Practical Data Analytics for BFSI
Leveraging Data Science for Driving Decisions in Banking, Financial Services, and Insurance Operations
1st Edition
Published on 2. September 2023
334 pages
978-93-88590-85-3 (ISBN)
Description
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.
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
More details
Content
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- About the Authors
- Technical Reviewers
- Acknowledgements
- Preface
- Errata
- Table of Contents
- Section - I: Introduction to BFSI Sector, Analytics, and Data Science
- 1. Introduction to BFSI and Data-Driven Banking
- Structure
- A brief history of BFSI
- Digital Banking
- Products and Services
- Approach to Digital Banking
- Drawbacks of Digital Banking
- Enablers of Digital Banking
- Data-driven Banking
- Conclusion
- Bibliography
- Multiple Choice Questions
- Answers
- 2. Introduction to Analytics and Data Science
- Structure
- Understanding Data and Analytics
- Diving Deep into Data
- Analytics
- Data Science
- Data Storage and Processing
- Data Analysis and Statistics
- Machine Learning and Deep Learning
- Principles of Computation and Programming
- Computation Optimization
- Business Intelligence
- Data Science in Data Driven Banking
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- 3. Major Areas of Analytics Utilization
- Structure
- Objectives
- Understanding Core Areas of Banking Utilization of Data
- Data Governance
- Business Development
- Business Optimization
- Risk and Fraud
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- Section - II: Data Governance and Infrastructures
- 4. Understanding Infrastructure Behind BFSI for Analytics
- Structure
- Understanding Storage Systems in BFSI
- Databases, Data Lakes and Lakehouses
- Data Science Life Cycle
- Administration
- Case Study: The Bank of France - Data Lake
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- 5. Data Governance and AI/ML Model Governance in BFSI
- Structure
- Introduction to Data Governance
- What is Data Governance?
- Data Governance Policy
- Data Governance Policy Structure
- Data Governance Organization
- Data Governance Apex Council (DGAC)
- Data Governance Council (DGC)
- Data Governance Officer (DGO)
- Data Stakeholders (DS)
- Data Architecture (DA)
- Data Architecture Governance
- Data Flow
- Data Categorization
- Data Classification
- Data Integration
- Meta Data Management
- Master data management (MDM)
- Data Retention and Archival
- Data Protection and Leakage prevention
- Data Communication and Disclosure
- AI/ML model governance
- Why is AI/ML model governance required?
- Components of Model Governance
- Model Definition
- Model Management and Activities
- Stages of Model Development
- Assessing the Requirement
- Business Requirement Document
- Exploratory Data Analysis
- Data Extraction, Preparation and Model Building
- Detecting Bias & Bias mitigation
- Model Solution Document
- Model Validation
- Approval
- Deployment
- Adoption
- Post Deployment Process
- Conclusion
- Multiple Choice Questions
- Answers
- 6. Domains of BFSI and Team Planning
- Structure
- Introduction to BFSI and its Domains
- Domains in BFSI
- Team Planning
- Data Science/Analytics Team
- Decentralized Operating Model
- Centralized Operating Model
- Centre of Excellence Operating Model
- Key members of data Analytics Team
- Data Scientists
- Roles and Responsibilities of Data Scientists
- Statistician
- Skills, Roles, and Responsibilities of Statisticians
- Data Engineer
- Business Data Analyst
- Data Steward
- Machine Learning Engineer (MLE)
- Machine Learning Operations Engineer (MLOps Engineer)
- Advance Positions
- Factors to be considered while building your team
- Conclusion
- Multiple Choice Questions
- Answers
- Section - III: Business Developmnent and Lead Generation
- 7. Customer Demographic Analysis and Customer Segmentation
- Structure
- Introduction to Customer Demographics
- Impact of Demographic Variables on Business
- Impact of Age Variable
- Impact of Income Variable
- Impact of Geographic Region Variables
- Impact of Education Level Variable
- Ethics in Demographic Analysis
- Importance of Customer Demographic
- Use of Demographics in Business and Marketing
- Obtaining Demographic Data
- Managing Demographic Data
- CRM software
- Understanding Customer Segmentation
- Why Customer Segmentation?
- Classification of Customer Segmentation
- Demographic Segmentation
- Psychographic Segmentation
- Behavioral Segmentation
- Geographic Segmentation
- Customer Segmentation Analysis
- RFM analysis for customer segmentation
- Cluster analysis for customer segmentation
- Limitations of Customer Segmentation
- Conclusion
- Multiple Choice Questions
- Answers
- 8. Text Mining and Social Media Analytics
- Structure
- Introduction to Text Mining and Text Analytics
- Why Text Analytics?
- Benefits of Text Analytics
- Working of Text Analytics
- Text Analytics Software
- Natural Language Processing (NLP)
- Applications of NLP in BFSI
- Social Media Analytics
- Social Media Analytics in BFSI
- Benefits of Social Media Analytics
- Steps for successful Social Media Analytics
- Social Media Analytics Tools
- Conclusion
- Multiple Choice Questions
- Answers
- 9. Lead Generation Through Analytical Reasoning and Machine Learning
- Structure
- Introduction to Lead Generation
- Lead
- Significance of Lead Generation
- Lead Generation Pipeline
- Analytical Reasoning in Lead Generation
- Machine Learning in Lead Generation
- Machine Learning
- Types of Machine Learning Used in Lead Generation
- Supervised Machine Learning
- Unsupervised Machine Learning
- Roles of Machine Learning in Lead Generation
- Capturing New Leads
- Lead Analysis
- Lead Classification
- Behavior Analysis
- Model Recalibration
- Lead Generation Methods through Machine Learning
- Contact Creation using Data-Warehouse
- Contact Creation using Website Data
- Email Automation
- Browsing history and Chatbots
- The Dominance of Machine Learning
- Hassle-free Acquisition of Lead Information
- Evolve a Hyper-Personalized Customer Experience
- Automation of Lead Generation
- Conclusion
- Multiple Choice Questions
- Answers
- 10. Cross Sell and Up Sell of Products Through Machine Learning
- Structure
- Basics of Cross Sell and Up Sell
- Benefits of Cross Selling and Up Selling
- Developing Machine Learning Model for Cross Selling
- Case Study - Cross Selling Life Insurance through Machine Learning Propensity model
- Problem Statement
- Data Stage
- Modeling Stage
- Implementation Stage
- Recommendation Engine/Next Best Product
- Defining Recommendation Engine
- Types of Recommendation Engine
- Content-based Filtering
- Collaborative-based Filtering
- Hybrid filtering
- Conclusion
- Multiple Choice Questions
- Answers
- Section - IV: Business Optimization
- 11. Pricing Optimization
- Structure
- Basics of Optimizations
- Linear Programming
- Types of Pricing
- Linear Regression and Ordinary Least Squares
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- 12. Data Envelopment Analysis
- Structure
- Introduction to Data Envelopment Analysis (DEA)
- DEA as a measure of efficiency
- Working of the DEA
- Data Envelopment Analysis Models
- CCR Model (Input Oriented)
- CCR Model (Output Oriented)
- BCC Model (Input Oriented)
- BCC Model (Output-Oriented)
- Recent trends in Data Envelopment Analysis
- Steps in Data Envelopment Analysis Modeling
- Advantages and disadvantages of DEA
- Advantages of DEA
- Disadvantages of DEA
- Conclusion
- Multiple Choice Questions
- Answers
- 13. ATM Cash Replenishment
- Structure
- Introduction to ATM Cash Replenishment
- Methodology of ATM Cash Replenishment Process
- Data Analysis and Prediction of Cash Demand
- Time Series Forecasting
- Multiple Linear Regression Model
- Model Adaptation
- Replenishment Plan and Optimization
- Conclusion
- Multiple Choice Questions
- Answers
- 14. Unstructured Data Analytics
- Structure
- Unstructured Data, Tensors and Neural Networks
- Tensors
- Neural Networks
- Raw Text and Audio
- Understanding Audio
- Images and Videos
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- Section - V: Risk, Fraud, and Compliance
- 15. Fraud Modelling
- Structure
- Fraud
- Behavioral and Root Cause Analysis
- Transactional Fraud Modelling and Real Time Analytics
- Supervised
- Unsupervised
- Industry Solutions and Real-Time Analysis
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- 16. Detection of Money Laundering and Analysis
- Structure
- Money Laundering in BFSI
- Analytics behind AML
- Percentiles and Quartiles
- Network/Graph Analysis
- Modeling in AML
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- 17. Credit Risk and Stressed Assets
- Structure
- Introduction to Credit Risk and Stressed Assets
- Credit Risk Modeling
- Data preparation
- Model development
- Performance window
- Roll rate analysis
- Waterfall analysis and exclusions
- Imbalanced data
- Holdout sample
- Segmentation for building scorecards
- Coarse classification
- Feature selection
- Model validation
- Credit risk discrimination
- Accuracy calibration
- Stability measures and swap set analysis
- Early Warning System for Stressed Assets
- Models for regulatory compliance
- International Financial Reporting Standard 9 (IFRS9)
- BASEL
- Stress testing
- Conclusion
- Multiple Choice Questions
- Answers
- Section - VI: Future Scope and Developments
- 18. High Performance Architectures: On-Premises and Cloud
- Structure
- High-performance computing
- Cloud Infrastructures
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- 19. Growing Trends in the Data-Driven Future of BFSI
- Structure
- AutoML
- Augmented AI
- Looking forward to BFSI
- Fintech evolution in India
- Open banking and Banking-as-a-Service (BaaS)
- Conclusion
- Multiple Choice Questions
- Answers
- Bibliography
- Index
