3 Review(s)

This book comprehensively analyses how technology is revolutionising the banking sector, covering key themes like data analytics, big data, blockchain, machine learning, and artificial intelligence. The book examines how these advancements transform traditional banking operations into digital-first processes, enhance customer experience, streamline operations, and improve risk management. It emphasises the significance of data in decision-making and explores big data tools like Hadoop for managing vast datasets. The book also discusses blockchain's role in fostering transparency and security, while machine learning and AI are analysed for their impact on predictive analysis, personalised services, and fraud detection. It is designed as a self-study guide and uses a modular approach to facilitate independent learning, offering practical examples, case studies, and assessment resources for finance professionals, banking practitioners, and students to adapt to the evolving landscape of digital finance. The Present Publication is the 2024 Edition, authored by Mr Burra Butchi Babu | Former General Manager – Bank of India and vetted by Mr V.A. Prasanth | Former General Manager & Chief Information Officer – Indian Bank. Taxmann exclusively publishes this book for the Indian Institute of Banking and Finance for the certificate course on 'Emerging Technologies' with the following noteworthy features: (i) [Transforming the Banking Paradigm] The book explains how technology drives the banking sector's transformation, transcending geographical limitations and enhancing operational efficiency. By adopting innovations like digital currencies, big data analytics, machine learning, and blockchain, banks are improving customer experience, increasing transparency, and reducing risks. The book explores these changes in great detail, explaining how technological synergies are paving the way for more innovative, faster, and safer banking operations (ii) [Practical Insights into Technological Adoption] Through real-world applications and expert insights, this book offers a practical perspective on how these emerging technologies are integrated into the banking ecosystem. It discusses how the fusion of finance and technology has fostered new opportunities for growth while addressing the challenges of data security, privacy, and ethical responsibility. Readers are guided to think critically about how these advancements balance the convenience of seamless transactions with the imperative to protect financial identities and safeguard sensitive data (iii) [Comprehensive Coverage through a Modular Approach] To ensure a thorough understanding, the book is structured modularly, covering specific technological areas and their applications in banking. Each module breaks down complex concepts into digestible sections, providing readers with a coherent learning pathway. From the fundamentals of data analytics to the nuanced intricacies of artificial intelligence, the book offers in-depth discussions designed to equip learners with the practical skills necessary for thriving in a technology-driven financial environment (iv) [Self-Learning Resources & Assessment Tools] The book is enriched with self-assessment resources such as multiple-choice questions, terminal questions, and comprehensive summaries, allowing readers to test their knowledge and reinforce their understanding. Every module is carefully structured with learning objectives, chapter overviews, keywords, and references, making it a holistic educational tool. The inclusion of practical examples, case studies, and exercises enhances its relevance for both academic and professional learning environments The book adopts a modular approach, ensuring a coherent and logical flow of content across its four modules, which are as follows: (i) Module A – Data Analytics (a) This module is a foundational entry point into data analytics, a key driver of decision-making in modern banking. It introduces emerging technological trends within banks, discussing data extraction, analysis, and visualisation. The module explains the various types of analytics (descriptive, diagnostic, predictive, and prescriptive) and how they extract actionable insights for better financial decision-making. Moreover, the section on immersive technologies such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) highlights their role in enhancing customer interactions and operational processes (li) Module B – Big Data & Hadoop (a) Big data is a critical component of modern banking and is explored extensively in this module. It discusses how vast datasets are collected, processed, and analysed, enabling banks to make informed decisions, understand customer behaviour, and detect market trends. The module covers topics like Hadoop's ecosystem and architecture, NoSQL databases, and real-life examples showcasing the role of big data in optimising banking processes. It dives into methods for handling large-scale data efficiently and how these insights lead to personalised customer services, risk assessment, and better regulatory compliance (lii) Module C – Blockchain & Digital Currencies (a) Blockchain is redefining trust and transparency in financial transactions. This module provides an elaborate overview of blockchain, including its types, key features, consensus mechanisms, and transaction flow. The section discusses the rise of digital currencies like Bitcoin and their influence on global finance, highlighting the decentralised nature of blockchain technology and its role in securing financial transactions. It also examines how smart contracts, interoperability, and distributed ledger technology are being implemented in banking to reduce fraud, automate processes, and facilitate seamless cross-border payments (liii) Module D – Machine Learning (ML) in Banking (a) Machine learning, a cornerstone of artificial intelligence, transforms how banks predict trends, personalise services, and detect fraud. This module introduces the concepts and types of machine learning, covering supervised, unsupervised, and reinforcement learning. Readers are guided through different stages of machine learning, the categorisation of algorithms, and practical examples of how banks use ML for predictive analysis, customer segmentation, credit scoring, and more. It also explores the future trajectory of ML in financial services and its potential to reshape the industry (liv) Module E – Artificial Intelligence (AI) (a) Artificial Intelligence (AI) has become integral to modernising financial services. This module covers the basics of AI, discussing neural networks, deep learning, natural language processing, and text classification. It examines the architectural framework of neural networks, the role of deep belief networks (DBNs), and generative adversarial networks (GANs) in financial modelling. The book explains the real-world applications of AI, such as chatbots, virtual assistants, fraud detection, automated underwriting, and risk assessment, demonstrating how AI is improving efficiency and customer service in the banking sector. (lv) Emerging Technologies –IoT & Robotic Process Automation (RPA) (a) Supplementary chapters discuss the Internet of Things (IoT) and Robotic Process Automation (RPA) and their impact on the financial world. By enabling interconnected banking solutions, readers will learn how IoT devices enhance customer experiences. Meanwhile, RPA's role in automating repetitive tasks, reducing manual errors, and increasing operational efficiency is explored, alongside the ethical and practical implications of hyper-automation in banking