Название: Building Business Models with Machine LearningАвтор: Ambika N, Vishal Jain, Cristian González García, Dac-Nhuong Le
Издательство: IGI Global
Год: 2025
Страниц: 308
Язык: английский
Формат: pdf (true), epub
Размер: 14.9 MB
Organizations worldwide grapple with the complexities of incorporating Machine Learning (ML) into their business models while ensuring sustainability. Decision-makers, data scientists, and business executives face the challenge of navigating this terrain to drive innovation and maintain a competitive edge. Building Business Models with Machine Learning provides a comprehensive solution, offering practical insights and strategies for integrating Machine Learning into organizational plans. By bridging the gap between theory and practice, we empower readers to leverage Machine Learning effectively, enabling them to develop resilient and flexible business models. The book serves as a vital resource for those seeking to understand the nuances of sustainable management in a volatile, uncertain, complex, and ambiguous (VUCA) world. It addresses key challenges such as irrational decision-making and the need for adaptive systems in modern business environments. Through a combination of theoretical frameworks and empirical research findings, our book equips readers with the knowledge and tools needed to navigate these challenges successfully.
Chapter 1: Financial fraud remains a significant concern across various industries, particularly in sectors reliant on financial transactions. This chapter delves into the application of Machine Learning (ML) for improving fraud detection capabilities within the financial sector. A thorough review of existing literature is presented, examining various ML algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Convolutional Neural Networks (CNN), and their effectiveness in detecting fraudulent activities. The chapter also explores the challenges faced by current methodologies and offers insights into potential areas for future research to enhance fraud detection systems.
Chapter 2: With the rise of social media platforms like Twitter, the proliferation of hostile content, including hate speech and cyberbullying, has become a pressing issue. This chapter focuses on identifying and combating abusive language on social media through Machine Learning techniques. It presents a range of strategies, equations, and methods for evaluating Twitter tweets to detect abusive language. The chapter highlights various algorithms designed to filter out harmful content, offering practical solutions for managing online toxicity and promoting healthier digital communication environments.
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Chapter 13: Recommendation systems are transforming various industries, and the financial sector is no exception. This chapter discusses how Machine Learning-powered recommendation systems can enhance the customer experience by offering personalized financial products and services. These systems analyze customer data, including spending habits and investment preferences, to recommend relevant products. The chapter explores the challenges of building effective recommender systems for the financial sector and highlights their potential to provide better, data-driven recommendations to customers.
Whether you are a seasoned professional, a postgraduate MBA program, or a managerial sciences student, this book offers invaluable insights that will significantly enhance your understanding and application of Machine Learning in business models.
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