Practical Data Analytics for BFSI : Leveraging Data Science for Driving Decisions in Banking, Financial Services, and Insurance Operations.

Intro
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.