality of emerging career pathways, hybrid learning models, and learning paths related to the learning and development of employees in the production or delivery fields. It also presents the relationship between skills taxonomy and competency framework with interactive methods using datasets, processing workflow diagrams, and architectural diagrams for easy understanding of the application of intelligent functions in role-based competency systems. By also covering upcoming areas of AI and data science in many government and private organizations, the book not only focuses on managing big data and cloud resources of the talent management system but also provides cybersecurity techniques to ensure that systems and employee competency data are secure. This book targets a mixed audience of students, engineers, scholars, researchers, academics, and professionals who are learning, researching, and working in the field of workforce training, human resources, talent management systems, requirement, headhunting, outsourcing, and manpower consultant services from different cultures and industries in the era of digital economy.

Download or read book Creating a Data Driven Organization written by Carl Anderson and published by "O'Reilly Media, Inc.". This book was released on 2015-07-23 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: "What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models"--Publisher's description.

Download or read book AI and IoT Based Technologies for Precision Medicine written by Khang, Alex and published by IGI Global. This book was released on 2023-10-18 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the post-COVID-19 healthcare landscape, the demand for smart healthcare solutions and precision medicine systems has grown significantly. To address these challenges, the book AI and IoT-Based Technologies for Precision Medicine provides a comprehensive resource for doctors, researchers, engineers, and students. By leveraging AI and IoT technologies, the book equips healthcare professionals with advanced tools and methodologies for predictive disease analysis, informed decision-making, and other aspects of precision medicine. This resource bridges the gap between theory and practice, exploring concepts like machine learning, deep learning, computer vision, AI-integrated applications, IoT-based technologies, healthcare data analytics, and biotechnology applications. Through this, the book empowers healthcare practitioners to pioneer innovative solutions that enhance efficiency, accuracy, and security in medical practices. AI and IoT-Based Technologies for Precision Medicine not only offer insights into the potential of AI-powered applications and IoT-equipped techniques in smart healthcare but also foster collaboration among healthcare scholars and professionals. This authoritative guide encourages knowledge sharing and collaboration to harness the transformative potential of AI and IoT, leading to revolutionary advancements in medical practices and healthcare services. With this book as a guide, readers can navigate the evolving landscape of high-tech medicine, taking confident steps toward a cutting-edge and precise medical ecosystem.

Download or read book Advanced IoT Technologies and Applications in the Industry 4 0 Digital Economy written by Alex Khang and published by CRC Press. This book was released on 2024-02-27 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of internet of things (IoT) technologies and artificial intelligence (AI)-enabled IoT solutions has gradually become accepted by business and production organizations as an effective tool for automating several activities effectively and efficiently and developing and distributing products to the global market. Within this book, the reader will learn how to implement IoT devices, IoT-equipped machines, and AI-equipped IoT applications using models and methodologies along with an array of case studies. Advanced IoT Technologies and Applications in the Industry 4.0 Digital Economy covers the basics of IoT-equipped machines in developing and managing various activities in many industries. It discusses all of the key points of an AI-enabled IoT solution, which includes predictive analytics, robotic process automation, predictive maintenance, automated processes, IoT technologies and IoT-equipped sensors related to machines and processes, production testing systems, and product assessment processes in the production environment. The book presents the concepts and interactive methods using datasets, processing workflow charts, and architectural diagrams along with additional real-time systems for easy and fast understanding of the application of IoT-equipped machines and AI-enabled solutions in organizations and includes many case studies throughout the book to enforce reader comprehension. This book is an ideal read for industry specialists, practitioners, researchers, scientists, and engineers working or involved in the fields of Robotics, IT, Computer Science, Soft Computing, IoT, AL/ML/DL, Data Science, the Semantic Web, Knowledge Engineering, and other related fields.

Download or read book Applied Predictive Analytics written by Dean Abbott and published by John Wiley & Sons. This book was released on 2014-03-31 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Download or read book Predictive Business Analytics written by Lawrence Maisel and published by John Wiley & Sons. This book was released on 2013-10-07 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions.

Download or read book AI Based Predictive Analytics written by Minghai Zheng and published by Independently Published. This book was released on 2023-06-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. #DataDrivenDecisions #PredictiveAnalytics Unlock the power of data-driven decisions with AI-based predictive analytics! This book provides the insights and tools you need to make informed decisions based on data. 2. #AIinBusiness #PredictiveModeling Stay ahead of the competition with AI-based predictive analytics. Learn how to implement predictive modeling in your business with this must-read book. 3. #BigDataInsights #DecisionMaking Want to make better decisions based on big data insights? Look no further than "AI-Based Predictive Analytics." This book is your ultimate guide to leveraging the power of AI for predictive analytics. 4. #MachineLearning #DataAnalysis Discover the latest machine learning techniques for data analysis with "AI-Based Predictive Analytics." This book will help you make sense of complex data sets and turn them into actionable insights. 5. #BusinessIntelligence #AIinAction Maximize your business intelligence capabilities with AI-based predictive analytics. Get your copy of this book to learn how to implement these powerful strategies in your organization. In today's data-driven world, predictive analytics has emerged as a powerful tool for organizations looking to make informed decisions based on data. By analyzing historical data and identifying patterns, predictive analytics enables businesses to predict future outcomes and take proactive measures to improve outcomes. With the rise of artificial intelligence (AI), predictive analytics has become even more sophisticated, offering new ways to analyze data and uncover insights that were previously impossible to obtain. The book "AI-Based Predictive Analytics: Empowering Data-Driven Decisions" provides a comprehensive guide on how to implement AI-based predictive analytics strategies in your organization. Whether you're an analyst, data scientist, or business leader, this book will provide you with practical insights and tools to improve decision-making and drive success. In this book, we'll explore various ways in which AI can be used in predictive analytics. We'll discuss the latest machine learning techniques, including deep learning, natural language processing, and regression analysis. Additionally, we'll examine case studies of successful implementations of AI-based predictive analytics in different industries, along with the benefits and challenges associated with these implementations. Whether you're looking to reduce costs, improve efficiency, or enhance customer experiences, this book will provide you with the knowledge and tools needed to achieve your goals. The chapters that follow will delve deeper into specific topics related to AI-based predictive analytics, providing you with a comprehensive guide for implementing these strategies in your organization. MingHai Zheng is the founder of zhengpublishing.com and lives in Wuhan, China. His main publishing areas are business, management, self-help, computers and other emerging foreword fields.

Download or read book Deep Learning Concepts in Operations Research written by Biswadip Basu Mallik and published by CRC Press. This book was released on 2024-08-30 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

Download or read book Predictive Analytics for the Modern Enterprise written by Nooruddin Abbas Ali and published by "O'Reilly Media, Inc.". This book was released on 2024-05-20 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

Download or read book Financial Data Analytics with Machine Learning Optimization and Statistics written by Yongzhao Chen and published by John Wiley & Sons. This book was released on 2024-11-19 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.