Explainable AI in Finance Demystifying Black Box Models for Traders & Analysts

Modern finance runs on algorithms, yet few understand why those algorithms make the decisions they do.
In Explainable AI in Finance, bestselling author James Preston reveals how to bridge the gap between performance and transparency in the age of machine learning.
Built for quants, traders, and financial engineers, this book unpacks the hidden logic inside black box models, showing how to design, audit, and deploy AI systems that institutions can truly trust. You'll learn how to make cutting-edge models interpretable, compliant, and profitable across real-world financial applications.
Inside You'll DiscoverThe Core Framework of Explainability: SHAP, LIME, counterfactuals, and interpretable surrogate modelsDeconstructing AI Black Boxes: Practical methods for visualizing model decisions and feature importanceModel Validation in Practice: How risk teams audit ML systems for transparency and fairnessAI for Risk and Alpha: Case studies in portfolio construction, volatility forecasting, and trading signal attributionGovernance and Regulation: How to align with Basel III, SEC, and the EU AI Act - without limiting innovationPython Implementations: Full code walkthroughs for integrating XAI techniques into live trading and risk platformsWho This Book Is ForQuantitative Analysts & Data ScientistsPortfolio & Risk ManagersAlgorithmic Traders & Fintech DevelopersInstitutional Investors & Compliance LeadersWhy It Matters

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