Data-Driven Modelling and Predictive Analytics in Business and Finance: Concepts, Designs, Technologies, and Applications (Advances in Computational Collective Intelligence)

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. Cover Half Title Series Information Title Page Copyright Page Table of Contents Preface Acknowledgments About the Editors List of Contributors 1 Application of Data Technologies and Tools in Business and Finance Sectors 1.1 Introduction 1.2 Related Work 1.3 Impact of Data Engineering, Data Analytics, and Data Visualization 1.4 Data Engineering 1.4.1 Introduction to Data Engineering 1.4.2 Evolution of Data Engineering 1.4.3 Role of the Data Engineer 1.4.4 Data Engineering Process 1.4.5 Data Engineering Tools and Technologies 1.4.6 Challenges in Data Engineering 1.5 Data Analytics 1.5.1 Introduction to Data Analytics 1.5.2 Evolution of Data Analytics 1.5.3 Role of a Data Analyst 1.5.4 Responsibilities of a Data Analyst 1.5.5 Data Analysis Process 1.5.6 Data Analysis Tools and Technologies 1.5.7 Challenges in Data Analytics 1.6 Data Visualization 1.6.1 Introduction to Data Visualization 1.6.2 Evolution of Data Visualization 1.6.3 Role of a Data Visualization Developer 1.6.4 Responsibilities of a Data Visualization Developer 1.6.5 Data Visualization Process 1.6.6 Data Visualization Tools and Technologies 1.6.7 Challenges in Data Visualization Case Study: Customer Segmentation for Improving Customer Retention in an E-Commerce Platform 1.6.8 Data Sourcing and Preparation 1.7 Conclusion References 2 Data Analytics Tools and Applications for Business and Finance Systems 2.1 Introduction 2.1.1 Data Science Tools 2.1.2 Variety of Tools 2.1.3 Open-Source Options 2.2 Importance of Data Analytics in Decision-Making 2.3 Types of Data Analytics 2.4 Data Analytics Tools 2.5 Exploratory Data Analysis (EDA) 2.5.1 Statistical Analysis Techniques 2.5.2 Big Data Analytics Tools 2.5.3 Data Visualization Tools 2.5.4 Real-Time Analytics Tools 2.6 Data Analytics in Specific Industries 2.7 Text Analytics and Sentiment Analysis Tools 2.8 Conclusion References 3 Big Data Tools for Business and Finance Sectors in the Era of Metaverse 3.1 Introduction 3.2 Big Data 3.3 Metaverse 3.4 Big Data in the Metaverse 3.5 Big Data Tools for the Metaverse 3.6 Real-Time Data Processing and Predictive Analytics 3.7 Security and Ethical Guidelines 3.8 Future Research Direction 3.9 Case Studies 3.10 Big Data Analytics for Business and Finance 3.11 Conclusion References 4 Digital Revolution and Innovation in the Banking and Finance Sectors 4.1 Introduction 4.2 Review of Literature 4.2.1 Objectives 4.2.2 Research Methodology 4.3 Evolution of Digital Banking 4.3.1 Tele Banking 4.3.2 Automated Teller Machine 4.3.3 Artificial Intelligence 4.3.4 Central Bank Digital Currency 4.3.5 Nation First Transit Card 4.4 Different Payment Systems Used in Banking Sector 4.5 Analysis of Digital Payments System 4.5.1 RTGS 4.5.2 IMPS 4.5.3 UPI 4.5.4 Debit Cards 4.5.5 Credit Cards 4.5.6 BHIM 4.5.7 NEFT 4.6 Contribution of Digital Banking in GDP 4.7 Conclusion 4.8 Future Scope of Work in Industry 4.0 References 5 Impact of AI and Data in Revolutionizing Microfinance in Developing Countries: Improving Outreach and Efficiency 5.1 Introduction 5.2 AI: The New Method of Preventing Frauds in Microfinance 5.3 The Role of AI in Mitigating Risk in Microfinance 5.4 AI Cost-Saving: Pointing Microfinance in a New Direction 5.5 Enhancing Financial Empowerment: Role of AI in Mobile Banking in Microfinance Institutions 5.6 Conclusion References 6 Digital Payments: The Growth Engine of the Digital Economy 6.1 Introduction 6.2 Aim of the Study 6.3 Literature Review 6.4 Data and Methodology 6.5 Discussions and Findings of the Study 6.5.1 Other Electronic Payment Alternatives 6.5.1.1 Debit, Credit, Cash, Travel, and Other Financial Institution Cards 6.5.1.2 USSD (Unstructured Supplementary Service Data) 6.5.1.3 AEPS (Aadhaar Enabled Payment System) 6.5.1.4 UPI (Unified Payments Interface) 6.5.1.5 UPI 123PAY 6.5.1.6 UPI LITE 6.5.1.7 BHIM Aadhaar Pay 6.5.1.8 Bharat Bill Payment System (BBPS) 6.5.1.9 National Electronic Toll Collection (NETC) 6.5.1.10 Location of Purchase (POS) 6.5.2 Transfer of Payments Electronically 6.5.2.1 Internet Banking 6.5.2.2 National Electronic Funds Transfer 6.5.2.3 Real Time Gross Settlement (RTGS) 6.5.2.4 Electronic Clearing Service (ECS) 6.5.2.5 Instant Payment Service (IMPS) 6.5.2.6 Mobile Wallet 6.5.2.7 Bank Prepaid Cards 6.5.2.8 Micro-ATMs 6.6 The Advantages of Digital Payments 6.7 CAGR Analysis 6.8 Conclusion 6.9 Future Scope of Work References 7 Machine Learning-Based Functionalities for Business Intelligence and Data Analytics Tools 7.1 Introduction 7.1.1 Business Intelligence (BI) 7.1.2 Business Analytics (BA) 7.1.3 Comparison of Business Intelligence With Business Analytics 7.1.4 Business 7.1.5 Finance Sector 7.1.6 Machine Learning 7.1.7 Machine Learning Functionalities for Business and Finance Sectors 7.2 Literature Review 7.3 System Design 7.3.1 Artificial Neural Network (ANN) 7.3.2 Mathematical Model for ANN 7.4 Results And Discussion 7.4.1 Evaluation Metrics 7.4.2 Accuracy 7.4.3 Sensitivity 7.4.4 Specificity 7.4.5 Time Duration 7.5 Conclusion References 8 A Study of a Domain-Specific Approach in Business Using Big Data Analytics and Visualization 8.1 Introduction 8.2 Literature Survey 8.3 Big Data and Domain-Specific Approach 8.4 Case Example of Improved Risk Management 8.4.1 Input 8.4.2 Process 8.4.3 Output 8.4.4 Conclusion of Case 8.4.5 Data Visualization Tools 8.4.6 Big Data Analytics 8.5 Case Study of Budgeting and Planning 8.5.1 Decision-Making Aspect: Financial Forecasting and Planning 8.5.2 Decision-Making Aspect: Visualization of Financial Data 8.5.3 Expected Outcomes of the Study 8.6 Conclusion References 9 Cloud-Based Data Management for Behavior Analytics in Business and Finance Sectors 9.1 Introduction 9.2 Foundations of Cloud-Based Data Management 9.2.1 Basic Architecture of Cloud-Based Data Management 9.2.2 The Role of Cloud-Based Data Management 9.2.3 Benefits of Cloud-Based Data Management 9.2.4 Challenges and Considerations 9.3 Literature Review 9.4 Best Practices 9.4.1 Data Governance 9.4.2 Hybrid Approaches 9.4.3 Continuous Monitoring 9.5 Cloud-Based Data Management in Business and Finance 9.5.1 Exploring Cloud Computing and Its Benefits 9.5.1.1 The Essence of Cloud Computing 9.5.1.2 Benefits for Businesses and Finance 9.5.2 Cloud Data Storage and Scalability 9.5.2.1 Data Storage Revolution 9.5.2.2 Unleashing Scalability 9.5.2.3 Data Redundancy and Reliability 9.5.2.4 Agile Decision-Making 9.5.3 Data Security and Privacy Considerations 9.5.3.1 Robust Data Security Measures 9.5.3.2 Compliance and Regulation 9.5.3.3 Vendor Security and Transparency 9.5.3.4 Data Privacy 9.5.4 Customer Data Collection and Sources 9.5.4.1 Transactional Data 9.5.4.2 Behavioral Data 9.5.4.3 Demographic and Socioeconomic Data 9.6 Challenges and Considerations 9.6.1 Data Privacy and Security in Cloud-Based Customer Analytics 9.6.2 Ethical Considerations in Personalization 9.6.3 Data Quality and Integration Challenges 9.7 Case Studies: Successful Implementations 9.7.1 Retail Industry 9.7.2 Financial Services Sector 9.7.3 E-Commerce Platforms 9.8 Future Trends and Implications 9.8.1 Advances in Cloud-Based Analytics and Machine Learning 9.8.2 Evolving Customer Expectations and Personalization 9.8.3 Integration With Emerging Technologies (AI, IoT) 9.9 Conclusion 9.10 Key Terms References 10 Theoretical Analysis and Data Modeling of the Influence of Shadow Banking On Systemic Risk 10.1 Introduction 10.2 Brief Overview of Shadow Banks 10.3 Review of the Literature 10.3.1 Conceptual Framework 10.3.2 Relationship of SBs and SR 10.3.3 Drivers of Systemic Risk 10.3.4 Methodological Approach 10.3.5 Literature Gap 10.4 Conclusion References 11 The Potential of a Fintech-Driven Model in Enabling Financial Inclusion 11.1 Introduction 11.2 Growth of Fintech and Its Influence On Financial Services 11.3 Government Initiatives to Promote Fintech and Financial Inclusion in India 11.3.1 National Strategy for Financial Inclusion (NSFI) 11.3.2 High-Level Committee On Deepening of Digital Payments 11.3.3 E-KYC 11.3.4 Fintech Regulatory Sandbox 11.3.5 Unified Payments Interface (UPI) 11.3.6 Other Initiatives 11.4 Key Challenges to the Fintech Sector in India 11.4.1 Cyber Security and Data Protection 11.4.2 Gain Trust in Fintech Products 11.4.3 Regulatory Measures to Improve Quality of Fintech Products 11.4.4 Development of Financial Infrastructure and Utilities 11.5 The Way Forward 11.5.1 Cooperation Between Commercial Banks and Fintech Companies 11.5.2 Protection of Personal Data 11.5.3 Focus On Rural Population 11.5.4 Fintech Adoption in SME Sector 11.5.5 Gender Gap in Financial Inclusion 11.5.6 Designing Tailored Financial Products 11.5.7 Digital Financial Literacy 11.6 Conclusion 11.7 Future Scope of Work in Industry 4.0 References 12 Predicting the Impact of Exchange Rate Volatility On Sectoral Indices 12.1 Introduction 12.2 Literature Review 12.3 Research Methodology 12.4 Results and Discussions 12.4.1 Descriptive Statistics 12.4.2 Unit Root Test 12.4.3 Garch (1, 1) Model Results 12.4.3.1 Impact On Auto Sector Indices 12.4.3.2 Impact On Energy Sector Indices 12.4.3.3 Impact On Financial Services Sector Indices 12.4.3.4 Impact On IT Sector Indices 12.4.3.5 Impact On Metal Sector Indices 12.4.3.6 Impact On Pharma Sector Indices 12.4.3.7 Impact Of Exchange Rate On Stock Indices 12.5 Conclusion References 13 Digital Competency Assessment and Data-Driven Performance Management for Start-Ups 13.1 Introduction 13.2 Background of Research 13.2.1 Historical Overview of Research 13.2.2 Research Questions 13.2.3 Objectives of the Study 13.2.4 Significance of the Research 13.3 Review of Literature 13.3.1 Digital Competencies for Start-Ups 13.3.2 Digital Competency Assessment Tools 13.3.3 Direct and Indirect Assessment Tools for Mapping Digital Competencies 13.3.4 Theoretical Foundations and Framework 13.4 Research Methodology 13.5 Findings and Analysis 13.5.1 Developing a Framework for Assessing the Digital Competitiveness of Start-Ups 13.5.2 Assessing the Digital Competitiveness of a Sample of Start-Ups Using the Framework 13.5.3 Identifying the Factors That Contribute to Digital Competitiveness and Success in Start-Ups 13.5.4 Developing Recommendations for Start-Ups and Their Managers to Improve Their Digital Competitiveness and Performance 13.6 Conclusion 13.7 Limitations 13.8 Recommendations and Suggestions 13.9 Future Scope of Work References 14 Blockchain Technologies and Applications for Business and Finance Systems 14.1 Introduction 14.1.1 Importance of Blockchain Technology 14.1.2 Architecture of Blockchain 14.1.3 Working Steps of Blockchain 14.1.4 Components of Blockchain 14.1.5 Characteristics of Blockchain 14.1.5.1 Functional Characteristics 14.1.5.2 Embryonic Characteristics 14.2 Types of Blockchain Technology 14.2.1 Public Blockchain 14.2.1.1 Advantages 14.2.1.2 Disadvantages 14.2.2 Private Blockchain 14.2.2.1 Advantages 14.2.2.2 Disadvantages 14.2.3 Hybrid Blockchain 14.2.3.1 Advantages 14.2.3.2 Disadvantages 14.2.4 Consortium Blockchain 14.2.4.1 Advantages 14.2.4.2 Disadvantages 14.3 Applications of Blockchain Technology 14.3.1 Finance 14.3.2 Cloud Computing 14.3.3 Internet of Things 14.3.4 Big Data Management 14.3.5 Industry 14.3.6 Education 14.3.7 Healthcare 14.3.8 E-Commerce 14.3.9 E-Government Service 14.3.10 Real Estate 14.3.11 Power and Energy 14.3.12 Transportation 14.3.13 Wireless Networks 14.3.14 Agriculture 14.3.15 Aviation 14.3.16 Forensic Science and Investigation 14.3.17 Additional Applications 14.4 Conclusion References 15 Analysing the Reaction for M&A of Rivals in an Emerging Market Economy 15.1 Introduction 15.2 Data and Methodology 15.2.1 Sample, Empirical Strategy and Model 15.2.2 Variables 15.3 Empirical Results 15.3.1 Descriptive Statistics 15.3.2 Results of Cross-Sectional Regression Analysis 15.4 Conclusion Note References 16 Management Model 6.0 and SWOT Analysis for the Market Share of Product in the Global Market 16.1 Introduction 16.2 Literature Review 16.3 Materials and Methods 16.4 Features of the Development of Management Model 6.0 16.4.1 Artificial Intelligence-Powered Management Model 16.4.2 Science-Driven Growth Business Model 16.4.3 Digital-Driven Management Model 16.4.4 Technology-Driven Business Recovery Strategy 16.4.5 Behavioral-Driven Marketing Strategy 16.5 Case Study—Global TPK Product Company (TPK) 16.5.1 Analytics Methodology 16.5.2 Analytics Results 16.5.3 Data Simulation 16.5.4 SWOT Analysis 16.5.5 Customer Satisfaction Monitoring Through Surveys 16.6 Conclusion References 17 Human-Centered and Design-Thinking Approaches for Predictive Analytics 17.1 Introduction 17.2 Data-Driven Decision-Making 17.3 Predictive Analytics Life Cycle 17.3.1 Discover Stage 17.3.2 Design Stage 17.3.3 Develop Stage 17.3.4 Deploy Stage 17.4 Types and Sources of Biases 17.5 Mitigation Strategies for Different Stages of Model Development 17.6 Choice of Algorithms 17.7 Human-Centered Approach to Predictive Analytics 17.7.1 Predictive Analytics 17.7.2 Design Thinking 17.8 Design-Thinking Approach for Predictive Analytics 17.9 Discussion and Limitations 17.10 Conclusion Note References 18 Co-Integration and Causality Between Macroeconomics Variables and Bitcoin 18.1 Introduction 18.2 Review of the Literature 18.3 Data and Research Framework 18.4 Results and Discussion 18.4.1 Descriptive Analysis 18.4.2 Augmented Dickey-Fuller Unit Root Test 18.4.3 Lag Order Selection—Schwarz Information Criterion 18.4.4 Co-Integration Analysis (Johansen) 18.4.5 Vector Error Correction Model 18.4.5 Granger Causality 18.4.6 Residual Diagnostics 18.5 Conclusion References 19 An Examination of Data Protection and Cyber Frauds in the Financial Sector 19.1 Introduction 19.2 Objectives of this Study 19.3 Review of the Literature 19.3.1 Design/Methodology/Approach 19.3.2 Findings 19.4 Cyber Attacks Sector-Wise Victims 19.4.1 General Findings 19.4.2 Distribution 19.5 Impacts of Data Leaks and Cyber Frauds On the Financial Sector 19.6 Governance and Counter Measures 19.7 Conclusion References 20 The ChatGPT: Its Influence On the Jobs Market—An Analytical Study 20.1 Introduction 20.1.1 Objectives of the Study 20.1.2 Research Questions 20.1.3 Scope of the Study 20.2 Literature Review 20.2.1 Architecture 20.2.2 Training 20.2.3 The Working Process of ChatGPT 20.3 Research Methodology 20.4 Findings and Analysis 20.4.1 Performance of Chatbots 20.4.2 Uses of ChatGPT 20.5 Challenges of ChatGPT (Table 20.2) 20.5.1 It Cannot Access the Internet 20.5.2 It May Produce Nonsensical Data 20.5.3 It Has a Limited Knowledge Base 20.5.4 It Lacks Emotional Intelligence 20.5.5 Potential Bias and Dependence On Training Data 20.5.6 Ethical Concerns 20.5.7 It Cannot Solve Complex Mathematical Questions With Accuracy 20.5.8 It Accepts Input in Text Form Only 20.5.9 It Lacks True Understanding of Words 20.6 Influence of ChatGPT On Jobs Market 20.7 Conclusion References 21 Cloud Data Security Using Advanced Encryption Standard With Ant Colony Optimization in Business Sector 21.1 Introduction 21.1.1 Cyber-Attack 21.1.2 Cryptology in Cloud Computing 21.1.3 Ant Colony Optimization Techniques 21.2 Literature Review 21.3 System Design 21.3.1 Advanced Encryption Standard (AES) 21.3.2 Encryption and Decryption Using Ant Colony Optimization 21.3.3 Data Security Using AES and ACO 21.4 Results and Discussion 21.4.1 Confusion Matrix 21.4.2 Accuracy 21.5 Conclusion References 22 Cybersecurity Techniques for Business and Finance Systems 22.1 Introduction 22.2 Fundamentals of Cyber-Attacks 22.2.1 Cyber-Attack: An Overview 22.2.2 Types of Cyber-Attacks 22.2.3 Factors to Overcome Cyber-Attacks 22.3 Network Security 22.3.1 Secure Network Architecture 22.3.2 Virtual Private Networks (VPNs) 22.4 Data Protection and Encryption 22.4.1 Data Protection Overview 22.4.2 Data Encryption Techniques 22.4.3 Secure Data Storage 22.5 Social Engineering and Human Factors 22.5.1 Phishing and Spear Phishing Attacks 22.5.1.1 Phishing Attacks 22.5.1.2 Spear Phishing Attacks 22.5.2 Employee Awareness and Training 22.6 Incident Response and Digital Forensics 22.6.1 Incident Response Planning 22.6.2 Digital Forensics Process 22.7 Emerging Trends and Technologies 22.7.1 Artificial Intelligence and Machine Learning in Cybersecurity 22.7.2 Internet of Things (IoT) Security 22.7.3 Cloud Security Considerations 22.8 Legal and Ethical Aspects of Cybersecurity 22.8.1 Cybersecurity Laws and Regulations 22.8.2 Ethical Hacking and Responsible Disclosure 22.8.2.1 Ethical Hacking 22.8.2.2 Responsible Disclosure 22.8.3 Privacy and Data Protection 22.9 Future Challenges and Recommendations 22.9.1 Cybersecurity Skills Gap 22.9.2 Threat Intelligence and Information Sharing 22.9.3 Threat Intelligence 22.9.4 Information Sharing 22.9.5 Best Practices and Recommendations 22.10 Conclusion References Index