Practical Data Analytics For Bfsi

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Practical Data Analytics For Bfsi

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Author : Bharat Sikka
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2023-09-02

Practical Data Analytics For Bfsi written by Bharat Sikka and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-02 with Computers categories.

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

Practical Data Analytics For Bfsi Leveraging Data Science For Driving Decisions In Banking Financial Services And Insurance Operations

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Author : Bharat Sikka
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2023-09-02

Practical Data Analytics For Bfsi Leveraging Data Science For Driving Decisions In Banking Financial Services And Insurance Operations written by Bharat Sikka and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-02 with Computers categories.

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. Book 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. What you will 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. 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. 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 Index

Hands On Artificial Intelligence For Banking

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Author : Jeffrey Ng
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-10

Hands On Artificial Intelligence For Banking written by Jeffrey Ng and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-10 with Computers categories.

Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python Key FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook Description Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI. What you will learnAutomate commercial bank pricing with reinforcement learningPerform technical analysis using convolutional layers in KerasUse natural language processing (NLP) for predicting market responses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances via Open Bank APISense market needs using sentiment analysis for algorithmic marketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open, and internal sourcesWho this book is for This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.

Data Science For Marketing Analytics

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Author : Mirza Rahim Baig
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-07

Data Science For Marketing Analytics written by Mirza Rahim Baig and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-07 with Computers categories.

Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language Key FeaturesUse data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making. What you will learnLoad, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models with hyperparameter tuning and SMOTEWho this book is for This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path. Basic prior knowledge of Python and experience working with data will help you access this book more easily.

Data Model Scorecard

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Author : Steve Hoberman
language : en
Publisher: Technics Publications
Release Date : 2015-11-01

Data Model Scorecard written by Steve Hoberman and has been published by Technics Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-01 with Computers categories.

Data models are the main medium used to communicate data requirements from business to IT, and within IT from analysts, modelers, and architects, to database designers and developers. Therefore it’s essential to get the data model right. But how do you determine right? That’s where the Data Model Scorecard® comes in. The Data Model Scorecard is a data model quality scoring tool containing ten categories aimed at improving the quality of your organization’s data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my client’s data models – I will show you how to apply the Scorecard in this book. This book, written for people who build, use, or review data models, contains the Data Model Scorecard template and an explanation along with many examples of each of the ten Scorecard categories. There are three sections: In Section I, Data Modeling and the Need for Validation, receive a short data modeling primer in Chapter 1, understand why it is important to get the data model right in Chapter 2, and learn about the Data Model Scorecard in Chapter 3. In Section II, Data Model Scorecard Categories, we will explain each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category: · Chapter 4: Correctness · Chapter 5: Completeness · Chapter 6: Scheme · Chapter 7: Structure · Chapter 8: Abstraction · Chapter 9: Standards · Chapter 10: Readability · Chapter 11: Definitions · Chapter 12: Consistency · Chapter 13: Data In Section III, Validating Data Models, we will prepare for the model review (Chapter 14), cover tips to help during the model review (Chapter 15), and then review a data model based upon an actual project (Chapter 16).

Artificial Intelligence In Healthcare Information Systems Security And Privacy Challenges

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Author : Narasimha Rao Vajjhala
language : en
Publisher: Springer Nature
Release Date : 2025-03-29

Artificial Intelligence In Healthcare Information Systems Security And Privacy Challenges written by Narasimha Rao Vajjhala and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-29 with Computers categories.

“Artificial Intelligence (AI) in Healthcare Information Systems: Security and Privacy Challenges” offers a deep dive into the integration of AI in healthcare, with a primary focus on addressing the significant security and privacy concerns that arise in this domain. The chapters in this book highlight the transformative potential of AI in diagnosing and predicting diseases, as well as its impact on fields like fetal medicine, but places special emphasis on the need for robust encryption, data protection techniques, and ethical considerations to safeguard sensitive healthcare data. The book also explores global case studies, from India to Kazakhstan, outlining the challenges and prospects of AI adoption in diverse healthcare settings. Readers will gain insights into AI's role in improving patient outcomes while navigating the complexities of data privacy and security. The book is a valuable resource for healthcare professionals, technologists, and policymakers who are focused on implementing AI-driven solutions securely and ethically in healthcare systems.

Elements Of Deep Learning For Computer Vision

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Author : Bharat Sikka
language : en
Publisher: BPB Publications
Release Date : 2021-06-24

Elements Of Deep Learning For Computer Vision written by Bharat Sikka and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-24 with Computers categories.

Conceptualizing deep learning in computer vision applications using PyTorch and Python libraries. KEY FEATURES ● Covers a variety of computer vision projects, including face recognition and object recognition such as Yolo, Faster R-CNN. ● Includes graphical representations and illustrations of neural networks and teaches how to program them. ● Includes deep learning techniques and architectures introduced by Microsoft, Google, and the University of Oxford. DESCRIPTION Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch. This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After establishing and understanding both the primary concepts, the book addresses them together by explaining Convolutional Neural Networks(CNNs). CNNs are further elaborated using top industry standards and research to explain how they provide complicated Object Detection in images and videos, while also explaining their evaluation. Towards the end, the book explains how to develop a fully functional object detection model, including its deployment over APIs. By the end of this book, you are well-equipped with the role of deep learning in the field of computer vision along with a guided process to design deep learning solutions. WHAT YOU WILL LEARN ● Get to know the mechanism of deep learning and how neural networks operate. ● Learn to develop a highly accurate neural network model. ● Access to rich Python libraries to address computer vision challenges. ● Build deep learning models using PyTorch and learn how to deploy using the API. ● Learn to develop Object Detection and Face Recognition models along with their deployment. WHO THIS BOOK IS FOR This book is for the readers who aspire to gain a strong fundamental understanding of how to infuse deep learning into computer vision and image processing applications. Readers are expected to have intermediate Python skills. No previous knowledge of PyTorch and Computer Vision is required. TABLE OF CONTENTS 1. An Introduction to Deep Learning 2. Supervised Learning 3. Gradient Descent 4. OpenCV with Python 5. Python Imaging Library and Pillow 6. Introduction to Convolutional Neural Networks 7. GoogLeNet, VGGNet, and ResNet 8. Understanding Object Detection 9. Popular Algorithms for Object Detection 10. Faster RCNN with PyTorch and YoloV4 with Darknet 11. Comparing Algorithms and API Deployment with Flask 12. Applications in Real World

Advances In Computational Intelligence And Informatics

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Author : Raghavendra Rao Chillarige
language : en
Publisher: Springer Nature
Release Date : 2024-08-22

Advances In Computational Intelligence And Informatics written by Raghavendra Rao Chillarige and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-22 with Computers categories.

The book is a collection of best selected research papers presented at 2nd International Conference on Advances in Computational Intelligence and Informatics (ICACII 2023) organized by Anurag University, Hyderabad, India, during 22–23 December 2023. It includes innovative ideas and new research findings in the field of Computational Intelligence and Informatics which is useful for researchers, scientists, technocrats, academicians, and engineers. The areas included are high performance system, data science and analytics, computational intelligence and expert systems, cloud computing, computer network, and emerging technologies.

Bank 4 0

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Author : Brett King
language : en
Publisher: Marshall Cavendish International Asia Pte Ltd
Release Date : 2018-08-15

Bank 4 0 written by Brett King and has been published by Marshall Cavendish International Asia Pte Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-15 with Business & Economics categories.

In the final book in the digital “BANK” series, Brett King tackles the topic of whether banks have a future at all in the emerging, technology embedded world of the 21st century. In 30-50 years when cash is gone, cards are gone and all vestiges of the traditional banking system have been re-engineered in real-time, what exactly will a bank look like? How will we reimagine a bank account, identity, value, assets, investments? hen stepping back from this vision of the future, King and his cadre of ‘disruptors’ and Fintech mafia chronicle the foundations of this new banking ecosystem today. From selfie-pay in China, blockchain in Africa, self-driving cars with their own bank accounts and augmented reality tech that informs the future design of banking systems, this proves once and for all that we’re not in Wall Street anymore Toto. Bank 4.0 is what banking will become. The Russian edition of Bank 4.0 was recognised as the best book by a foreign author (2019) at the Business Book of the Year Award organised by PwC Russia.

Principles Of Green Banking

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Author : Suborna Barua
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2020-08-10

Principles Of Green Banking written by Suborna Barua and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-10 with Business & Economics categories.

Environmental sustainability is perhaps the key societal challenge of our times. Achieving it will require a significant level of financing and investment, and here the role of the banking industry is fundamental. Banks can play a broader and far-reaching role by adopting environmental concerns in their internal and external business operations. Principles of Green Banking is a comprehensive account of the different aspects of green banking and offers theories and principles as well as practical how-to guidelines to adopt green banking practices. This book discusses why green banking is central to achieving sustainable development. It illustrates the evolution of green banking around the world, different types of environmental risks created by firms and how these risks offer threats to sustain ability, and ongoing trends and patterns of green banking practice. Critically, it also presents an outline of the regulatory framework necessary to help the entire banking sector adapt to the change towards green banking. It is a valuable resource for financial sector professionals and scholars in the fields of sustainable finance and banking.