Machine Learning for Finance
Packt Publishing Limited (Verlag)
978-1-80602-991-4 (ISBN)
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Key Features
Practical machine learning techniques for financial data and time-series forecasting
Apply GenAI for synthetic data generation, financial NLP, and reporting automation
Deploy financial ML pipelines with Docker, FastAPI, Prometheus, and Grafana
Explore advanced topics like Reinforcement Learning, Agentic AI, and financial MLOps
Book DescriptionThe integration of machine learning into finance is driving a fundamental shift—optimizing risk, forecasting markets, and enabling smarter compliance. Machine Learning for Finance is a hands-on, end-to-end guide designed to help readers bridge the gap between AI research and real-world financial deployment. Written by two leading AI engineers at Mastercard, this book combines deep technical insight with practical finance-specific workflows.
Starting with the foundations of ML, GenAI, and financial data, the book builds toward sophisticated applications such as agentic AI, reinforcement learning, and MLOps for finance. You'll learn to generate synthetic data using GANs, extract insights from SEC filings using transformers, and deploy real-time trading bots with monitoring pipelines. Each chapter includes projects modeled after real use cases—fraud detection, risk modeling, portfolio optimization, and more.
Machine Learning for Finance is tailored for practitioners. It goes beyond code snippets and theory, offering full-stack implementations that scale in production environments. Whether you're automating regulatory filings or building robo-advisors, this book gives you the tools to deliver AI innovation in one of the most complex, regulated industries. What you will learn
Build financial ML systems using Python, NumPy, Pandas, and Scikit-learn
Predict stock prices, interest rates, and credit risk using ML and deep learning
Automate earnings call analysis and financial filings using LLMs
Detect anomalies and fraud using unsupervised learning and zero-shot techniques
Train RL agents for dynamic portfolio optimization
Deploy real-time financial ML apps with FastAPI, Docker, and observability tools
Understand AI governance and compliance frameworks (EU AI Act, SEC guidance)
Who this book is forFinancial Analysts and Quants: Applying Machine learning (ML) for trading, risk management, and forecasting.
Data Scientists and ML Engineers: Focused on financial applications and real-world deployments.
Portfolio Managers and Investment Strategists: Using Artificial Intelligence (AI) for market analysis and algorithmic trading.
Finance Students and Academics: Seeking practical applications of AI/ML in finance.
Readers should have:
Basic Python programming skills, statistics and probability.
Familiarity with financial market dynamics and instruments.
Ahmed Menshawy is the Vice President of AI Engineering at Mastercard. He leads the AI Engineering team to drive the development and operationalization of AI products, address a broad range of challenges and technical debts for ML pipelines deployment. He also leads a team dedicated to creating several AI accelerators and capabilities, including serving engines and feature stores, aimed at enhancing various aspects of AI engineering. Mahmoud Fahmy is a Lead Machine Learning Engineer at Mastercard, specializing in the development and operationalization of AI products. His primary focus is on optimizing machine learning pipelines and navigating the intricate challenges of deploying models effectively for end customers. Ajaz Ahmed Siddiqui is part of Microsoft's AI business solutions group, where he helps CFOs and finance teams bring AI agents into their core financial processes. Before Microsoft, he worked at Oracle, helping FP&A leaders implement budgeting, forecasting, and consolidation. Over the past decade, he has worked alongside finance leaders through successive waves of change, from early ML use cases in forecasting and anomaly detection to today's agentic AI handling end-to-end financial workflows.
Table of Contents
Introduction to Machine Learning, GenAI and Finance
Essential Mathematical and Statistical Concepts
Introduction to Python for Financial Data Science
Supervised Learning for Financial Applications
Unsupervised Learning in Finance
Reinforcement Learning for Financial Decision-Making
Enhancing Customer Insight with Sentiment Analysis
Deep Learning for Financial Time Series
Generative AI in Finance: Foundations and Applications
Implementation and Challenges of Generative AI in Finance
Foundations of Agentic AI in Finance
Applications and Future Trends of Agentic AI in Finance
Building and Deploying Machine Learning Models
| Erscheinungsdatum | 04.12.2025 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| Sozialwissenschaften ► Pädagogik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| ISBN-10 | 1-80602-991-X / 180602991X |
| ISBN-13 | 978-1-80602-991-4 / 9781806029914 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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