Mastering Big Data in Finance: Analytics and Risk Assessment. Digital Life, #1

What's driving the real decisions in finance today? Hint: it's not gut feeling or outdated dashboards. It's data-lots of it. But raw data isn't value. You need the right models, tools, and systems to turn it into strategy. This book is a hands-on, no-nonsense guide to how big data and AI are transforming finance-from fraud detection and portfolio management to predictive modeling, compliance, and intelligent automation.
Whether you're a practitioner, analyst, or executive, this book will help you understand not just the technology, but the logic behind it. ? What You'll Learn What big data really means (and doesn't) in finance The 5 V's-Volume, Velocity, Variety, Veracity, Value-with real financial use cases Common challenges: data quality, ethical traps, platform confusion Types of analytics: descriptive, predictive, prescriptive, cognitive How Spark, Hadoop, and modern platforms power distributed processing Smart adoption of cloud and multi-cloud strategies (AWS, Azure, GCP) Case studies from fraud detection to behavioral scoring ML + big data: algorithms, preprocessing, drift, and debugging Big data applications in portfolio theory and forecasting Real-time decision systems and intelligent agents Compliance, GDPR, explainability, and governance essentials  ?? Who It's For Finance professionals who need to understand big data without getting lost in code Data scientists working in banking, insurance, or fintech Managers and decision-makers who want to avoid buzzwords and get to what works Students or Udemy course attendees who want more depth, examples, and practical structure  ?? Bonus: Appendices Include? Glossary of key terms (plain English) Tool & platform recommendations Open-source vs enterprise decision guide Setup tips for local Spark, Kafka, and ML experimentation Further reading, datasets, and resource links  Built for clarity.
Focused on real-world application. Designed to stay relevant. ?? Start making decisions with your data-not despite it.