Data-Driven Modelling and Predictive Analytics in Business and Finance: Concepts, Designs, Technologies, and Applications

✍ Scribed by Alex Khang, Rashmi Gujrati, Hayri Uygun, R. K. Tailor, Sanjaya Singh Gaur

• Publisher: CRC Press
• Year: 202
• Tongue: English
• Leaves: 443
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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