SUBMIT BOOK PROPOSAL
Online Store
benthambooks logo

Site Breadcrumb

Advances in AI for Financial, Cyber, and Healthcare Analytics: A Multidisciplinary Approach

Editors: Ashwani Kumar, Mohit Kumar, Avinash Kumar Sharma, Yojna Arora

Advances in AI for Financial, Cyber, and Healthcare Analytics: A Multidisciplinary Approach

ISBN: 979-8-89881-055-9
eISBN: 979-8-89881-054-2 (Online)

Introduction

Advances in AI for Financial, Cyber, and Healthcare Analytics: A Multidisciplinary Approach comprehensively explores how artificial intelligence and machine learning are reshaping decision-making, predictive modelling, and operational strategies across three critical sectors—finance, cybersecurity, and healthcare.

Across nine chapters, the book delves into the foundations of financial analytics and explores AI’s role in market prediction, fraud detection, and risk analysis. It progresses into healthcare applications such as disease classification using ResNet, ethical implications of AI decisions, and the evolution of human-centred, edge-driven healthcare systems. In the cybersecurity domain, it addresses predictive threat modelling, smart home authentication, and biometric identification through advanced AI techniques.

Key Features:

  • - Unifies financial, healthcare, and cyber analytics through AI-driven solutions
  • - Demonstrates practical implementations with code examples and case studies
  • - Covers cutting-edge technologies like CNN-LSTM, attention models, and edge computing
  • - Addresses ethical, technical, and human-centred dimensions of AI


Readership:

Data scientists, ML engineers, financial analysts, cybersecurity experts, and technology policymakers seeking a hands-on, multidisciplinary understanding of AI’s transformative impact across high-stakes industries.

Foreword

In the past few years, the world of finance has experienced a seismic shift. Traditional financial analysis, relying heavily on historical data, human intuition, and static models, is now being supplemented, if not outright transformed, by the power of data science and machine learning. As financial markets continue to evolve in complexity and speed, the need for more sophisticated tools to analyze, predict, and optimize financial outcomes has never been greater. Against this background, machine learning for financial analytics emerges as a timely and necessary guide for professionals working within this new context. Financial data is best suited to machine learning techniques, as it possesses high volumes, variety, and velocity, characteristics that help identify patterns in large datasets, make predictions, and adapt to new information. From algorithmic trading to fraud detection, risk management to portfolio optimization, machine learning offers innovative solutions to some of the most pressing challenges in finance today. This book provides a comprehensive and approachable guide for those eager to harness the power of machine learning in the financial domain.

It also discusses how the techniques of machine learning could be used to provide new insights, improve forecasting, and optimize decisions in finance. The book easily navigates the challenges by giving the technical depth needed to understand the algorithms and the financial context necessary for their application. This is more than an academic resource; this is a practical guide to the future of finance. As we look forward, data-driven decision-making will continue to grow, and machine learning will be at the heart of this transformation. This book will provide you with the knowledge and tools needed to succeed in this fast-changing field if you are a financial analyst, a data scientist, an investor, or a student with an interest in the intersection of technology and finance. In the chapters that follow, you will embark on a journey that will reshape your understanding of both finance and machine learning.

RELATED BOOKS