Название: Practical Data Analytics for BFSI: Leveraging Data Science for Driving Decisions in Banking, Financial Services, and Insurance OperationsАвтор: Bharat Sikka, Priyender Yadav, Prashant Verma
Издательство: Orange Education Pvt Ltd, AVA
Год: September 2023
Страниц: 334
Язык: английский
Формат: epub (true)
Размер: 10.1 MB
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 explores the area of Data Science and Analytics, including Artificial Intelligence/Machine Learning, that runs behind the BFSI sector to provide its services. This book functionally explains the core concepts of how businesses adapt to Data-Driven solutions to generate profits, reduce risks, and provide better customer support, giving a brief idea to the top management in understanding such a field. On the other hand, this book technically aims to provide solutions to multiple banking pain points using programming languages like Python, R, IBM SPSS, etc.
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.
There are various libraries that perform AutoML, in Python for tasks focused in Machine Learning Auto-SKLearn and TPOT provide easy solution to such a task with a default of 50 to 100 models. While for neural networks Auto-Keras designs the best neural networks for your data, it also comes along with various predefined formats for type of datasets such as CNNs for images and RNNs for language. For R, AutoML, Hyperopt and H2O library provide a simple solution for again almost 50 models automated selection. Besides all the above points, AutoML is also a relevant part of MLOps automation, where the modeling is also automated and no manual intervention is required, allowing for Continuous Integration (CI)/Continuous Development (CD) pipelining. MLOps again provides us with an agile environment to productionize models for real-time or near real-time utilization and from the same comes another terminology of AI/ML utilization also known as Augmented AI.
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.
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
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