Revolutionizing BFSI with Data Analytics KEY FEATURES ● Real-world examples and exercises will ground you in the practical application of analytics techniques specific to BFSI. ● Master Python for essential coding, SQL for data manipulation, and industry-leading tools like IBM SPSS and Power BI for sophisticated analyses. ● Understand how data-driven strategies generate profits, mitigate risks, and redefine customer support dynamics within the BFSI sphere. DESCRIPTION Are you looking to unlock the transformative potential of data analytics in the dynamic world of Banking, Financial Services, and Insurance (BFSI)? This book is your essential guide to mastering the intricate interplay of data science and analytics that underpins the BFSI landscape. Designed for intermediate-level practitioners, as well as those aspiring to join the ranks of BFSI analytics professionals, this book is your compass in the data-driven realm of banking. Address the unique challenges and opportunities of the BFSI sector using Artificial Intelligence and Machine Learning models for a data driven analysis. This book is a step by step guide to utilize tools like IBM SPSS and Microsoft Power BI. Hands-on examples that utilize Python and SQL programming languages make this an essential guide. The book features numerous case studies that illuminate various use cases of Analytics in BFSI. Each chapter is enriched with practical insights and concludes with a valuable multiple-choice questionnaire, reinforcing understanding and engagement. This book will uncover how these solutions not only pave the way for increased profitability but also navigate risks with precision and elevate customer support to unparalleled heights. WHAT WILL YOU LEARN ● Delve into the world of Data Science, including Artificial Intelligence and Machine Learning, with a focus on their application within BFSI. ● Explore hands-on examples and step-by-step tutorials that provide practical solutions to real-world challenges faced by banking institutions. ● Develop skills in essential programming languages such as Python (fundamentals) and SQL (intermediate), crucial for effective data manipulation and analysis. ● Gain insights into how businesses adapt data-driven strategies to make informed decisions, leading to improved operational efficiency. ● Stay updated on emerging trends, technologies, and innovations shaping the future of data analytics in the BFSI industry. WHO IS THIS BOOK FOR? This book is tailored for professionals already engaged in or seeking roles within Data Analytics in the BFSI industry. Additionally, it serves as a strategic resource for business leaders and upper management, guiding them in shaping data platforms and products within their organizations. The book also serves as a starting point for individuals interested in the BFSI sector. Prior experience with coding tools such as Python, SQL, Power BI is beneficial but not required as it covers all dimensions from the basics. TABLE OF CONTENTS 1. Introduction to BFSI and Data Driven Banking 2. Introduction to Analytics and Data Science 3. Major Areas of Analytics Utilization 4. Understanding Infrastructures behind BFSI for Analytics 5. Data Governance and AI/ML Model Governance in BFSI 6. Domains of BFSI and team planning 7. Customer Demographic Analysis and Customer Segmentation 8. Text Mining and Social Media Analytics 9. Lead Generation Through Analytical Reasoning and Machine Learning 10. Cross Sell and Up Sell of Products through Machine Learning 11. Pricing Optimization 12. Data Envelopment Analysis 13. ATM Cash Forecasting 14. Unstructured Data Analytics 15. Fraud Modelling 16. Detection of Money Laundering and Analysis 17. Credit Risk and Stressed Assets 18. High Performance Architectures: On-Premises and Cloud 19. Growing Trends in the Data-Driven Future of BFSI

Learn how to use AI and blockchain to build decentralized intelligent applications (DIApps) that overcome real-world challenges Key FeaturesUnderstand the fundamental concepts for converging artificial intelligence and blockchainApply your learnings to build apps using machine learning with Ethereum, IPFS, and MoiBitGet well-versed with the AI-blockchain ecosystem to develop your own DIAppsBook Description AI and blockchain are two emerging technologies catalyzing the pace of enterprise innovation. With this book, you’ll understand both technologies and converge them to solve real-world challenges. This AI blockchain book is divided into three sections. The first section covers the fundamentals of blockchain, AI, and affiliated technologies, where you’ll learn to differentiate between the various implementations of blockchains and AI with the help of examples. The second section takes you through domain-specific applications of AI and blockchain. You’ll understand the basics of decentralized databases and file systems and connect the dots between AI and blockchain before exploring products and solutions that use them together. You’ll then discover applications of AI techniques in crypto trading. In the third section, you’ll be introduced to the DIApp design pattern and compare it with the DApp design pattern. The book also highlights unique aspects of SDLC (software development lifecycle) when building a DIApp, shows you how to implement a sample contact tracing application, and delves into the future of AI with blockchain. By the end of this book, you’ll have developed the skills you need to converge AI and blockchain technologies to build smart solutions using the DIApp design pattern. What you will learnGet well-versed in blockchain basics and AI methodologiesUnderstand the significance of data collection and cleaning in AI modelingDiscover the application of analytics in cryptocurrency tradingGet to grips with open, permissioned, and private blockchainsExplore the DIApp design pattern and its merit in digital solutionsFind out how LSTM and ARIMA can be applied in crypto tradingUse the DIApp design pattern to build a sample contact tracing applicationGet started with building your own DIApps across various domainsWho this book is for This book is for blockchain and AI architects, developers, data scientists, data engineers, and evangelists who want to harness the power of artificial intelligence in blockchain applications. If you are looking for a blend of theoretical and practical use cases to understand how to implement smart cognitive insights into blockchain solutions, this book is what you need! Knowledge of machine learning and blockchain concepts is required.

Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples

The volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry.

Winner of two first place AJN Book of the Year Awards! This award-winning resource uniquely integrates national goals with nursing practice to achieve safe, efficient quality of care through technology management. The heavily revised third edition emphasizes the importance of federal policy in digitally transforming the U.S. healthcare delivery system, addressing its evolution and current policy initiatives to engage consumers and promote interoperability of the IT infrastructure nationwide. It focuses on ways to optimize the massive U.S. investment in HIT infrastructure and examines usability, innovative methods of workflow redesign, and challenges with electronic clinical quality measures (eCQMs). Additionally, the text stresses documentation challenges that relate to usability issues with EHRs and sub-par adoption and implementation. The third edition also explores data science, secondary data analysis, and advanced analytic methods in greater depth, along with new information on robotics, artificial intelligence, and ethical considerations. Contributors include a broad array of notable health professionals, which reinforces the book's focus on interprofessionalism. Woven throughout are the themes of point-of-care applications, data management, and analytics, with an emphasis on the interprofessional team. Additionally, the text fosters an understanding of compensation regulations and factors. New to the Third Edition: Examines current policy initiatives to engage consumers and promote nationwide interoperability of the IT infrastructure Emphasizes usability, workflow redesign, and challenges with electronic clinical quality measures Covers emerging challenge proposed by CMS to incorporate social determinants of health Focuses on data science, secondary data analysis, citizen science, and advanced analytic methods Revised chapter on robotics with up-to-date content relating to the impact on nursing practice New information on artificial intelligence and ethical considerations New case studies and exercises to reinforce learning and specifics for managing public health during and after a pandemic COVID-19 pandemic-related lessons learned from data availability, data quality, and data use when trying to predict its impact on the health of communities Analytics that focus on health inequity and how to address it Expanded and more advanced coverage of interprofessional practice and education (IPE) Enhanced instructor package Key Features: Presents national standards and healthcare initiatives as a guiding structure throughout Advanced analytics is reflected in several chapters such as cybersecurity, genomics, robotics, and specifically exemplify how artificial intelligence (AI) and machine learning (ML) support related professional practice Addresses the new re-envisioned AACN essentials Includes chapter objectives, case studies, end-of-chapter exercises, and questions to reinforce understanding Aligned with QSEN graduate-level competencies and the expanded TIGER (Technology Informatics Guiding Education Reform) competencies.

Big Data Analytics in the Insurance Market is an industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. A must for people seeking to broaden their knowledge of big data concepts and their real-world applications, particularly in the field of insurance.

The ever-evolving field of management in today's corporate world is marked by constant disruptions and turbulence. The emergence of Artificial Intelligence (AI) and Emotional Intelligence (EI) presents opportunities for automation, optimization, and effective leadership, but it also raises concerns about job displacement and the need to bridge the gap between these two domains. AI and Emotional Intelligence for Modern Business Management: Bridging the Gap and Nurturing Success offers solutions to closing the knowledge gap. This book provides comprehensive insights and practical strategies to academic scholars, researchers, practitioners, educators, and students. Targeting a diverse audience, this book serves as a solution-oriented resource for navigating the complexities of AI and EI in business management. By addressing both AI and EI, the book equips readers with the necessary tools to integrate these domains seamlessly into modern business management practices, stimulating informed discussions, inspiring innovative approaches, and fostering a deeper understanding of the opportunities and challenges posed by these emerging fields.

This book develops insights of digitalization and the future of financial services to originate an innovative approach to financial field, in order to underpin research and practice in the wide area of digital finance. The aim of this book is to extend our understandings on how digitalization and the future of financial services can be helpful in different business circumstances in many cross-functional financial areas, such as financial markets, financial risk management, financial technologies, investment finance, etc. Thus, the book aims at addressing the relevance of digital finance for different players, highlighting differences in tools and processes as well as identifying innovative practices in financial digitalization. This can result in some novel theoretical and practical insights that can foster financial players, in order to proactively explore and exploit opportunities in financial digitalization and offset financial risks and increase efficiency.

Business Intelligence and Analytics (BI&A) has been one of the leading technological trends in recent years and is one of the top most priority technology investments. Enterprises require the support of extensive data processing and analytical techniques to bolster their processes. The book comes at an opportune time to provide a holistic overview of BI&A along with its associated concepts, components, infrastructure etc. It also details its applications in various verticals of management such as Marketing, Finance and HR. This book also discusses relevant software such as Excel, SPSS, R and Eviews. STRATGIE can be an invaluable resource for students, instructors, and practitioners alike.