Anomaly Identification in Finance : ML Fraud Detection Projects for Risk Mitigation with 30 Practical Exercises

Safeguard the financial world from devastating fraud and anomalies with Anomaly Identification in Finance: ML Fraud Detection Projects for Risk Mitigation with 30 Practical Exercises by expert Riches Wren. In an era where cyber threats and sophisticated scams cost the global economy trillions annually, this hands-on guide empowers you to harness machine learning's power to detect, prevent, and mitigate risks before they escalate into crises.
Struggling with imbalanced datasets, evolving fraud tactics, or regulatory compliance in your financial projects? This book demystifies anomaly detection, bridging theory and practice to build robust, scalable ML systems tailored for finance. Drawing on real-world insights, author Riches Wren walks you through the foundations of financial anomalies, machine learning fundamentals, and data preparation, before diving into core techniques like supervised models (logistic regression, random forests, XGBoost), unsupervised approaches (isolation forests, autoencoders, clustering), deep learning (RNNs, LSTMs, GANs), and time-series analysis (ARIMA, Prophet, graph neural networks).
What makes this book indispensable is its project-based learning: 30 meticulously crafted practical exercises that simulate professional scenarios, from exploratory data analysis with Pandas and generating synthetic fraud data, to training LSTM models, building scikit-learn pipelines, and deploying with FastAPI and MLflow. Tackle capstone projects like a credit card fraud dashboard with Streamlit, money laundering detection using graph algorithms, and insider trading anomaly spotting with NLP—each with step-by-step guidance, code snippets, review questions, and extension challenges to deepen your expertise.
Explore compelling case studies, including the 2010 Flash Crash, credit card fraud, unusual trading patterns, cryptocurrency monitoring, and crypto pump-and-dump schemes, to see ML in action. Learn ethical considerations, explainable AI with SHAP and LIME, federated learning for privacy, handling adversarial attacks, and future trends like quantum computing and blockchain integration. Appendices provide Python setup guides, dataset sources, a glossary, and solutions to selected exercises, plus access to a companion GitHub repository for full code, datasets, and resources.
Ideal for data scientists, financial analysts, ML engineers, risk managers, and fintech professionals with basic Python and ML knowledge (variables, functions, scikit-learn basics). Whether you're fortifying banking systems, optimizing e-commerce security, or ensuring market integrity, this book delivers actionable strategies to slash fraud losses and boost confidence.